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# Source code for astropy.modeling.core

    # Licensed under a 3-clause BSD style license - see LICENSE.rst

    """
    This module defines base classes for all models.  The base class of all
    models is `~astropy.modeling.Model`. `~astropy.modeling.FittableModel` is
    the base class for all fittable models. Fittable models can be linear or
    nonlinear in a regression analysis sense.

    All models provide a `__call__` method which performs the transformation in
    a purely mathematical way, i.e. the models are unitless.  Model instances can
    represent either a single model, or a "model set" representing multiple copies
    of the same type of model, but with potentially different values of the
    parameters in each model making up the set.
    """

    # pylint: disable=invalid-name, protected-access, redefined-outer-name
    import abc
    import copy
    import functools
    import inspect
    import operator
    from collections import defaultdict, deque
    from inspect import signature
    from textwrap import indent

    import numpy as np

    from astropy.nddata.utils import add_array, extract_array
    from astropy.table import Table
    from astropy.units import Quantity, UnitsError, dimensionless_unscaled
    from astropy.utils import find_current_module, metadata, sharedmethod
    from astropy.utils.codegen import make_function_with_signature
    from astropy.utils.compat import COPY_IF_NEEDED

    from .bounding_box import CompoundBoundingBox, ModelBoundingBox
    from .parameters import InputParameterError, Parameter, _tofloat, param_repr_oneline
    from .utils import (
        _combine_equivalency_dict,
        _ConstraintsDict,
        _SpecialOperatorsDict,
        combine_labels,
        get_inputs_and_params,
        make_binary_operator_eval,
        quantity_asanyarray,
    )

    __all__ = [
        "CompoundModel",
        "Fittable1DModel",
        "Fittable2DModel",
        "FittableModel",
        "Model",
        "ModelDefinitionError",
        "bind_bounding_box",
        "bind_compound_bounding_box",
        "custom_model",
        "fix_inputs",
    ]

    def _model_oper(oper, **kwargs):
        """
        Returns a function that evaluates a given Python arithmetic operator
        between two models.  The operator should be given as a string, like ``'+'``
        or ``'**'``.
        """
        return lambda left, right: CompoundModel(oper, left, right, **kwargs)

    class ModelDefinitionError(TypeError):
        """Used for incorrect models definitions."""

    class _ModelMeta(abc.ABCMeta):
        """
        Metaclass for Model.

        Currently just handles auto-generating the param_names list based on
        Parameter descriptors declared at the class-level of Model subclasses.
        """

        _is_dynamic = False
        """
        This flag signifies whether this class was created in the "normal" way,
        with a class statement in the body of a module, as opposed to a call to
        `type` or some other metaclass constructor, such that the resulting class
        does not belong to a specific module.  This is important for pickling of
        dynamic classes.

        This flag is always forced to False for new classes, so code that creates
        dynamic classes should manually set it to True on those classes when
        creating them.
        """

        # Default empty dict for _parameters_, which will be empty on model
        # classes that don't have any Parameters

        def __new__(cls, name, bases, members, **kwds):
            # See the docstring for _is_dynamic above
            if "_is_dynamic" not in members:
                members["_is_dynamic"] = cls._is_dynamic
            opermethods = [
                ("__add__", _model_oper("+")),
                ("__sub__", _model_oper("-")),
                ("__mul__", _model_oper("*")),
                ("__truediv__", _model_oper("/")),
                ("__pow__", _model_oper("**")),
                ("__or__", _model_oper("|")),
                ("__and__", _model_oper("&")),
                ("_fix_inputs", _model_oper("fix_inputs")),
            ]

            members["_parameters_"] = {
                k: v for k, v in members.items() if isinstance(v, Parameter)
            }

            for opermethod, opercall in opermethods:
                members[opermethod] = opercall
            self = super().__new__(cls, name, bases, members, **kwds)

            param_names = list(members["_parameters_"])

            # Need to walk each base MRO to collect all parameter names
            for base in bases:
                for tbase in base.__mro__:
                    if issubclass(tbase, Model):
                        # Preserve order of definitions
                        param_names = list(tbase._parameters_) + param_names
            # Remove duplicates (arising from redefinition in subclass).
            param_names = list(dict.fromkeys(param_names))
            if self._parameters_:
                if hasattr(self, "_param_names"):
                    # Slight kludge to support compound models, where
                    # param_names is a property; could be improved with a
                    # little refactoring but fine for now
                    self._param_names = tuple(param_names)
                else:
                    self.param_names = tuple(param_names)

            return self

        def __init__(cls, name, bases, members, **kwds):
            super().__init__(name, bases, members, **kwds)
            cls._create_inverse_property(members)
            cls._create_bounding_box_property(members)
            pdict = {}
            for base in bases:
                for tbase in base.__mro__:
                    if not issubclass(tbase, Model):
                        continue
                    pdict |= cls._parameters_
            cls._handle_special_methods(members, pdict)

        def __repr__(cls):
            """
            Custom repr for Model subclasses.
            """
            return cls._format_cls_repr()

        def _repr_pretty_(cls, p, cycle):
            """
            Repr for IPython's pretty printer.

            By default IPython "pretty prints" classes, so we need to implement
            this so that IPython displays the custom repr for Models.
            """
            p.text(repr(cls))

        def __reduce__(cls):
            if not cls._is_dynamic:
                # Just return a string specifying where the class can be imported
                # from
                return cls.__name__
            members = dict(cls.__dict__)
            # Delete any ABC-related attributes--these will be restored when
            # the class is reconstructed:
            for key in list(members):
                if key.startswith("_abc_"):
                    del members[key]

            # Delete custom __init__ and __call__ if they exist:
            for key in ("__init__", "__call__"):
                members.pop(key, None)

            return (type(cls), (cls.__name__, cls.__bases__, members))

        @property
        def name(cls):
            """
            The name of this model class--equivalent to ``cls.__name__``.

            This attribute is provided for symmetry with the
            `~astropy.modeling.Model.name` attribute of model instances.
            """
            return cls.__name__

        @property
        def _is_concrete(cls):
            """
            A class-level property that determines whether the class is a concrete
            implementation of a Model--i.e. it is not some abstract base class or
            internal implementation detail (i.e. begins with '_').
            """
            return not (cls.__name__.startswith("_") or inspect.isabstract(cls))

        def rename(cls, name=None, inputs=None, outputs=None):
            """
            Creates a copy of this model class with a new name, inputs or outputs.

            The new class is technically a subclass of the original class, so that
            instance and type checks will still work.  For example::

                >>> from astropy.modeling.models import Rotation2D
                >>> SkyRotation = Rotation2D.rename('SkyRotation')
                >>> SkyRotation
                <class 'astropy.modeling.core.SkyRotation'>
                Name: SkyRotation (Rotation2D)
                N_inputs: 2
                N_outputs: 2
                Fittable parameters: ('angle',)
                >>> issubclass(SkyRotation, Rotation2D)
                True
                >>> r = SkyRotation(90)
                >>> isinstance(r, Rotation2D)
                True
            """
            mod = find_current_module(2)
            if mod:
                modname = mod.__name__
            else:
                modname = "__main__"

            if name is None:
                name = cls.name
            if inputs is None:
                inputs = cls.inputs
            else:
                if not isinstance(inputs, tuple):
                    raise TypeError("Expected 'inputs' to be a tuple of strings.")
                elif len(inputs) != len(cls.inputs):
                    raise ValueError(f"{cls.name} expects {len(cls.inputs)} inputs")
            if outputs is None:
                outputs = cls.outputs
            else:
                if not isinstance(outputs, tuple):
                    raise TypeError("Expected 'outputs' to be a tuple of strings.")
                elif len(outputs) != len(cls.outputs):
                    raise ValueError(f"{cls.name} expects {len(cls.outputs)} outputs")
            new_cls = type(name, (cls,), {"inputs": inputs, "outputs": outputs})
            new_cls.__module__ = modname
            new_cls.__qualname__ = name

            return new_cls

        def _create_inverse_property(cls, members):
            inverse = members.get("inverse")
            if inverse is None or cls.__bases__[0] is object:
                # The latter clause is the prevent the below code from running on
                # the Model base class, which implements the default getter and
                # setter for .inverse
                return

            if isinstance(inverse, property):
                # We allow the @property decorator to be omitted entirely from
                # the class definition, though its use should be encouraged for
                # clarity
                inverse = inverse.fget

            # Store the inverse getter internally, then delete the given .inverse
            # attribute so that cls.inverse resolves to Model.inverse instead
            cls._inverse = inverse
            del cls.inverse

        def _create_bounding_box_property(cls, members):
            """
            Takes any bounding_box defined on a concrete Model subclass (either
            as a fixed tuple or a property or method) and wraps it in the generic
            getter/setter interface for the bounding_box attribute.
            """
            # TODO: Much of this is verbatim from _create_inverse_property--I feel
            # like there could be a way to generify properties that work this way,
            # but for the time being that would probably only confuse things more.
            bounding_box = members.get("bounding_box")
            if bounding_box is None or cls.__bases__[0] is object:
                return

            if isinstance(bounding_box, property):
                bounding_box = bounding_box.fget

            if not callable(bounding_box):
                # See if it's a hard-coded bounding_box (as a sequence) and
                # normalize it
                try:
                    bounding_box = ModelBoundingBox.validate(
                        cls, bounding_box, _preserve_ignore=True
                    )
                except ValueError as exc:
                    raise ModelDefinitionError(exc.args[0])
            else:
                sig = signature(bounding_box)
                # May be a method that only takes 'self' as an argument (like a
                # property, but the @property decorator was forgotten)
                #
                # However, if the method takes additional arguments then this is a
                # parameterized bounding box and should be callable
                if len(sig.parameters) > 1:
                    bounding_box = cls._create_bounding_box_subclass(bounding_box, sig)

            # See the Model.bounding_box getter definition for how this attribute
            # is used
            cls._bounding_box = bounding_box
            del cls.bounding_box

        def _create_bounding_box_subclass(cls, func, sig):
            """
            For Models that take optional arguments for defining their bounding
            box, we create a subclass of ModelBoundingBox with a ``__call__`` method
            that supports those additional arguments.

            Takes the function's Signature as an argument since that is already
            computed in _create_bounding_box_property, so no need to duplicate that
            effort.
            """
            # TODO: Might be convenient if calling the bounding box also
            # automatically sets the _user_bounding_box.  So that
            #
            #    >>> model.bounding_box(arg=1)
            #
            # in addition to returning the computed bbox, also sets it, so that
            # it's a shortcut for
            #
            #    >>> model.bounding_box = model.bounding_box(arg=1)
            #
            # Not sure if that would be non-obvious / confusing though...

            def __call__(self, **kwargs):
                return func(self._model, **kwargs)

            kwargs = []
            for idx, param in enumerate(sig.parameters.values()):
                if idx == 0:
                    # Presumed to be a 'self' argument
                    continue

                if param.default is param.empty:
                    raise ModelDefinitionError(
                        f"The bounding_box method for {cls.name} is not correctly "
                        "defined: If defined as a method all arguments to that "
                        "method (besides self) must be keyword arguments with "
                        "default values that can be used to compute a default "
                        "bounding box."
                    )

                kwargs.append((param.name, param.default))

            __call__.__signature__ = sig

            return type(
                f"{cls.name}ModelBoundingBox", (ModelBoundingBox,), {"__call__": __call__}
            )

        def _handle_special_methods(cls, members, pdict):
            # Handle init creation from inputs
            def update_wrapper(wrapper, cls):
                # Set up the new __call__'s metadata attributes as though it were
                # manually defined in the class definition
                # A bit like functools.update_wrapper but uses the class instead of
                # the wrapped function
                wrapper.__module__ = cls.__module__
                wrapper.__doc__ = getattr(cls, wrapper.__name__).__doc__
                if hasattr(cls, "__qualname__"):
                    wrapper.__qualname__ = f"{cls.__qualname__}.{wrapper.__name__}"

            if (
                "__call__" not in members
                and "n_inputs" in members
                and isinstance(members["n_inputs"], int)
                and members["n_inputs"] > 0
            ):
                # Don't create a custom __call__ for classes that already have one
                # explicitly defined (this includes the Model base class, and any
                # other classes that manually override __call__

                def __call__(self, *inputs, **kwargs):
                    """Evaluate this model on the supplied inputs."""
                    return super(cls, self).__call__(*inputs, **kwargs)

                # When called, models can take two optional keyword arguments:
                #
                # * model_set_axis, which indicates (for multi-dimensional input)
                #   which axis is used to indicate different models
                #
                # * equivalencies, a dictionary of equivalencies to be applied to
                #   the input values, where each key should correspond to one of
                #   the inputs.
                #
                # The following code creates the __call__ function with these
                # two keyword arguments.

                args = ("self",)
                kwargs = {
                    "model_set_axis": None,
                    "with_bounding_box": False,
                    "fill_value": np.nan,
                    "equivalencies": None,
                    "inputs_map": None,
                }

                new_call = make_function_with_signature(
                    __call__, args, kwargs, varargs="inputs", varkwargs="new_inputs"
                )

                # The following makes it look like __call__
                # was defined in the class
                update_wrapper(new_call, cls)

                cls.__call__ = new_call

            if (
                "__init__" not in members
                and not inspect.isabstract(cls)
                and cls._parameters_
            ):
                # Build list of all parameters including inherited ones

                # If *all* the parameters have default values we can make them
                # keyword arguments; otherwise they must all be positional
                # arguments
                if all(p.default is not None for p in pdict.values()):
                    args = ("self",)
                    kwargs = []
                    for param_name, param_val in pdict.items():
                        default = param_val.default
                        unit = param_val.unit
                        # If the unit was specified in the parameter but the
                        # default is not a Quantity, attach the unit to the
                        # default.
                        if unit is not None:
                            default = Quantity(
                                default, unit, copy=COPY_IF_NEEDED, subok=True
                            )
                        kwargs.append((param_name, default))
                else:
                    args = ("self",) + tuple(pdict.keys())
                    kwargs = {}

                def __init__(self, *params, **kwargs):
                    return super(cls, self).__init__(*params, **kwargs)

                new_init = make_function_with_signature(
                    __init__, args, kwargs, varkwargs="kwargs"
                )
                update_wrapper(new_init, cls)
                cls.__init__ = new_init

        # *** Arithmetic operators for creating compound models ***
        __add__ = _model_oper("+")
        __sub__ = _model_oper("-")
        __mul__ = _model_oper("*")
        __truediv__ = _model_oper("/")
        __pow__ = _model_oper("**")
        __or__ = _model_oper("|")
        __and__ = _model_oper("&")
        _fix_inputs = _model_oper("fix_inputs")

        # *** Other utilities ***

        def _format_cls_repr(cls, keywords=[]):
            """
            Internal implementation of ``__repr__``.

            This is separated out for ease of use by subclasses that wish to
            override the default ``__repr__`` while keeping the same basic
            formatting.
            """
            # For the sake of familiarity start the output with the standard class
            # __repr__
            parts = [super().__repr__()]

            if not cls._is_concrete:
                return parts[0]

            def format_inheritance(cls):
                bases = []
                for base in cls.mro()[1:]:
                    if not issubclass(base, Model):
                        continue
                    elif inspect.isabstract(base) or base.__name__.startswith("_"):
                        break
                    bases.append(base.name)
                if bases:
                    return f"{cls.name} ({' -> '.join(bases)})"
                return cls.name

            try:
                default_keywords = [
                    ("Name", format_inheritance(cls)),
                    ("N_inputs", cls.n_inputs),
                    ("N_outputs", cls.n_outputs),
                ]

                if cls.param_names:
                    default_keywords.append(("Fittable parameters", cls.param_names))

                for keyword, value in default_keywords + keywords:
                    if value is not None:
                        parts.append(f"{keyword}: {value}")

                return "\n".join(parts)
            except Exception:
                # If any of the above formatting fails fall back on the basic repr
                # (this is particularly useful in debugging)
                return parts[0]

    class Model(metaclass=_ModelMeta):
        """
        Base class for all models.

        This is an abstract class and should not be instantiated directly.

        The following initialization arguments apply to the majority of Model
        subclasses by default (exceptions include specialized utility models
        like `~astropy.modeling.mappings.Mapping`).  Parametric models take all
        their parameters as arguments, followed by any of the following optional
        keyword arguments:

        Parameters
        ----------
        name : str, optional
            A human-friendly name associated with this model instance
            (particularly useful for identifying the individual components of a
            compound model).

        meta : dict, optional
            An optional dict of user-defined metadata to attach to this model.
            How this is used and interpreted is up to the user or individual use
            case.

        n_models : int, optional
            If given an integer greater than 1, a *model set* is instantiated
            instead of a single model.  This affects how the parameter arguments
            are interpreted.  In this case each parameter must be given as a list
            or array--elements of this array are taken along the first axis (or
            ``model_set_axis`` if specified), such that the Nth element is the
            value of that parameter for the Nth model in the set.

            See the section on model sets in the documentation for more details.

        model_set_axis : int, optional
            This argument only applies when creating a model set (i.e. ``n_models >
            1``).  It changes how parameter values are interpreted.  Normally the
            first axis of each input parameter array (properly the 0th axis) is
            taken as the axis corresponding to the model sets.  However, any axis
            of an input array may be taken as this "model set axis".  This accepts
            negative integers as well--for example use ``model_set_axis=-1`` if the
            last (most rapidly changing) axis should be associated with the model
            sets. Also, ``model_set_axis=False`` can be used to tell that a given
            input should be used to evaluate all the models in the model set.

        fixed : dict, optional
            Dictionary ``{parameter_name: bool}`` setting the fixed constraint
            for one or more parameters.  `True` means the parameter is held fixed
            during fitting and is prevented from updates once an instance of the
            model has been created.

            Alternatively the `~astropy.modeling.Parameter.fixed` property of a
            parameter may be used to lock or unlock individual parameters.

        tied : dict, optional
            Dictionary ``{parameter_name: callable}`` of parameters which are
            linked to some other parameter. The dictionary values are callables
            providing the linking relationship.

            Alternatively the `~astropy.modeling.Parameter.tied` property of a
            parameter may be used to set the ``tied`` constraint on individual
            parameters.

        bounds : dict, optional
            A dictionary ``{parameter_name: value}`` of lower and upper bounds of
            parameters. Keys are parameter names. Values are a list or a tuple
            of length 2 giving the desired range for the parameter.

            Alternatively the `~astropy.modeling.Parameter.min` and
            `~astropy.modeling.Parameter.max` or
            ~astropy.modeling.Parameter.bounds` properties of a parameter may be
            used to set bounds on individual parameters.

        eqcons : list, optional
            List of functions of length n such that ``eqcons[j](x0, *args) == 0.0``
            in a successfully optimized problem.

        ineqcons : list, optional
            List of functions of length n such that ``ieqcons[j](x0, *args) >=
            0.0`` is a successfully optimized problem.

        Examples
        --------
        >>> from astropy.modeling import models
        >>> def tie_center(model):
        ...         mean = 50 * model.stddev
        ...         return mean
        >>> tied_parameters = {'mean': tie_center}

        Specify that ``'mean'`` is a tied parameter in one of two ways:

        >>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
        ...                        tied=tied_parameters)

        or

        >>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
        >>> g1.mean.tied
        False
        >>> g1.mean.tied = tie_center
        >>> g1.mean.tied
        <function tie_center at 0x...>

        Fixed parameters:

        >>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
        ...                        fixed={'stddev': True})
        >>> g1.stddev.fixed
        True

        or

        >>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
        >>> g1.stddev.fixed
        False
        >>> g1.stddev.fixed = True
        >>> g1.stddev.fixed
        True
        """

        parameter_constraints = Parameter.constraints
        """
        Primarily for informational purposes, these are the types of constraints
        that can be set on a model's parameters.
        """

        model_constraints = ("eqcons", "ineqcons")
        """
        Primarily for informational purposes, these are the types of constraints
        that constrain model evaluation.
        """

        param_names = ()
        """
        Names of the parameters that describe models of this type.

        The parameters in this tuple are in the same order they should be passed in
        when initializing a model of a specific type.  Some types of models, such
        as polynomial models, have a different number of parameters depending on
        some other property of the model, such as the degree.

        When defining a custom model class the value of this attribute is
        automatically set by the `~astropy.modeling.Parameter` attributes defined
        in the class body.
        """

        standard_broadcasting = True
        fittable = False
        linear = True
        _separable = None
        """ A boolean flag to indicate whether a model is separable."""
        meta = metadata.MetaData()
        """A dict-like object to store optional information."""

        # By default models either use their own inverse property or have no
        # inverse at all, but users may also assign a custom inverse to a model,
        # optionally; in that case it is of course up to the user to determine
        # whether their inverse is *actually* an inverse to the model they assign
        # it to.
        _inverse = None
        _user_inverse = None

        _bounding_box = None
        _user_bounding_box = None

        _has_inverse_bounding_box = False

        # Default n_models attribute, so that __len__ is still defined even when a
        # model hasn't completed initialization yet
        _n_models = 1

        # New classes can set this as a boolean value.
        # It is converted to a dictionary mapping input name to a boolean value.
        _input_units_strict = False

        # Allow dimensionless input (and corresponding output). If this is True,
        # input values to evaluate will gain the units specified in input_units. If
        # this is a dictionary then it should map input name to a bool to allow
        # dimensionless numbers for that input.
        # Only has an effect if input_units is defined.
        _input_units_allow_dimensionless = False

        # Default equivalencies to apply to input values. If set, this should be a
        # dictionary where each key is a string that corresponds to one of the
        # model inputs. Only has an effect if input_units is defined.
        input_units_equivalencies = None

        # Covariance matrix can be set by fitter if available.
        # If cov_matrix is available, then std will set as well
        _cov_matrix = None
        _stds = None

        def __init_subclass__(cls, **kwargs):
            super().__init_subclass__()

        def __init__(self, *args, meta=None, name=None, **kwargs):
            super().__init__()
            self._default_inputs_outputs()
            if meta is not None:
                self.meta = meta
            self._name = name
            # add parameters to instance level by walking MRO list
            mro = self.__class__.__mro__
            for cls in mro:
                if issubclass(cls, Model):
                    for parname, val in cls._parameters_.items():
                        newpar = copy.deepcopy(val)
                        newpar.model = self
                        if parname not in self.__dict__:
                            self.__dict__[parname] = newpar

            self._initialize_constraints(kwargs)
            kwargs = self._initialize_setters(kwargs)
            # Remaining keyword args are either parameter values or invalid
            # Parameter values must be passed in as keyword arguments in order to
            # distinguish them
            self._initialize_parameters(args, kwargs)
            self._initialize_slices()
            self._initialize_unit_support()

            # Initialize the cache for the constraints (used primarily when
            # sync_constraints is False)
            self._constraints_cache = {}

        def _default_inputs_outputs(self):
            if self.n_inputs == 1 and self.n_outputs == 1:
                self._inputs = ("x",)
                self._outputs = ("y",)
            elif self.n_inputs == 2 and self.n_outputs == 1:
                self._inputs = ("x", "y")
                self._outputs = ("z",)
            else:
                try:
                    self._inputs = tuple("x" + str(idx) for idx in range(self.n_inputs))
                    self._outputs = tuple("x" + str(idx) for idx in range(self.n_outputs))
                except TypeError:
                    # self.n_inputs and self.n_outputs are properties
                    # This is the case when subclasses of Model do not define
                    # ``n_inputs``, ``n_outputs``, ``inputs`` or ``outputs``.
                    self._inputs = ()
                    self._outputs = ()

        def _initialize_setters(self, kwargs):
            """
            This exists to inject defaults for settable properties for models
            originating from `~astropy.modeling.custom_model`.
            """
            if hasattr(self, "_settable_properties"):
                setters = {
                    name: kwargs.pop(name, default)
                    for name, default in self._settable_properties.items()
                }
                for name, value in setters.items():
                    setattr(self, name, value)

            return kwargs

        @property
        def inputs(self):
            return self._inputs

        @inputs.setter
        def inputs(self, val):
            if len(val) != self.n_inputs:
                raise ValueError(
                    f"Expected {self.n_inputs} number of inputs, got {len(val)}."
                )
            self._inputs = val
            self._initialize_unit_support()

        @property
        def outputs(self):
            return self._outputs

        @outputs.setter
        def outputs(self, val):
            if len(val) != self.n_outputs:
                raise ValueError(
                    f"Expected {self.n_outputs} number of outputs, got {len(val)}."
                )
            self._outputs = val

        @property
        def n_inputs(self) -> int:
            """The number of inputs."""
            return len(getattr(self, "inputs", ()))

        @property
        def n_outputs(self) -> int:
            """The number of outputs."""
            return len(getattr(self, "outputs", ()))

        def _calculate_separability_matrix(self):
            """
            This is a hook which customises the behavior of modeling.separable.

            This allows complex subclasses to customise the separability matrix.
            If it returns `NotImplemented` the default behavior is used.
            """
            return NotImplemented

        def _initialize_unit_support(self):
            """
            Convert self._input_units_strict and
            self.input_units_allow_dimensionless to dictionaries
            mapping input name to a boolean value.
            """
            if isinstance(self._input_units_strict, bool):
                self._input_units_strict = dict.fromkeys(
                    self.inputs, self._input_units_strict
                )

            if isinstance(self._input_units_allow_dimensionless, bool):
                self._input_units_allow_dimensionless = dict.fromkeys(
                    self.inputs, self._input_units_allow_dimensionless
                )

        @property
        def input_units_strict(self):
            """
            Enforce strict units on inputs to evaluate. If this is set to True,
            input values to evaluate will be in the exact units specified by
            input_units. If the input quantities are convertible to input_units,
            they are converted. If this is a dictionary then it should map input
            name to a bool to set strict input units for that parameter.
            """
            val = self._input_units_strict
            if isinstance(val, bool):
                return dict.fromkeys(self.inputs, val)
            return dict(zip(self.inputs, val.values()))

        @property
        def input_units_allow_dimensionless(self):
            """
            Allow dimensionless input (and corresponding output). If this is True,
            input values to evaluate will gain the units specified in input_units. If
            this is a dictionary then it should map input name to a bool to allow
            dimensionless numbers for that input.
            Only has an effect if input_units is defined.
            """
            val = self._input_units_allow_dimensionless
            if isinstance(val, bool):
                return dict.fromkeys(self.inputs, val)
            return dict(zip(self.inputs, val.values()))

        @property
        def uses_quantity(self):
            """
            True if this model has been created with `~astropy.units.Quantity`
            objects or if there are no parameters.

            This can be used to determine if this model should be evaluated with
            `~astropy.units.Quantity` or regular floats.
            """
            pisq = [isinstance(p, Quantity) for p in self._param_sets(units=True)]
            return (len(pisq) == 0) or any(pisq)

        def __repr__(self):
            return self._format_repr()

        def __str__(self):
            return self._format_str()

        def __len__(self):
            return self._n_models

        @staticmethod
        def _strip_ones(intup):
            return tuple(item for item in intup if item != 1)

        def __setattr__(self, attr, value):
            if isinstance(self, CompoundModel):
                param_names = self._param_names
            param_names = self.param_names

            if param_names is not None and attr in self.param_names:
                param = self.__dict__[attr]
                value = _tofloat(value)
                if param._validator is not None:
                    param._validator(self, value)
                # check consistency with previous shape and size
                eshape = self._param_metrics[attr]["shape"]
                if eshape == ():
                    eshape = (1,)
                vshape = np.array(value).shape
                if vshape == ():
                    vshape = (1,)
                esize = self._param_metrics[attr]["size"]
                if np.size(value) != esize or self._strip_ones(vshape) != self._strip_ones(
                    eshape
                ):
                    raise InputParameterError(
                        f"Value for parameter {attr} does not match shape or size\nexpected"
                        f" by model ({vshape}, {np.size(value)}) vs ({eshape}, {esize})"
                    )
                if param.unit is None:
                    if isinstance(value, Quantity):
                        param._unit = value.unit
                        param.value = value.value
                    else:
                        param.value = value
                else:
                    if not isinstance(value, Quantity):
                        raise UnitsError(
                            f"The '{param.name}' parameter should be given as a"
                            " Quantity because it was originally "
                            "initialized as a Quantity"
                        )
                    param._unit = value.unit
                    param.value = value.value
            else:
                if attr in ["fittable", "linear"]:
                    self.__dict__[attr] = value
                else:
                    super().__setattr__(attr, value)

        def _pre_evaluate(self, *args, **kwargs):
            """
            Model specific input setup that needs to occur prior to model evaluation.
            """
            # Broadcast inputs into common size
            inputs, broadcasted_shapes = self.prepare_inputs(*args, **kwargs)

            # Setup actual model evaluation method
            parameters = self._param_sets(raw=True, units=self._has_units)

            def evaluate(_inputs):
                return self.evaluate(*_inputs, *parameters)

            return evaluate, inputs, broadcasted_shapes, kwargs

        def get_bounding_box(self, with_bbox=True):
            """
            Return the ``bounding_box`` of a model if it exists or ``None``
            otherwise.

            Parameters
            ----------
            with_bbox :
                The value of the ``with_bounding_box`` keyword argument
                when calling the model. Default is `True` for usage when
                looking up the model's ``bounding_box`` without risk of error.
            """
            bbox = None

            if not isinstance(with_bbox, bool) or with_bbox:
                try:
                    bbox = self.bounding_box
                except NotImplementedError:
                    pass

                if isinstance(bbox, CompoundBoundingBox) and not isinstance(
                    with_bbox, bool
                ):
                    bbox = bbox[with_bbox]

            return bbox

        @property
        def _argnames(self):
            """The inputs used to determine input_shape for bounding_box evaluation."""
            return self.inputs

        def _validate_input_shape(
            self, _input, idx, argnames, model_set_axis, check_model_set_axis
        ):
            """Perform basic validation of a single model input's shape.

            The shape has the minimum dimensions for the given model_set_axis.

            Returns the shape of the input if validation succeeds.
            """
            input_shape = np.shape(_input)
            # Ensure that the input's model_set_axis matches the model's
            # n_models
            if input_shape and check_model_set_axis:
                # Note: Scalar inputs *only* get a pass on this
                if len(input_shape) < model_set_axis + 1:
                    raise ValueError(
                        f"For model_set_axis={model_set_axis}, all inputs must be at "
                        f"least {model_set_axis + 1}-dimensional."
                    )
                if input_shape[model_set_axis] != self._n_models:
                    try:
                        argname = argnames[idx]
                    except IndexError:
                        # the case of model.inputs = ()
                        argname = str(idx)

                    raise ValueError(
                        f"Input argument '{argname}' does not have the correct dimensions"
                        f" in model_set_axis={model_set_axis} for a model set with"
                        f" n_models={self._n_models}."
                    )

            return input_shape

        def _validate_input_shapes(self, inputs, argnames, model_set_axis):
            """
            Perform basic validation of model inputs
                --that they are mutually broadcastable and that they have
                the minimum dimensions for the given model_set_axis.

            If validation succeeds, returns the total shape that will result from
            broadcasting the input arrays with each other.
            """
            check_model_set_axis = self._n_models > 1 and model_set_axis is not False

            all_shapes = []
            for idx, _input in enumerate(inputs):
                all_shapes.append(
                    self._validate_input_shape(
                        _input, idx, argnames, model_set_axis, check_model_set_axis
                    )
                )

            # If we only have one input we don't need to broadcast it
            if len(all_shapes) == 1:
                return all_shapes[0]

            try:
                return np.broadcast_shapes(*all_shapes)
            except ValueError as exc:
                exc.add_note("All inputs must have identical shapes or must be scalars.")
                raise exc

        def input_shape(self, inputs):
            """Get input shape for bounding_box evaluation."""
            return self._validate_input_shapes(inputs, self._argnames, self.model_set_axis)

        def _generic_evaluate(self, evaluate, _inputs, fill_value, with_bbox):
            """Generic model evaluation routine.

            Selects and evaluates model with or without bounding_box enforcement.
            """
            # Evaluate the model using the prepared evaluation method either
            #   enforcing the bounding_box or not.
            bbox = self.get_bounding_box(with_bbox)
            if (not isinstance(with_bbox, bool) or with_bbox) and bbox is not None:
                outputs = bbox.evaluate(evaluate, _inputs, fill_value)
            else:
                outputs = evaluate(_inputs)
            return outputs

        def _post_evaluate(self, inputs, outputs, broadcasted_shapes, with_bbox, **kwargs):
            """
            Model specific post evaluation processing of outputs.
            """
            if self.get_bounding_box(with_bbox) is None and self.n_outputs == 1:
                outputs = (outputs,)

            outputs = self.prepare_outputs(broadcasted_shapes, *outputs, **kwargs)
            outputs = self._process_output_units(inputs, outputs)

            if self.n_outputs == 1:
                return outputs[0]
            return outputs

        @property
        def bbox_with_units(self):
            return not isinstance(self, CompoundModel)

        def __call__(self, *args, **kwargs):
            """
            Evaluate this model using the given input(s) and the parameter values
            that were specified when the model was instantiated.
            """
            # Turn any keyword arguments into positional arguments.
            args, kwargs = self._get_renamed_inputs_as_positional(*args, **kwargs)

            # Read model evaluation related parameters
            with_bbox = kwargs.pop("with_bounding_box", False)
            fill_value = kwargs.pop("fill_value", np.nan)

            # prepare for model evaluation (overridden in CompoundModel)
            evaluate, inputs, broadcasted_shapes, kwargs = self._pre_evaluate(
                *args, **kwargs
            )

            outputs = self._generic_evaluate(evaluate, inputs, fill_value, with_bbox)

            # post-process evaluation results (overridden in CompoundModel)
            return self._post_evaluate(
                inputs, outputs, broadcasted_shapes, with_bbox, **kwargs
            )

        def _get_renamed_inputs_as_positional(self, *args, **kwargs):
            def _keyword2positional(kwargs):
                # Inputs were passed as keyword (not positional) arguments.
                # Because the signature of the ``__call__`` is defined at
                # the class level, the name of the inputs cannot be changed at
                # the instance level and the old names are always present in the
                # signature of the method. In order to use the new names of the
                # inputs, the old names are taken out of ``kwargs``, the input
                # values are sorted in the order of self.inputs and passed as
                # positional arguments to ``__call__``.

                # These are the keys that are always present as keyword arguments.
                keys = [
                    "model_set_axis",
                    "with_bounding_box",
                    "fill_value",
                    "equivalencies",
                    "inputs_map",
                ]

                new_inputs = {}
                # kwargs contain the names of the new inputs + ``keys``
                allkeys = list(kwargs.keys())
                # Remove the names of the new inputs from kwargs and save them
                # to a dict ``new_inputs``.
                for key in allkeys:
                    if key not in keys:
                        new_inputs[key] = kwargs[key]
                        del kwargs[key]
                return new_inputs, kwargs

            n_args = len(args)

            new_inputs, kwargs = _keyword2positional(kwargs)
            n_all_args = n_args + len(new_inputs)

            if n_all_args < self.n_inputs:
                raise ValueError(
                    f"Missing input arguments - expected {self.n_inputs}, got {n_all_args}"
                )
            elif n_all_args > self.n_inputs:
                raise ValueError(
                    f"Too many input arguments - expected {self.n_inputs}, got {n_all_args}"
                )
            if n_args == 0:
                # Create positional arguments from the keyword arguments in ``new_inputs``.
                new_args = []
                for k in self.inputs:
                    new_args.append(new_inputs[k])
            elif n_args != self.n_inputs:
                # Some inputs are passed as positional, others as keyword arguments.
                args = list(args)

                # Create positional arguments from the keyword arguments in ``new_inputs``.
                new_args = []
                for k in self.inputs:
                    if k in new_inputs:
                        new_args.append(new_inputs[k])
                    else:
                        new_args.append(args[0])
                        del args[0]
            else:
                new_args = args
            return new_args, kwargs

        # *** Properties ***
        @property
        def name(self):
            """User-provided name for this model instance."""
            return self._name

        @name.setter
        def name(self, val):
            """Assign a (new) name to this model."""
            self._name = val

        @property
        def model_set_axis(self):
            """
            The index of the model set axis--that is the axis of a parameter array
            that pertains to which model a parameter value pertains to--as
            specified when the model was initialized.

            See the documentation on :ref:`astropy:modeling-model-sets`
            for more details.
            """
            return self._model_set_axis

        @property
        def param_sets(self):
            """
            Return parameters as a pset.

            This is a list with one item per parameter set, which is an array of
            that parameter's values across all parameter sets, with the last axis
            associated with the parameter set.
            """
            return self._param_sets()

        @property
        def parameters(self):
            """
            A flattened array of all parameter values in all parameter sets.

            Fittable parameters maintain this list and fitters modify it.
            """
            # Currently the sequence of a model's parameters must be contiguous
            # within the _parameters array (which may be a view of a larger array,
            # for example when taking a sub-expression of a compound model), so
            # the assumption here is reliable:
            if not self.param_names:
                # Trivial, but not unheard of
                return self._parameters

            self._parameters_to_array()
            start = self._param_metrics[self.param_names[0]]["slice"].start
            stop = self._param_metrics[self.param_names[-1]]["slice"].stop

            return self._parameters[start:stop]

        @parameters.setter
        def parameters(self, value):
            """
            Assigning to this attribute updates the parameters array rather than
            replacing it.
            """
            if not self.param_names:
                return

            start = self._param_metrics[self.param_names[0]]["slice"].start
            stop = self._param_metrics[self.param_names[-1]]["slice"].stop

            try:
                value = np.asanyarray(value).ravel()
                self._parameters[start:stop] = value
            except ValueError as e:
                raise InputParameterError(
                    "Input parameter values not compatible with the model "
                    f"parameters array: {e!r}"
                )
            self._array_to_parameters()

        @property
        def sync_constraints(self):
            """
            This is a boolean property that indicates whether or not accessing constraints
            automatically check the constituent models current values. It defaults to True
            on creation of a model, but for fitting purposes it should be set to False
            for performance reasons.
            """
            if not hasattr(self, "_sync_constraints"):
                self._sync_constraints = True
            return self._sync_constraints

        @sync_constraints.setter
        def sync_constraints(self, value):
            if not isinstance(value, bool):
                raise ValueError("sync_constraints only accepts True or False as values")
            self._sync_constraints = value

            # We need to invalidate the cache whenever sync_constraints is changed.
            # If we are setting sync_constraints to True, then this will ensure
            # that we recompute the properties next time they are called, and if
            # setting to False, it will allow us to make sure the cache is up-to-date
            # below before disabling syncing.
            self._constraints_cache.clear()

            # If setting to False, cache all the values with the present state
            # to make sure we don't ever update the cache once the syncing is
            # disabled. Note that these will automatically then cause 'fixed',
            # 'bounds' and 'tied' to be called.
            if not value:
                _ = self.has_fixed
                _ = self.has_bounds
                _ = self.has_tied

        @property
        def fixed(self):
            """
            A ``dict`` mapping parameter names to their fixed constraint.
            """
            if "fixed" not in self._constraints_cache or self.sync_constraints:
                self._constraints_cache["fixed"] = _ConstraintsDict(self, "fixed")
            return self._constraints_cache["fixed"]

        @property
        def bounds(self):
            """
            A ``dict`` mapping parameter names to their upper and lower bounds as
            ``(min, max)`` tuples or ``[min, max]`` lists.
            """
            if "bounds" not in self._constraints_cache or self.sync_constraints:
                self._constraints_cache["bounds"] = _ConstraintsDict(self, "bounds")
            return self._constraints_cache["bounds"]

        @property
        def tied(self):
            """
            A ``dict`` mapping parameter names to their tied constraint.
            """
            if "tied" not in self._constraints_cache or self.sync_constraints:
                self._constraints_cache["tied"] = _ConstraintsDict(self, "tied")
            return self._constraints_cache["tied"]

        @property
        def has_fixed(self):
            """
            Whether the model has any fixed constraints.
            """
            if "has_fixed" not in self._constraints_cache or self.sync_constraints:
                self._constraints_cache["has_fixed"] = any(self.fixed.values())
            return self._constraints_cache["has_fixed"]

        @property
        def has_bounds(self):
            """
            Whether the model has any bounds constraints.
            """
            if "has_bounds" not in self._constraints_cache or self.sync_constraints:
                self._constraints_cache["has_bounds"] = any(
                    b != (None, None) for b in self.bounds.values()
                )
            return self._constraints_cache["has_bounds"]

        @property
        def has_tied(self):
            """
            Whether the model has any tied constraints.
            """
            if "has_tied" not in self._constraints_cache or self.sync_constraints:
                self._constraints_cache["has_tied"] = any(self.tied.values())
            return self._constraints_cache["has_tied"]

        @property
        def eqcons(self):
            """List of parameter equality constraints."""
            return self._mconstraints["eqcons"]

        @property
        def ineqcons(self):
            """List of parameter inequality constraints."""
            return self._mconstraints["ineqcons"]

        def has_inverse(self):
            """
            Returns True if the model has an analytic or user
            inverse defined.
            """
            try:
                self.inverse  # noqa: B018
            except NotImplementedError:
                return False

            return True

        @property
        def inverse(self):
            """
            Returns a new `~astropy.modeling.Model` instance which performs the
            inverse transform, if an analytic inverse is defined for this model.

            Even on models that don't have an inverse defined, this property can be
            set with a manually-defined inverse, such a pre-computed or
            experimentally determined inverse (often given as a
            `~astropy.modeling.polynomial.PolynomialModel`, but not by
            requirement).

            A custom inverse can be deleted with ``del model.inverse``.  In this
            case the model's inverse is reset to its default, if a default exists
            (otherwise the default is to raise `NotImplementedError`).

            Note to authors of `~astropy.modeling.Model` subclasses:  To define an
            inverse for a model simply override this property to return the
            appropriate model representing the inverse.  The machinery that will
            make the inverse manually-overridable is added automatically by the
            base class.
            """
            if self._user_inverse is not None:
                return self._user_inverse
            elif self._inverse is not None:
                result = self._inverse()
                if result is not NotImplemented:
                    if not self._has_inverse_bounding_box:
                        result.bounding_box = None
                    return result

            raise NotImplementedError(
                "No analytical or user-supplied inverse transform "
                "has been implemented for this model."
            )

        @inverse.setter
        def inverse(self, value):
            if not isinstance(value, (Model, type(None))):
                raise ValueError(
                    "The inverse attribute may be assigned a Model instance "
                    "or None (where None explicitly forces the model to have "
                    "no inverse."
                )

            self._user_inverse = value

        @inverse.deleter
        def inverse(self):
            """
            Resets the model's inverse to its default (if one exists, otherwise
            the model will have no inverse).
            """
            try:
                del self._user_inverse
            except AttributeError:
                pass

        @property
        def has_user_inverse(self):
            """
            A flag indicating whether or not a custom inverse model has been
            assigned to this model by a user, via assignment to ``model.inverse``.
            """
            return self._user_inverse is not None

        @property
        def bounding_box(self):
            r"""
            A `tuple` of length `n_inputs` defining the bounding box limits, or
            raise `NotImplementedError` for no bounding_box.

            The default limits are given by a ``bounding_box`` property or method
            defined in the class body of a specific model.  If not defined then
            this property just raises `NotImplementedError` by default (but may be
            assigned a custom value by a user).  ``bounding_box`` can be set
            manually to an array-like object of shape ``(model.n_inputs, 2)``. For
            further usage, see :ref:`astropy:bounding-boxes`

            The limits are ordered according to the `numpy` ``'C'`` indexing
            convention, and are the reverse of the model input order,
            e.g. for inputs ``('x', 'y', 'z')``, ``bounding_box`` is defined:

            * for 1D: ``(x_low, x_high)``
            * for 2D: ``((y_low, y_high), (x_low, x_high))``
            * for 3D: ``((z_low, z_high), (y_low, y_high), (x_low, x_high))``

            Examples
            --------
            Setting the ``bounding_box`` limits for a 1D and 2D model:

            >>> from astropy.modeling.models import Gaussian1D, Gaussian2D
            >>> model_1d = Gaussian1D()
            >>> model_2d = Gaussian2D(x_stddev=1, y_stddev=1)
            >>> model_1d.bounding_box = (-5, 5)
            >>> model_2d.bounding_box = ((-6, 6), (-5, 5))

            Setting the bounding_box limits for a user-defined 3D
            `~astropy.modeling.custom_model`:

            >>> from astropy.modeling.models import custom_model
            >>> def const3d(x, y, z, amp=1):
            ...    return amp
            ...
            >>> Const3D = custom_model(const3d)
            >>> model_3d = Const3D()
            >>> model_3d.bounding_box = ((-6, 6), (-5, 5), (-4, 4))

            To reset ``bounding_box`` to its default limits just delete the
            user-defined value--this will reset it back to the default defined
            on the class:

            >>> del model_1d.bounding_box

            To disable the bounding box entirely (including the default),
            set ``bounding_box`` to `None`:

            >>> model_1d.bounding_box = None
            >>> model_1d.bounding_box  # doctest: +IGNORE_EXCEPTION_DETAIL
            Traceback (most recent call last):
            NotImplementedError: No bounding box is defined for this model
            (note: the bounding box was explicitly disabled for this model;
            use `del model.bounding_box` to restore the default bounding box,
            if one is defined for this model).
            """
            if self._user_bounding_box is not None:
                if self._user_bounding_box is NotImplemented:
                    raise NotImplementedError(
                        "No bounding box is defined for this model (note: the "
                        "bounding box was explicitly disabled for this model; "
                        "use `del model.bounding_box` to restore the default "
                        "bounding box, if one is defined for this model)."
                    )
                return self._user_bounding_box
            elif self._bounding_box is None:
                raise NotImplementedError("No bounding box is defined for this model.")
            elif isinstance(self._bounding_box, ModelBoundingBox):
                # This typically implies a hard-coded bounding box.  This will
                # probably be rare, but it is an option
                return self._bounding_box
            elif inspect.ismethod(self._bounding_box):
                return ModelBoundingBox.validate(self, self._bounding_box())
            else:
                # The only other allowed possibility is that it's a ModelBoundingBox
                # subclass, so we call it with its default arguments and return an
                # instance of it (that can be called to recompute the bounding box
                # with any optional parameters)
                # (In other words, in this case self._bounding_box is a *class*)
                bounding_box = self._bounding_box((), model=self)()
                return self._bounding_box(bounding_box, model=self)

        @bounding_box.setter
        def bounding_box(self, bounding_box):
            """
            Assigns the bounding box limits.
            """
            if bounding_box is None:
                cls = None
                # We use this to explicitly set an unimplemented bounding box (as
                # opposed to no user bounding box defined)
                bounding_box = NotImplemented
            elif isinstance(bounding_box, (CompoundBoundingBox, dict)):
                cls = CompoundBoundingBox
            elif isinstance(self._bounding_box, type) and issubclass(
                self._bounding_box, ModelBoundingBox
            ):
                cls = self._bounding_box
            else:
                cls = ModelBoundingBox

            if cls is not None:
                try:
                    bounding_box = cls.validate(self, bounding_box, _preserve_ignore=True)
                except ValueError as exc:
                    raise ValueError(exc.args[0])

            self._user_bounding_box = bounding_box

        def set_slice_args(self, *args):
            if isinstance(self._user_bounding_box, CompoundBoundingBox):
                self._user_bounding_box.slice_args = args
            else:
                raise RuntimeError("The bounding_box for this model is not compound")

        @bounding_box.deleter
        def bounding_box(self):
            self._user_bounding_box = None

        @property
        def has_user_bounding_box(self):
            """
            A flag indicating whether or not a custom bounding_box has been
            assigned to this model by a user, via assignment to
            ``model.bounding_box``.
            """
            return self._user_bounding_box is not None

        @property
        def cov_matrix(self):
            """
            Fitter should set covariance matrix, if available.
            """
            return self._cov_matrix

        @cov_matrix.setter
        def cov_matrix(self, cov):
            self._cov_matrix = cov

            unfix_untied_params = [
                p
                for p in self.param_names
                if (self.fixed[p] is False) and (self.tied[p] is False)
            ]
            if type(cov) == list:  # model set
                param_stds = []
                for c in cov:
                    param_stds.append(
                        [np.sqrt(x) if x > 0 else None for x in np.diag(c.cov_matrix)]
                    )
                for p, param_name in enumerate(unfix_untied_params):
                    par = getattr(self, param_name)
                    par.std = [item[p] for item in param_stds]
                    setattr(self, param_name, par)
            else:
                param_stds = [
                    np.sqrt(x) if x > 0 else None for x in np.diag(cov.cov_matrix)
                ]
                for param_name in unfix_untied_params:
                    par = getattr(self, param_name)
                    par.std = param_stds.pop(0)
                    setattr(self, param_name, par)

        @property
        def stds(self):
            """
            Standard deviation of parameters, if covariance matrix is available.
            """
            return self._stds

        @stds.setter
        def stds(self, stds):
            self._stds = stds

        @property
        def separable(self):
            """A flag indicating whether a model is separable."""
            if self._separable is not None:
                return self._separable
            raise NotImplementedError(
                'The "separable" property is not defined for '
                f"model {self.__class__.__name__}"
            )

        # *** Public methods ***

        def without_units_for_data(self, **kwargs):
            """
            Return an instance of the model for which the parameter values have
            been converted to the right units for the data, then the units have
            been stripped away.

            The input and output Quantity objects should be given as keyword
            arguments.

            Notes
            -----
            This method is needed in order to be able to fit models with units in
            the parameters, since we need to temporarily strip away the units from
            the model during the fitting (which might be done by e.g. scipy
            functions).

            The units that the parameters should be converted to are not
            necessarily the units of the input data, but are derived from them.
            Model subclasses that want fitting to work in the presence of
            quantities need to define a ``_parameter_units_for_data_units`` method
            that takes the input and output units (as two dictionaries) and
            returns a dictionary giving the target units for each parameter.

            """
            model = self.copy()

            inputs_unit = {
                inp: getattr(kwargs[inp], "unit", dimensionless_unscaled)
                for inp in self.inputs
                if kwargs[inp] is not None
            }

            outputs_unit = {
                out: getattr(kwargs[out], "unit", dimensionless_unscaled)
                for out in self.outputs
                if kwargs[out] is not None
            }
            parameter_units = self._parameter_units_for_data_units(
                inputs_unit, outputs_unit
            )
            for name, unit in parameter_units.items():
                parameter = getattr(model, name)
                if parameter.unit is not None:
                    parameter.value = parameter.quantity.to(unit).value
                    parameter._set_unit(None, force=True)

            if isinstance(model, CompoundModel):
                model.strip_units_from_tree()

            return model

        def output_units(self, **kwargs):
            """
            Return a dictionary of output units for this model given a dictionary
            of fitting inputs and outputs.

            The input and output Quantity objects should be given as keyword
            arguments.

            Notes
            -----
            This method is needed in order to be able to fit models with units in
            the parameters, since we need to temporarily strip away the units from
            the model during the fitting (which might be done by e.g. scipy
            functions).

            This method will force extra model evaluations, which maybe computationally
            expensive. To avoid this, one can add a return_units property to the model,
            see :ref:`astropy:models_return_units`.
            """
            units = self.return_units

            if units is None or units == {}:
                inputs = {inp: kwargs[inp] for inp in self.inputs}

                values = self(**inputs)
                if self.n_outputs == 1:
                    values = (values,)

                units = {
                    out: getattr(values[index], "unit", dimensionless_unscaled)
                    for index, out in enumerate(self.outputs)
                }

            return units

        def strip_units_from_tree(self):
            for item in self._leaflist:
                for parname in item.param_names:
                    par = getattr(item, parname)
                    par._set_unit(None, force=True)

        def with_units_from_data(self, **kwargs):
            """
            Return an instance of the model which has units for which the parameter
            values are compatible with the data units specified.

            The input and output Quantity objects should be given as keyword
            arguments.

            Notes
            -----
            This method is needed in order to be able to fit models with units in
            the parameters, since we need to temporarily strip away the units from
            the model during the fitting (which might be done by e.g. scipy
            functions).

            The units that the parameters will gain are not necessarily the units
            of the input data, but are derived from them. Model subclasses that
            want fitting to work in the presence of quantities need to define a
            ``_parameter_units_for_data_units`` method that takes the input and output
            units (as two dictionaries) and returns a dictionary giving the target
            units for each parameter.
            """
            model = self.copy()
            inputs_unit = {
                inp: getattr(kwargs[inp], "unit", dimensionless_unscaled)
                for inp in self.inputs
                if kwargs[inp] is not None
            }

            outputs_unit = {
                out: getattr(kwargs[out], "unit", dimensionless_unscaled)
                for out in self.outputs
                if kwargs[out] is not None
            }

            parameter_units = self._parameter_units_for_data_units(
                inputs_unit, outputs_unit
            )

            # We are adding units to parameters that already have a value, but we
            # don't want to convert the parameter, just add the unit directly,
            # hence the call to ``_set_unit``.
            for name, unit in parameter_units.items():
                parameter = getattr(model, name)
                parameter._set_unit(unit, force=True)

            return model

        @property
        def _has_units(self):
            # Returns True if any of the parameters have units
            return any(getattr(self, param).unit is not None for param in self.param_names)

        @property
        def _supports_unit_fitting(self):
            # If the model has a ``_parameter_units_for_data_units`` method, this
            # indicates that we have enough information to strip the units away
            # and add them back after fitting, when fitting quantities
            return hasattr(self, "_parameter_units_for_data_units")

        @abc.abstractmethod
        def evaluate(self, *args, **kwargs):
            """Evaluate the model on some input variables."""

        def sum_of_implicit_terms(self, *args, **kwargs):
            """
            Evaluate the sum of any implicit model terms on some input variables.
            This includes any fixed terms used in evaluating a linear model that
            do not have corresponding parameters exposed to the user. The
            prototypical case is `astropy.modeling.functional_models.Shift`, which
            corresponds to a function y = a + bx, where b=1 is intrinsically fixed
            by the type of model, such that sum_of_implicit_terms(x) == x. This
            method is needed by linear fitters to correct the dependent variable
            for the implicit term(s) when solving for the remaining terms
            (ie. a = y - bx).
            """

        def render(self, out=None, coords=None):
            """
            Evaluate a model at fixed positions, respecting the ``bounding_box``.

            The key difference relative to evaluating the model directly is that
            this method is limited to a bounding box if the
            `~astropy.modeling.Model.bounding_box` attribute is set.

            Parameters
            ----------
            out : `numpy.ndarray`, optional
                An array that the evaluated model will be added to.  If this is not
                given (or given as ``None``), a new array will be created.
            coords : array-like, optional
                An array to be used to translate from the model's input coordinates
                to the ``out`` array. It should have the property that
                ``self(coords)`` yields the same shape as ``out``.  If ``out`` is
                not specified, ``coords`` will be used to determine the shape of
                the returned array. If this is not provided (or None), the model
                will be evaluated on a grid determined by
                `~astropy.modeling.Model.bounding_box`.

            Returns
            -------
            out : `numpy.ndarray`
                The model added to ``out`` if ``out`` is not ``None``,
                or else a new array from evaluating the model
                over ``coords``. If ``out`` and ``coords`` are
                both `None`, the returned array is limited to the
                `~astropy.modeling.Model.bounding_box` limits. If
                `~astropy.modeling.Model.bounding_box` is `None`, ``arr`` or
                ``coords`` must be passed.

            Raises
            ------
            ValueError
                If ``coords`` are not given and the
                `~astropy.modeling.Model.bounding_box` of this model is not
                set.

            Examples
            --------
            :ref:`astropy:bounding-boxes`
            """
            try:
                bbox = self.bounding_box
            except NotImplementedError:
                bbox = None

            if isinstance(bbox, ModelBoundingBox):
                bbox = bbox.bounding_box()

            ndim = self.n_inputs

            if (coords is None) and (out is None) and (bbox is None):
                raise ValueError("If no bounding_box is set, coords or out must be input.")

            # for consistent indexing
            if ndim == 1:
                if coords is not None:
                    coords = [coords]
                if bbox is not None:
                    bbox = [bbox]

            if coords is not None:
                coords = np.asanyarray(coords, dtype=float)
                # Check dimensions match out and model
                assert len(coords) == ndim
                if out is not None:
                    if coords[0].shape != out.shape:
                        raise ValueError("inconsistent shape of the output.")
                else:
                    out = np.zeros(coords[0].shape)

            if out is not None:
                out = np.asanyarray(out)
                if out.ndim != ndim:
                    raise ValueError(
                        "the array and model must have the same number of dimensions."
                    )

            if bbox is not None:
                # Assures position is at center pixel,
                # important when using add_array.
                pd = (
                    np.array([(np.mean(bb), np.ceil((bb[1] - bb[0]) / 2)) for bb in bbox])
                    .astype(int)
                    .T
                )
                pos, delta = pd

                if coords is not None:
                    sub_shape = tuple(delta * 2 + 1)
                    sub_coords = np.array(
                        [extract_array(c, sub_shape, pos) for c in coords]
                    )
                else:
                    limits = [slice(p - d, p + d + 1, 1) for p, d in pd.T]
                    sub_coords = np.mgrid[limits]

                sub_coords = sub_coords[::-1]

                if out is None:
                    out = self(*sub_coords)
                else:
                    try:
                        out = add_array(out, self(*sub_coords), pos)
                    except ValueError:
                        raise ValueError(
                            "The `bounding_box` is larger than the input out in "
                            "one or more dimensions. Set "
                            "`model.bounding_box = None`."
                        )
            else:
                if coords is None:
                    im_shape = out.shape
                    limits = [slice(i) for i in im_shape]
                    coords = np.mgrid[limits]

                coords = coords[::-1]

                out += self(*coords)

            return out

        @property
        def input_units(self):
            """
            This property is used to indicate what units or sets of units the
            evaluate method expects, and returns a dictionary mapping inputs to
            units (or `None` if any units are accepted).

            Model sub-classes can also use function annotations in evaluate to
            indicate valid input units, in which case this property should
            not be overridden since it will return the input units based on the
            annotations.
            """
            if hasattr(self, "_input_units"):
                return self._input_units
            elif hasattr(self.evaluate, "__annotations__"):
                annotations = self.evaluate.__annotations__.copy()
                annotations.pop("return", None)
                if annotations:
                    # If there are not annotations for all inputs this will error.
                    return {name: annotations[name] for name in self.inputs}
            else:
                # None means any unit is accepted
                return None

        @property
        def return_units(self):
            """
            This property is used to indicate what units or sets of units the
            output of evaluate should be in, and returns a dictionary mapping
            outputs to units (or `None` if any units are accepted).

            Model sub-classes can also use function annotations in evaluate to
            indicate valid output units, in which case this property should not be
            overridden since it will return the return units based on the
            annotations.
            """
            if hasattr(self, "_return_units"):
                return self._return_units
            elif hasattr(self.evaluate, "__annotations__"):
                return self.evaluate.__annotations__.get("return", None)
            else:
                # None means any unit is accepted
                return None

        def _prepare_inputs_single_model(self, params, inputs, **kwargs):
            broadcasts = []
            for idx, _input in enumerate(inputs):
                input_shape = _input.shape

                # Ensure that array scalars are always upgrade to 1-D arrays for the
                # sake of consistency with how parameters work.  They will be cast back
                # to scalars at the end
                if not input_shape:
                    inputs[idx] = _input.reshape((1,))

                if not params:
                    max_broadcast = input_shape
                else:
                    max_broadcast = ()

                for param in params:
                    try:
                        # bypass the broadcast_shapes call for performance reasons
                        # if parameter is a scalar
                        if self.standard_broadcasting and param.shape:
                            broadcast = np.broadcast_shapes(input_shape, param.shape)
                        else:
                            broadcast = input_shape
                    except ValueError as exc:
                        exc.add_note(
                            f"self input argument {self.inputs[idx]!r} of shape"
                            f" {input_shape!r} cannot be broadcast with parameter"
                            f" {param.name!r} of shape {param.shape!r}.",
                        )
                        raise exc

                    if len(broadcast) > len(max_broadcast):
                        max_broadcast = broadcast
                    elif len(broadcast) == len(max_broadcast):
                        max_broadcast = max(max_broadcast, broadcast)

                broadcasts.append(max_broadcast)

            if self.n_outputs > self.n_inputs:
                extra_outputs = self.n_outputs - self.n_inputs
                if not broadcasts:
                    # If there were no inputs then the broadcasts list is empty
                    # just add a None since there is no broadcasting of outputs and
                    # inputs necessary (see _prepare_outputs_single_self)
                    broadcasts.append(None)
                broadcasts.extend([broadcasts[0]] * extra_outputs)

            return inputs, (broadcasts,)

        @staticmethod
        def _remove_axes_from_shape(shape, axis):
            """
            Given a shape tuple as the first input, construct a new one by  removing
            that particular axis from the shape and all preceding axes. Negative axis
            numbers are permittted, where the axis is relative to the last axis.
            """
            if len(shape) == 0:
                return shape
            if axis < 0:
                axis = len(shape) + axis
                return shape[:axis] + shape[axis + 1 :]
            if axis >= len(shape):
                axis = len(shape) - 1
            shape = shape[axis + 1 :]
            return shape

        def _prepare_inputs_model_set(self, params, inputs, model_set_axis_input, **kwargs):
            reshaped = []
            pivots = []

            model_set_axis_param = self.model_set_axis  # needed to reshape param
            for idx, _input in enumerate(inputs):
                max_param_shape = ()
                if self._n_models > 1 and model_set_axis_input is not False:
                    # Use the shape of the input *excluding* the model axis
                    input_shape = (
                        _input.shape[:model_set_axis_input]
                        + _input.shape[model_set_axis_input + 1 :]
                    )
                else:
                    input_shape = _input.shape

                for param in params:
                    try:
                        np.broadcast_shapes(
                            input_shape,
                            self._remove_axes_from_shape(param.shape, model_set_axis_param),
                        )
                    except ValueError as exc:
                        exc.add_note(
                            f"Model input argument {self.inputs[idx]!r} of shape"
                            f" {input_shape!r} "
                            f"cannot be broadcast with parameter {param.name!r} of shape "
                            f"{self._remove_axes_from_shape(param.shape, model_set_axis_param)!r}.",
                        )
                        raise exc

                    if len(param.shape) - 1 > len(max_param_shape):
                        max_param_shape = self._remove_axes_from_shape(
                            param.shape, model_set_axis_param
                        )

                # We've now determined that, excluding the model_set_axis, the
                # input can broadcast with all the parameters
                input_ndim = len(input_shape)
                if model_set_axis_input is False:
                    if len(max_param_shape) > input_ndim:
                        # Just needs to prepend new axes to the input
                        n_new_axes = 1 + len(max_param_shape) - input_ndim
                        new_axes = (1,) * n_new_axes
                        new_shape = new_axes + _input.shape
                        pivot = model_set_axis_param
                    else:
                        pivot = input_ndim - len(max_param_shape)
                        new_shape = _input.shape[:pivot] + (1,) + _input.shape[pivot:]
                    new_input = _input.reshape(new_shape)
                else:
                    if len(max_param_shape) >= input_ndim:
                        n_new_axes = len(max_param_shape) - input_ndim
                        pivot = self.model_set_axis
                        new_axes = (1,) * n_new_axes
                        new_shape = (
                            _input.shape[: pivot + 1] + new_axes + _input.shape[pivot + 1 :]
                        )
                        new_input = _input.reshape(new_shape)
                    else:
                        pivot = _input.ndim - len(max_param_shape) - 1
                        new_input = np.rollaxis(_input, model_set_axis_input, pivot + 1)
                pivots.append(pivot)
                reshaped.append(new_input)

            if self.n_inputs < self.n_outputs:
                pivots.extend([model_set_axis_input] * (self.n_outputs - self.n_inputs))

            return reshaped, (pivots,)

        def prepare_inputs(
            self, *inputs, model_set_axis=None, equivalencies=None, **kwargs
        ):
            """
            This method is used in `~astropy.modeling.Model.__call__` to ensure
            that all the inputs to the model can be broadcast into compatible
            shapes (if one or both of them are input as arrays), particularly if
            there are more than one parameter sets. This also makes sure that (if
            applicable) the units of the input will be compatible with the evaluate
            method.
            """
            # When we instantiate the model class, we make sure that __call__ can
            # take the following two keyword arguments: model_set_axis and
            # equivalencies.
            if model_set_axis is None:
                # By default the model_set_axis for the input is assumed to be the
                # same as that for the parameters the model was defined with
                # TODO: Ensure that negative model_set_axis arguments are respected
                model_set_axis = self.model_set_axis

            params = [getattr(self, name) for name in self.param_names]
            inputs = [np.asanyarray(_input, dtype=float) for _input in inputs]

            self._validate_input_shapes(inputs, self.inputs, model_set_axis)

            inputs_map = kwargs.get("inputs_map")

            inputs = self._validate_input_units(inputs, equivalencies, inputs_map)

            # The input formatting required for single models versus a multiple
            # model set are different enough that they've been split into separate
            # subroutines
            if self._n_models == 1:
                return self._prepare_inputs_single_model(params, inputs, **kwargs)
            else:
                return self._prepare_inputs_model_set(
                    params, inputs, model_set_axis, **kwargs
                )

        def _validate_input_units(self, inputs, equivalencies=None, inputs_map=None):
            inputs = list(inputs)
            name = self.name or self.__class__.__name__
            # Check that the units are correct, if applicable

            if self.input_units is not None:
                # If a leaflist is provided that means this is in the context of
                # a compound model and it is necessary to create the appropriate
                # alias for the input coordinate name for the equivalencies dict
                if inputs_map:
                    edict = {}
                    for mod, mapping in inputs_map:
                        if self is mod:
                            edict[mapping[0]] = equivalencies[mapping[1]]
                else:
                    edict = equivalencies
                # We combine any instance-level input equivalencies with user
                # specified ones at call-time.
                input_units_equivalencies = _combine_equivalency_dict(
                    self.inputs, edict, self.input_units_equivalencies
                )

                # We now iterate over the different inputs and make sure that their
                # units are consistent with those specified in input_units.
                for i in range(len(inputs)):
                    input_name = self.inputs[i]
                    input_unit = self.input_units.get(input_name, None)

                    if input_unit is None:
                        continue

                    if isinstance(inputs[i], Quantity):
                        # We check for consistency of the units with input_units,
                        # taking into account any equivalencies

                        if inputs[i].unit.is_equivalent(
                            input_unit, equivalencies=input_units_equivalencies[input_name]
                        ):
                            # If equivalencies have been specified, we need to
                            # convert the input to the input units - this is
                            # because some equivalencies are non-linear, and
                            # we need to be sure that we evaluate the model in
                            # its own frame of reference. If input_units_strict
                            # is set, we also need to convert to the input units.
                            if (
                                len(input_units_equivalencies) > 0
                                or self.input_units_strict[input_name]
                            ):
                                inputs[i] = inputs[i].to(
                                    input_unit,
                                    equivalencies=input_units_equivalencies[input_name],
                                )

                        else:
                            # We consider the following two cases separately so as
                            # to be able to raise more appropriate/nicer exceptions

                            if input_unit is dimensionless_unscaled:
                                raise UnitsError(
                                    f"{name}: Units of input '{self.inputs[i]}', "
                                    f"{inputs[i].unit} ({inputs[i].unit.physical_type}),"
                                    "could not be converted to "
                                    "required dimensionless "
                                    "input"
                                )
                            else:
                                raise UnitsError(
                                    f"{name}: Units of input '{self.inputs[i]}', "
                                    f"{inputs[i].unit} ({inputs[i].unit.physical_type}),"
                                    " could not be "
                                    "converted to required input"
                                    f" units of {input_unit} ({input_unit.physical_type})"
                                )
                    else:
                        # If we allow dimensionless input, we add the units to the
                        # input values without conversion, otherwise we raise an
                        # exception.

                        if (
                            not self.input_units_allow_dimensionless[input_name]
                            and input_unit is not dimensionless_unscaled
                            and input_unit is not None
                        ):
                            if np.any(inputs[i] != 0):
                                raise UnitsError(
                                    f"{name}: Units of input '{self.inputs[i]}',"
                                    " (dimensionless), could not be converted to required "
                                    f"input units of {input_unit} "
                                    f"({input_unit.physical_type})"
                                )
            return inputs

        def _process_output_units(self, inputs, outputs):
            inputs_are_quantity = any(isinstance(i, Quantity) for i in inputs)
            if self.return_units and inputs_are_quantity:
                # We allow a non-iterable unit only if there is one output
                if self.n_outputs == 1 and not np.iterable(self.return_units):
                    return_units = {self.outputs[0]: self.return_units}
                else:
                    return_units = self.return_units

                outputs = tuple(
                    Quantity(out, return_units.get(out_name, None), subok=True)
                    for out, out_name in zip(outputs, self.outputs)
                )
            return outputs

        @staticmethod
        def _prepare_output_single_model(output, broadcast_shape):
            if broadcast_shape is not None:
                if not broadcast_shape:
                    return output.item()
                else:
                    try:
                        return output.reshape(broadcast_shape)
                    except ValueError:
                        try:
                            return output.item()
                        except ValueError:
                            return output

            return output

        def _prepare_outputs_single_model(self, outputs, broadcasted_shapes):
            outputs = list(outputs)
            shapes = broadcasted_shapes[0]
            for idx, output in enumerate(outputs):
                if None in shapes:
                    # Previously, we used our own function (check_broadcast) instead
                    # of np.broadcast_shapes in the following try block
                    # - check_broadcast raised an exception when passed a None.
                    # - as of numpy 1.26, np.broadcast raises a deprecation warning
                    # when passed a `None` value, but returns an empty tuple.
                    #
                    # Since () and None have different effects downstream of this function,
                    # and to preserve backward-compatibility, we handle this special here
                    broadcast_shape = shapes[idx]
                else:
                    try:
                        broadcast_shape = np.broadcast_shapes(*shapes)
                    except Exception:
                        broadcast_shape = shapes[idx]

                outputs[idx] = self._prepare_output_single_model(output, broadcast_shape)

            return tuple(outputs)

        def _prepare_outputs_model_set(self, outputs, broadcasted_shapes, model_set_axis):
            pivots = broadcasted_shapes[0]
            # If model_set_axis = False was passed then use
            # self._model_set_axis to format the output.
            if model_set_axis is None or model_set_axis is False:
                model_set_axis = self.model_set_axis
            outputs = list(outputs)
            for idx, output in enumerate(outputs):
                pivot = pivots[idx]
                if pivot < output.ndim and pivot != model_set_axis:
                    outputs[idx] = np.rollaxis(output, pivot, model_set_axis)
            return tuple(outputs)

        def prepare_outputs(self, broadcasted_shapes, *outputs, **kwargs):
            model_set_axis = kwargs.get("model_set_axis")

            if len(self) == 1:
                return self._prepare_outputs_single_model(outputs, broadcasted_shapes)
            else:
                return self._prepare_outputs_model_set(
                    outputs, broadcasted_shapes, model_set_axis
                )

        def copy(self):
            """
            Return a copy of this model.

            Uses a deep copy so that all model attributes, including parameter
            values, are copied as well.
            """
            return copy.deepcopy(self)

        def deepcopy(self):
            """
            Return a deep copy of this model.

            """
            return self.copy()

        @sharedmethod
        def rename(self, name):
            """
            Return a copy of this model with a new name.
            """
            new_model = self.copy()
            new_model._name = name
            return new_model

        def coerce_units(
            self,
            input_units=None,
            return_units=None,
            input_units_equivalencies=None,
            input_units_allow_dimensionless=False,
        ):
            """
            Attach units to this (unitless) model.

            Parameters
            ----------
            input_units : dict or tuple, optional
                Input units to attach.  If dict, each key is the name of a model input,
                and the value is the unit to attach.  If tuple, the elements are units
                to attach in order corresponding to `~astropy.modeling.Model.inputs`.
            return_units : dict or tuple, optional
                Output units to attach.  If dict, each key is the name of a model output,
                and the value is the unit to attach.  If tuple, the elements are units
                to attach in order corresponding to `~astropy.modeling.Model.outputs`.
            input_units_equivalencies : dict, optional
                Default equivalencies to apply to input values.  If set, this should be a
                dictionary where each key is a string that corresponds to one of the
                model inputs.
            input_units_allow_dimensionless : bool or dict, optional
                Allow dimensionless input. If this is True, input values to evaluate will
                gain the units specified in input_units. If this is a dictionary then it
                should map input name to a bool to allow dimensionless numbers for that
                input.

            Returns
            -------
            `~astropy.modeling.CompoundModel`
                A `~astropy.modeling.CompoundModel` composed of the current
                model plus `~astropy.modeling.mappings.UnitsMapping`
                model(s) that attach the units.

            Raises
            ------
            ValueError
                If the current model already has units.

            Examples
            --------
            Wrapping a unitless model to require and convert units:

            >>> from astropy.modeling.models import Polynomial1D
            >>> from astropy import units as u
            >>> poly = Polynomial1D(1, c0=1, c1=2)
            >>> model = poly.coerce_units((u.m,), (u.s,))
            >>> model(u.Quantity(10, u.m))  # doctest: +FLOAT_CMP
            <Quantity 21. s>
            >>> model(u.Quantity(1000, u.cm))  # doctest: +FLOAT_CMP
            <Quantity 21. s>
            >>> model(u.Quantity(10, u.cm))  # doctest: +FLOAT_CMP
            <Quantity 1.2 s>

            Wrapping a unitless model but still permitting unitless input:

            >>> from astropy.modeling.models import Polynomial1D
            >>> from astropy import units as u
            >>> poly = Polynomial1D(1, c0=1, c1=2)
            >>> model = poly.coerce_units((u.m,), (u.s,), input_units_allow_dimensionless=True)
            >>> model(u.Quantity(10, u.m))  # doctest: +FLOAT_CMP
            <Quantity 21. s>
            >>> model(10)  # doctest: +FLOAT_CMP
            <Quantity 21. s>
            """
            from .mappings import UnitsMapping

            result = self

            if input_units is not None:
                if self.input_units is not None:
                    model_units = self.input_units
                else:
                    model_units = {}

                for unit in [model_units.get(i) for i in self.inputs]:
                    if unit is not None and unit != dimensionless_unscaled:
                        raise ValueError(
                            "Cannot specify input_units for model with existing input units"
                        )

                if isinstance(input_units, dict):
                    if input_units.keys() != set(self.inputs):
                        message = (
                            f"""input_units keys ({", ".join(input_units.keys())}) """
                            f"""do not match model inputs ({", ".join(self.inputs)})"""
                        )
                        raise ValueError(message)
                    input_units = [input_units[i] for i in self.inputs]

                if len(input_units) != self.n_inputs:
                    message = (
                        "input_units length does not match n_inputs: "
                        f"expected {self.n_inputs}, received {len(input_units)}"
                    )
                    raise ValueError(message)

                mapping = tuple(
                    (unit, model_units.get(i)) for i, unit in zip(self.inputs, input_units)
                )
                input_mapping = UnitsMapping(
                    mapping,
                    input_units_equivalencies=input_units_equivalencies,
                    input_units_allow_dimensionless=input_units_allow_dimensionless,
                )
                input_mapping.inputs = self.inputs
                input_mapping.outputs = self.inputs
                result = input_mapping | result

            if return_units is not None:
                if self.return_units is not None:
                    model_units = self.return_units
                else:
                    model_units = {}

                for unit in [model_units.get(i) for i in self.outputs]:
                    if unit is not None and unit != dimensionless_unscaled:
                        raise ValueError(
                            "Cannot specify return_units for model "
                            "with existing output units"
                        )

                if isinstance(return_units, dict):
                    if return_units.keys() != set(self.outputs):
                        message = (
                            f"""return_units keys ({", ".join(return_units.keys())}) """
                            f"""do not match model outputs ({", ".join(self.outputs)})"""
                        )
                        raise ValueError(message)
                    return_units = [return_units[i] for i in self.outputs]

                if len(return_units) != self.n_outputs:
                    message = (
                        "return_units length does not match n_outputs: "
                        f"expected {self.n_outputs}, received {len(return_units)}"
                    )
                    raise ValueError(message)

                mapping = tuple(
                    (model_units.get(i), unit)
                    for i, unit in zip(self.outputs, return_units)
                )
                return_mapping = UnitsMapping(mapping)
                return_mapping.inputs = self.outputs
                return_mapping.outputs = self.outputs
                result = result | return_mapping

            return result

        @property
        def n_submodels(self):
            """
            Return the number of components in a single model, which is
            obviously 1.
            """
            return 1

        def _initialize_constraints(self, kwargs):
            """
            Pop parameter constraint values off the keyword arguments passed
            to `~astropy.modeling.Model.__init__` and store them in private
            instance attributes.
            """
            # Pop any constraints off the keyword arguments
            for constraint in self.parameter_constraints:
                values = kwargs.pop(constraint, {})
                for ckey, cvalue in values.items():
                    param = getattr(self, ckey)
                    setattr(param, constraint, cvalue)
            self._mconstraints = {}
            for constraint in self.model_constraints:
                values = kwargs.pop(constraint, [])
                self._mconstraints[constraint] = values

        def _reset_parameters(self, *args, **kwargs):
            """
            Reset parameters on the models to those specified.

            Parameters can be specified either as positional arguments or keyword
            arguments, as in the model initializer. Any parameters not specified
            will be reset to their default values.
            """
            self._initialize_parameters(args, kwargs)
            self._initialize_slices()

        def _initialize_parameters(self, args, kwargs):
            """
            Initialize the _parameters array that stores raw parameter values for
            all parameter sets for use with vectorized fitting algorithms; on
            FittableModels the _param_name attributes actually just reference
            slices of this array.
            """
            n_models = kwargs.pop("n_models", None)

            if not (
                n_models is None
                or (isinstance(n_models, (int, np.integer)) and n_models >= 1)
            ):
                raise ValueError(
                    "n_models must be either None (in which case it is "
                    "determined from the model_set_axis of the parameter initial "
                    "values) or it must be a positive integer "
                    f"(got {n_models!r})"
                )

            model_set_axis = kwargs.pop("model_set_axis", None)
            if model_set_axis is None:
                if n_models is not None and n_models > 1:
                    # Default to zero
                    model_set_axis = 0
                else:
                    # Otherwise disable
                    model_set_axis = False
            else:
                if not (
                    model_set_axis is False
                    or np.issubdtype(type(model_set_axis), np.integer)
                ):
                    raise ValueError(
                        "model_set_axis must be either False or an integer "
                        "specifying the parameter array axis to map to each "
                        f"model in a set of models (got {model_set_axis!r})."
                    )

            # Process positional arguments by matching them up with the
            # corresponding parameters in self.param_names--if any also appear as
            # keyword arguments this presents a conflict
            params = set()
            if len(args) > len(self.param_names):
                raise TypeError(
                    f"{self.__class__.__name__}.__init__() takes at most "
                    f"{len(self.param_names)} positional arguments ({len(args)} given)"
                )

            self._model_set_axis = model_set_axis
            self._param_metrics = defaultdict(dict)

            for idx, arg in enumerate(args):
                if arg is None:
                    # A value of None implies using the default value, if exists
                    continue
                # We use quantity_asanyarray here instead of np.asanyarray because
                # if any of the arguments are quantities, we need to return a
                # Quantity object not a plain Numpy array.
                param_name = self.param_names[idx]
                params.add(param_name)
                if not isinstance(arg, Parameter):
                    value = quantity_asanyarray(arg, dtype=float)
                else:
                    value = arg
                self._initialize_parameter_value(param_name, value)

            # At this point the only remaining keyword arguments should be
            # parameter names; any others are in error.
            for param_name in self.param_names:
                if param_name in kwargs:
                    if param_name in params:
                        raise TypeError(
                            f"{self.__class__.__name__}.__init__() got multiple values for"
                            f" parameter {param_name!r}"
                        )
                    value = kwargs.pop(param_name)
                    if value is None:
                        continue
                    # We use quantity_asanyarray here instead of np.asanyarray
                    # because if any of the arguments are quantities, we need
                    # to return a Quantity object not a plain Numpy array.
                    value = quantity_asanyarray(value, dtype=float)
                    params.add(param_name)
                    self._initialize_parameter_value(param_name, value)
            # Now deal with case where param_name is not supplied by args or kwargs
            for param_name in self.param_names:
                if param_name not in params:
                    self._initialize_parameter_value(param_name, None)

            if kwargs:
                # If any keyword arguments were left over at this point they are
                # invalid--the base class should only be passed the parameter
                # values, constraints, and param_dim
                for kwarg in kwargs:
                    # Just raise an error on the first unrecognized argument
                    raise TypeError(
                        f"{self.__class__.__name__}.__init__() got an unrecognized"
                        f" parameter {kwarg!r}"
                    )

            # Determine the number of model sets: If the model_set_axis is
            # None then there is just one parameter set; otherwise it is determined
            # by the size of that axis on the first parameter--if the other
            # parameters don't have the right number of axes or the sizes of their
            # model_set_axis don't match an error is raised
            if model_set_axis is not False and n_models != 1 and params:
                max_ndim = 0
                if model_set_axis < 0:
                    min_ndim = abs(model_set_axis)
                else:
                    min_ndim = model_set_axis + 1

                for name in self.param_names:
                    value = getattr(self, name)
                    param_ndim = np.ndim(value)
                    if param_ndim < min_ndim:
                        raise InputParameterError(
                            "All parameter values must be arrays of dimension at least"
                            f" {min_ndim} for model_set_axis={model_set_axis} (the value"
                            f" given for {name!r} is only {param_ndim}-dimensional)"
                        )

                    max_ndim = max(max_ndim, param_ndim)

                    if n_models is None:
                        # Use the dimensions of the first parameter to determine
                        # the number of model sets
                        n_models = value.shape[model_set_axis]
                    elif value.shape[model_set_axis] != n_models:
                        raise InputParameterError(
                            f"Inconsistent dimensions for parameter {name!r} for"
                            f" {n_models} model sets.  The length of axis"
                            f" {model_set_axis} must be the same for all input parameter"
                            " values"
                        )

                self._check_param_broadcast(max_ndim)
            else:
                if n_models is None:
                    n_models = 1

                self._check_param_broadcast(None)

            self._n_models = n_models
            # now validate parameters
            for name in params:
                param = getattr(self, name)
                if param._validator is not None:
                    param._validator(self, param.value)

        def _initialize_parameter_value(self, param_name, value):
            """Mostly deals with consistency checks and determining unit issues."""
            if isinstance(value, Parameter):
                self.__dict__[param_name] = value
                return
            param = getattr(self, param_name)
            # Use default if value is not provided
            if value is None:
                default = param.default
                if default is None:
                    # No value was supplied for the parameter and the
                    # parameter does not have a default, therefore the model
                    # is underspecified
                    raise TypeError(
                        f"{self.__class__.__name__}.__init__() requires a value for "
                        f"parameter {param_name!r}"
                    )
                value = default
                unit = param.unit
            else:
                if isinstance(value, Quantity):
                    unit = value.unit
                    value = value.value
                else:
                    unit = None
            if unit is None and param.unit is not None:
                raise InputParameterError(
                    f"{self.__class__.__name__}.__init__() requires a Quantity for"
                    f" parameter {param_name!r}"
                )

            param._unit = unit
            param._set_unit(unit, force=True)
            param.internal_unit = None
            if param._setter is not None:
                if unit is not None:
                    _val = param._setter(value * unit)
                else:
                    _val = param._setter(value)
                if isinstance(_val, Quantity):
                    param.internal_unit = _val.unit
                    param._internal_value = np.array(_val.value)
                else:
                    param.internal_unit = None
                    param._internal_value = np.array(_val)
            else:
                param._value = np.array(value)

        def _initialize_slices(self):
            param_metrics = self._param_metrics
            total_size = 0

            for name in self.param_names:
                param = getattr(self, name)
                value = param.value
                param_size = np.size(value)
                param_shape = np.shape(value)
                param_slice = slice(total_size, total_size + param_size)
                param_metrics[name]["slice"] = param_slice
                param_metrics[name]["shape"] = param_shape
                param_metrics[name]["size"] = param_size
                total_size += param_size
            self._parameters = np.empty(total_size, dtype=np.float64)

        def _parameters_to_array(self):
            # Now set the parameter values (this will also fill
            # self._parameters)
            param_metrics = self._param_metrics
            for name in self.param_names:
                param = getattr(self, name)
                value = param.value
                if not isinstance(value, np.ndarray):
                    value = np.array([value])
                self._parameters[param_metrics[name]["slice"]] = value.ravel()

            # Finally validate all the parameters; we do this last so that
            # validators that depend on one of the other parameters' values will
            # work

        def _array_to_parameters(self):
            param_metrics = self._param_metrics
            for name in self.param_names:
                param = getattr(self, name)
                value = self._parameters[param_metrics[name]["slice"]]
                value.shape = param_metrics[name]["shape"]
                param.value = value

        def _check_param_broadcast(self, max_ndim):
            """
            This subroutine checks that all parameter arrays can be broadcast
            against each other, and determines the shapes parameters must have in
            order to broadcast correctly.

            If model_set_axis is None this merely checks that the parameters
            broadcast and returns an empty dict if so.  This mode is only used for
            single model sets.
            """
            all_shapes = []
            model_set_axis = self._model_set_axis

            for name in self.param_names:
                param = getattr(self, name)
                value = param.value
                param_shape = np.shape(value)
                param_ndim = len(param_shape)
                if max_ndim is not None and param_ndim < max_ndim:
                    # All arrays have the same number of dimensions up to the
                    # model_set_axis dimension, but after that they may have a
                    # different number of trailing axes.  The number of trailing
                    # axes must be extended for mutual compatibility.  For example
                    # if max_ndim = 3 and model_set_axis = 0, an array with the
                    # shape (2, 2) must be extended to (2, 1, 2).  However, an
                    # array with shape (2,) is extended to (2, 1).
                    new_axes = (1,) * (max_ndim - param_ndim)

                    if model_set_axis < 0:
                        # Just need to prepend axes to make up the difference
                        broadcast_shape = new_axes + param_shape
                    else:
                        broadcast_shape = (
                            param_shape[: model_set_axis + 1]
                            + new_axes
                            + param_shape[model_set_axis + 1 :]
                        )
                    self._param_metrics[name]["broadcast_shape"] = broadcast_shape
                    all_shapes.append(broadcast_shape)
                else:
                    all_shapes.append(param_shape)

            # Now check mutual broadcastability of all shapes
            try:
                np.broadcast_shapes(*all_shapes)
            except ValueError as exc:
                base_message = (
                    "All parameter arrays "
                    "must have shapes that are mutually compatible according "
                    "to the broadcasting rules."
                )
                raise InputParameterError(f"{base_message} {repr(exc)}") from None

        def _param_sets(self, raw=False, units=False):
            """
            Implementation of the Model.param_sets property.

            This internal implementation has a ``raw`` argument which controls
            whether or not to return the raw parameter values (i.e. the values that
            are actually stored in the ._parameters array, as opposed to the values
            displayed to users.  In most cases these are one in the same but there
            are currently a few exceptions.

            Note: This is notably an overcomplicated device and may be removed
            entirely in the near future.
            """
            values = []
            shapes = []
            for name in self.param_names:
                param = getattr(self, name)

                if raw and param._setter:
                    value = param._internal_value
                else:
                    value = param.value

                broadcast_shape = self._param_metrics[name].get("broadcast_shape")
                if broadcast_shape is not None:
                    value = value.reshape(broadcast_shape)

                shapes.append(np.shape(value))

                if len(self) == 1:
                    # Add a single param set axis to the parameter's value (thus
                    # converting scalars to shape (1,) array values) for
                    # consistency
                    value = np.array([value])

                if units:
                    if raw and param.internal_unit is not None:
                        unit = param.internal_unit
                    else:
                        unit = param.unit
                    if unit is not None:
                        value = Quantity(value, unit, subok=True)

                values.append(value)

            if len(set(shapes)) != 1 or units:
                # If the parameters are not all the same shape, converting to an
                # array is going to produce an object array
                # However the way Numpy creates object arrays is tricky in that it
                # will recurse into array objects in the list and break them up
                # into separate objects.  Doing things this way ensures a 1-D
                # object array the elements of which are the individual parameter
                # arrays.  There's not much reason to do this over returning a list
                # except for consistency
                psets = np.empty(len(values), dtype=object)
                psets[:] = values
                return psets

            return np.array(values)

        def _format_repr(self, args=[], kwargs={}, defaults={}):
            """
            Internal implementation of ``__repr__``.

            This is separated out for ease of use by subclasses that wish to
            override the default ``__repr__`` while keeping the same basic
            formatting.
            """
            parts = [repr(a) for a in args]

            parts.extend(
                f"{name}={param_repr_oneline(getattr(self, name))}"
                for name in self.param_names
            )

            if self.name is not None:
                parts.append(f"name={self.name!r}")

            for kwarg, value in kwargs.items():
                if kwarg in defaults and defaults[kwarg] == value:
                    continue
                parts.append(f"{kwarg}={value!r}")

            if len(self) > 1:
                parts.append(f"n_models={len(self)}")

            return f"<{self.__class__.__name__}({', '.join(parts)})>"

        def _format_str(self, keywords=[], defaults={}):
            """
            Internal implementation of ``__str__``.

            This is separated out for ease of use by subclasses that wish to
            override the default ``__str__`` while keeping the same basic
            formatting.
            """
            default_keywords = [
                ("Model", self.__class__.__name__),
                ("Name", self.name),
                ("Inputs", self.inputs),
                ("Outputs", self.outputs),
                ("Model set size", len(self)),
            ]

            parts = [
                f"{keyword}: {value}"
                for keyword, value in default_keywords
                if value is not None
            ]

            for keyword, value in keywords:
                if keyword.lower() in defaults and defaults[keyword.lower()] == value:
                    continue
                parts.append(f"{keyword}: {value}")
            parts.append("Parameters:")

            if len(self) == 1:
                columns = [[getattr(self, name).value] for name in self.param_names]
            else:
                columns = [getattr(self, name).value for name in self.param_names]

            if columns:
                param_table = Table(columns, names=self.param_names)
                # Set units on the columns
                for name in self.param_names:
                    param_table[name].unit = getattr(self, name).unit
                parts.append(indent(str(param_table), 4 * " "))

            return "\n".join(parts)

    class FittableModel(Model):
        """
        Base class for models that can be fitted using the built-in fitting
        algorithms.
        """

        linear = False
        # derivative with respect to parameters
        fit_deriv = None
        """
        Function (similar to the model's `~astropy.modeling.Model.evaluate`)
        to compute the derivatives of the model with respect to its
        parameters, for use by fitting algorithms. In other words, this
        computes the Jacobian matrix with respect to the model's parameters.
        """
        # Flag that indicates if the model derivatives with respect to parameters
        # are given in columns or rows
        col_fit_deriv = True
        fittable = True

    class Fittable1DModel(FittableModel):
        """
        Base class for one-dimensional fittable models.

        This class provides an easier interface to defining new models.
        Examples can be found in `astropy.modeling.functional_models`.
        """

        n_inputs = 1
        n_outputs = 1
        _separable = True

    class Fittable2DModel(FittableModel):
        """
        Base class for two-dimensional fittable models.

        This class provides an easier interface to defining new models.
        Examples can be found in `astropy.modeling.functional_models`.
        """

        n_inputs = 2
        n_outputs = 1

    def _make_arithmetic_operator(oper):
        # We don't bother with tuple unpacking here for efficiency's sake, but for
        # documentation purposes:
        #
        #     f_eval, f_n_inputs, f_n_outputs = f
        #
        # and similarly for g
        def op(f, g):
            return (make_binary_operator_eval(oper, f[0], g[0]), f[1], f[2])

        return op

    def _composition_operator(f, g):
        # We don't bother with tuple unpacking here for efficiency's sake, but for
        # documentation purposes:
        #
        #     f_eval, f_n_inputs, f_n_outputs = f
        #
        # and similarly for g
        return (lambda inputs, params: g[0](f[0](inputs, params), params), f[1], g[2])

    def _join_operator(f, g):
        # We don't bother with tuple unpacking here for efficiency's sake, but for
        # documentation purposes:
        #
        #     f_eval, f_n_inputs, f_n_outputs = f
        #
        # and similarly for g
        return (
            lambda inputs, params: (
                f[0](inputs[: f[1]], params) + g[0](inputs[f[1] :], params)
            ),
            f[1] + g[1],
            f[2] + g[2],
        )

    BINARY_OPERATORS = {
        "+": _make_arithmetic_operator(operator.add),
        "-": _make_arithmetic_operator(operator.sub),
        "*": _make_arithmetic_operator(operator.mul),
        "/": _make_arithmetic_operator(operator.truediv),
        "**": _make_arithmetic_operator(operator.pow),
        "|": _composition_operator,
        "&": _join_operator,
    }

    SPECIAL_OPERATORS = _SpecialOperatorsDict()

    def _add_special_operator(sop_name, sop):
        return SPECIAL_OPERATORS.add(sop_name, sop)

    class CompoundModel(Model):
        """
        Base class for compound models.

        While it can be used directly, the recommended way
        to combine models is through the model operators.
        """

        def __init__(self, op, left, right, name=None):
            self.__dict__["_param_names"] = None
            self._n_submodels = None
            self.op = op
            self.left = left
            self.right = right
            self._bounding_box = None
            self._user_bounding_box = None
            self._leaflist = None
            self._tdict = None
            self._parameters = None
            self._parameters_ = None
            self._param_metrics = None

            if op != "fix_inputs" and len(left) != len(right):
                raise ValueError("Both operands must have equal values for n_models")
            self._n_models = len(left)

            if op != "fix_inputs" and (
                (left.model_set_axis != right.model_set_axis) or left.model_set_axis
            ):  # not False and not 0
                raise ValueError(
                    "model_set_axis must be False or 0 and consistent for operands"
                )
            self._model_set_axis = left.model_set_axis

            if op in ["+", "-", "*", "/", "**"] or op in SPECIAL_OPERATORS:
                if left.n_inputs != right.n_inputs or left.n_outputs != right.n_outputs:
                    raise ModelDefinitionError(
                        "Both operands must match numbers of inputs and outputs"
                    )
                self.n_inputs = left.n_inputs
                self.n_outputs = left.n_outputs
                self.inputs = left.inputs
                self.outputs = left.outputs
            elif op == "&":
                self.n_inputs = left.n_inputs + right.n_inputs
                self.n_outputs = left.n_outputs + right.n_outputs
                self.inputs = combine_labels(left.inputs, right.inputs)
                self.outputs = combine_labels(left.outputs, right.outputs)
            elif op == "|":
                if left.n_outputs != right.n_inputs:
                    raise ModelDefinitionError(
                        "Unsupported operands for |:"
                        f" {left.name} (n_inputs={left.n_inputs},"
                        f" n_outputs={left.n_outputs}) and"
                        f" {right.name} (n_inputs={right.n_inputs},"
                        f" n_outputs={right.n_outputs}); n_outputs for the left-hand model"
                        " must match n_inputs for the right-hand model."
                    )

                self.n_inputs = left.n_inputs
                self.n_outputs = right.n_outputs
                self.inputs = left.inputs
                self.outputs = right.outputs
            elif op == "fix_inputs":
                if not isinstance(left, Model):
                    raise ValueError(
                        'First argument to "fix_inputs" must be an instance of '
                        "an astropy Model."
                    )
                if not isinstance(right, dict):
                    raise ValueError(
                        'Expected a dictionary for second argument of "fix_inputs".'
                    )

                # Dict keys must match either possible indices
                # for model on left side, or names for inputs.
                self.n_inputs = left.n_inputs - len(right)
                # Assign directly to the private attribute (instead of using the setter)
                # to avoid asserting the new number of outputs matches the old one.
                self._outputs = left.outputs
                self.n_outputs = left.n_outputs
                newinputs = list(left.inputs)
                keys = right.keys()
                input_ind = []
                for key in keys:
                    if np.issubdtype(type(key), np.integer):
                        if key >= left.n_inputs or key < 0:
                            raise ValueError(
                                "Substitution key integer value "
                                "not among possible input choices."
                            )
                        if key in input_ind:
                            raise ValueError(
                                "Duplicate specification of same input (index/name)."
                            )
                        input_ind.append(key)
                    elif isinstance(key, str):
                        if key not in left.inputs:
                            raise ValueError(
                                "Substitution key string not among possible input choices."
                            )
                        # Check to see it doesn't match positional
                        # specification.
                        ind = left.inputs.index(key)
                        if ind in input_ind:
                            raise ValueError(
                                "Duplicate specification of same input (index/name)."
                            )
                        input_ind.append(ind)
                # Remove substituted inputs
                input_ind.sort()
                input_ind.reverse()
                for ind in input_ind:
                    del newinputs[ind]
                self.inputs = tuple(newinputs)
                # Now check to see if the input model has bounding_box defined.
                # If so, remove the appropriate dimensions and set it for this
                # instance.
                try:
                    self.bounding_box = self.left.bounding_box.fix_inputs(self, right)
                except NotImplementedError:
                    pass

            else:
                raise ModelDefinitionError("Illegal operator: ", self.op)
            self.name = name
            self._fittable = None
            if op in ("|", "+", "-"):
                self.linear = left.linear and right.linear
            else:
                self.linear = False
            self.eqcons = []
            self.ineqcons = []
            self.n_left_params = len(self.left.parameters)
            self._map_parameters()

            # Initialize the cache for the constraints (used primarily when
            # sync_constraints is False)
            self._constraints_cache = {}

        def _get_left_inputs_from_args(self, args):
            return args[: self.left.n_inputs]

        def _get_right_inputs_from_args(self, args):
            op = self.op
            if op == "&":
                # Args expected to look like (*left inputs, *right inputs, *left params, *right params)
                return args[self.left.n_inputs : self.left.n_inputs + self.right.n_inputs]
            elif op == "|" or op == "fix_inputs":
                return None
            else:
                return args[: self.left.n_inputs]

        def _get_left_params_from_args(self, args):
            op = self.op
            if op == "&":
                # Args expected to look like (*left inputs, *right inputs, *left params, *right params)
                n_inputs = self.left.n_inputs + self.right.n_inputs
                return args[n_inputs : n_inputs + self.n_left_params]
            else:
                return args[self.left.n_inputs : self.left.n_inputs + self.n_left_params]

        def _get_right_params_from_args(self, args):
            op = self.op
            if op == "fix_inputs":
                return None
            if op == "&":
                # Args expected to look like (*left inputs, *right inputs, *left params, *right params)
                return args[self.left.n_inputs + self.right.n_inputs + self.n_left_params :]
            else:
                return args[self.left.n_inputs + self.n_left_params :]

        def _get_kwarg_model_parameters_as_positional(self, args, kwargs):
            # could do it with inserts but rebuilding seems like simpilist way

            # TODO: Check if any param names are in kwargs maybe as an intersection of sets?
            if self.op == "&":
                new_args = list(args[: self.left.n_inputs + self.right.n_inputs])
                args_pos = self.left.n_inputs + self.right.n_inputs
            else:
                new_args = list(args[: self.left.n_inputs])
                args_pos = self.left.n_inputs

            for param_name in self.param_names:
                kw_value = kwargs.pop(param_name, None)
                if kw_value is not None:
                    value = kw_value
                else:
                    try:
                        value = args[args_pos]
                    except IndexError:
                        raise IndexError("Missing parameter or input")

                    args_pos += 1
                new_args.append(value)

            return new_args, kwargs

        def _apply_operators_to_value_lists(self, leftval, rightval, **kw):
            op = self.op
            if op == "+":
                return binary_operation(operator.add, leftval, rightval)
            elif op == "-":
                return binary_operation(operator.sub, leftval, rightval)
            elif op == "*":
                return binary_operation(operator.mul, leftval, rightval)
            elif op == "/":
                return binary_operation(operator.truediv, leftval, rightval)
            elif op == "**":
                return binary_operation(operator.pow, leftval, rightval)
            elif op == "&":
                if not isinstance(leftval, tuple):
                    leftval = (leftval,)
                if not isinstance(rightval, tuple):
                    rightval = (rightval,)
                return leftval + rightval
            elif op in SPECIAL_OPERATORS:
                return binary_operation(SPECIAL_OPERATORS[op], leftval, rightval)
            else:
                raise ModelDefinitionError("Unrecognized operator {op}")

        def evaluate(self, *args, **kw):
            op = self.op
            args, kw = self._get_kwarg_model_parameters_as_positional(args, kw)
            left_inputs = self._get_left_inputs_from_args(args)
            left_params = self._get_left_params_from_args(args)

            if op == "fix_inputs":
                pos_index = dict(zip(self.left.inputs, range(self.left.n_inputs)))
                fixed_inputs = {
                    key if np.issubdtype(type(key), np.integer) else pos_index[key]: value
                    for key, value in self.right.items()
                }
                left_inputs = [
                    fixed_inputs[ind] if ind in fixed_inputs.keys() else inp
                    for ind, inp in enumerate(left_inputs)
                ]

            leftval = self.left.evaluate(*left_inputs, *left_params)

            if op == "fix_inputs":
                return leftval

            right_inputs = self._get_right_inputs_from_args(args)
            right_params = self._get_right_params_from_args(args)

            if op == "|":
                if isinstance(leftval, tuple):
                    return self.right.evaluate(*leftval, *right_params)
                else:
                    return self.right.evaluate(leftval, *right_params)
            else:
                rightval = self.right.evaluate(*right_inputs, *right_params)

            return self._apply_operators_to_value_lists(leftval, rightval, **kw)

        @property
        def fit_deriv(self):
            # If either side of the model is missing analytical derivative then we can't compute one
            if self.left.fit_deriv is None or self.right.fit_deriv is None:
                return None

            # Only the following operators are supported
            op = self.op
            if op not in ["-", "+", "*", "/"]:
                return None

            def _calc_compound_deriv(*args, **kwargs):
                args, kw = self._get_kwarg_model_parameters_as_positional(args, kwargs)
                left_inputs = self._get_left_inputs_from_args(args)
                left_params = self._get_left_params_from_args(args)

                right_inputs = self._get_right_inputs_from_args(args)
                right_params = self._get_right_params_from_args(args)

                left_deriv = self.left.fit_deriv(*left_inputs, *left_params)
                right_deriv = self.right.fit_deriv(*right_inputs, *right_params)

                # Not all fit_deriv methods return consistent types, some return
                # single arrays, some return lists of arrays, etc. We now convert
                # this to a single array.
                left_deriv = np.asanyarray(left_deriv)
                right_deriv = np.asanyarray(right_deriv)

                if not self.left.col_fit_deriv:
                    left_deriv = np.moveaxis(left_deriv, -1, 0)

                if not self.right.col_fit_deriv:
                    right_deriv = np.moveaxis(right_deriv, -1, 0)

                # Some models preserve the shape of the input in the output of
                # fit_deriv whereas some do not. For example for a 6-parameter model,
                # passing input with shape (5, 3) might produce a deriv array with
                # shape (6, 5, 3) or (6, 15). We therefore normalize this to always
                # ravel all but the first dimension
                left_deriv = left_deriv.reshape((left_deriv.shape[0], -1))
                right_deriv = right_deriv.reshape((right_deriv.shape[0], -1))

                # Convert the arrays back to lists over the first dimension so as to
                # be able to concatenate them (we don't use .tolist() which would
                # convert to a list of lists instead of a list of arrays)
                left_deriv = list(left_deriv)
                right_deriv = list(right_deriv)

                # We now have to use various differentiation rules to apply the
                # arithmetic operators to the derivatives.
                # If we consider an example of a compound model
                # h(x, a, b, c) made up of two models g(x, a)
                # and h(x, b, c), one with one parameter and
                # the other with two parameters, the derivatives
                # are evaluated as follows:

                # Addition
                # h(x, a, b, c) = f(x, a) + g(x, b, c)
                # fit_deriv = [df/da, dg/db, dg/dc]

                # Subtraction
                # h(x, a, b, c) = f(x, a) - g(x, b, c)
                # fit_deriv = [df/da, -dg/db, -dg/dc]

                # Multiplication
                # h(x, a, b, c) = f(x, a) * g(x, b, c)
                # fit_deriv = [g(x, b, c) * df/da,
                #              f(x, a) * dg/db,
                #              f(x, a) * dg/dc]

                # Division - Quotient rule
                # h(x, a, b, c) = f(x, a) / g(x, b, c)
                # fit_deriv = [df/da / g(x, b, c),
                #              -f(x, a) * dg/db / g(x, b, c)**2,
                #              -f(x, a) * dg/dc / g(x, b, c)**2]

                if op in ["+", "-"]:
                    if op == "-":
                        right_deriv = [-x for x in right_deriv]

                    return np.array(left_deriv + right_deriv)

                leftval = self.left.evaluate(*left_inputs, *left_params).ravel()
                rightval = self.right.evaluate(*right_inputs, *right_params).ravel()

                if op == "*":
                    return np.array(
                        [rightval * dparam for dparam in left_deriv] +
                        [leftval * dparam for dparam in right_deriv]
                    )  # fmt: skip
                if op == "/":
                    return np.array(
                        [dparam / rightval for dparam in left_deriv] +
                        [-leftval * (dparam / rightval**2) for dparam in right_deriv]
                    )  # fmt: skip

            return _calc_compound_deriv

        @property
        def col_fit_deriv(self):
            return True

        @property
        def n_submodels(self):
            if self._leaflist is None:
                self._make_leaflist()
            return len(self._leaflist)

        @property
        def submodel_names(self):
            """Return the names of submodels in a ``CompoundModel``."""
            if self._leaflist is None:
                self._make_leaflist()
            names = [item.name for item in self._leaflist]
            nonecount = 0
            newnames = []
            for item in names:
                if item is None:
                    newnames.append(f"None_{nonecount}")
                    nonecount += 1
                else:
                    newnames.append(item)
            return tuple(newnames)

        def _pre_evaluate(self, *args, **kwargs):
            """
            CompoundModel specific input setup that needs to occur prior to
                model evaluation.

            Note
            ----
                All of the _pre_evaluate for each component model will be
                performed at the time that the individual model is evaluated.
            """
            # If equivalencies are provided, necessary to map parameters and pass
            # the leaflist as a keyword input for use by model evaluation so that
            # the compound model input names can be matched to the model input
            # names.
            if "equivalencies" in kwargs:
                # Restructure to be useful for the individual model lookup
                kwargs["inputs_map"] = [
                    (value[0], (value[1], key)) for key, value in self.inputs_map().items()
                ]

            # Setup actual model evaluation method
            def evaluate(_inputs):
                return self._evaluate(*_inputs, **kwargs)

            return evaluate, args, None, kwargs

        @property
        def _argnames(self):
            """
            No inputs should be used to determine input_shape when handling compound models.
            """
            return ()

        def _post_evaluate(self, inputs, outputs, broadcasted_shapes, with_bbox, **kwargs):
            """
            CompoundModel specific post evaluation processing of outputs.

            Note
            ----
                All of the _post_evaluate for each component model will be
                performed at the time that the individual model is evaluated.
            """
            if self.get_bounding_box(with_bbox) is not None and self.n_outputs == 1:
                return outputs[0]
            return outputs

        def _evaluate(self, *args, **kw):
            op = self.op
            if op != "fix_inputs":
                if op != "&":
                    leftval = self.left(*args, **kw)
                    if op != "|":
                        rightval = self.right(*args, **kw)
                    else:
                        rightval = None

                else:
                    leftval = self.left(*(args[: self.left.n_inputs]), **kw)
                    rightval = self.right(*(args[self.left.n_inputs :]), **kw)

                if op != "|":
                    return self._apply_operators_to_value_lists(leftval, rightval, **kw)

                elif op == "|":
                    if isinstance(leftval, tuple):
                        return self.right(*leftval, **kw)
                    else:
                        return self.right(leftval, **kw)

            else:
                subs = self.right
                newargs = list(args)
                subinds = []
                subvals = []
                for key in subs.keys():
                    if np.issubdtype(type(key), np.integer):
                        subinds.append(key)
                    elif isinstance(key, str):
                        ind = self.left.inputs.index(key)
                        subinds.append(ind)
                    subvals.append(subs[key])
                # Turn inputs specified in kw into positional indices.
                # Names for compound inputs do not propagate to sub models.
                kwind = []
                kwval = []
                for kwkey in list(kw.keys()):
                    if kwkey in self.inputs:
                        ind = self.inputs.index(kwkey)
                        if ind < len(args):
                            raise ValueError(
                                "Keyword argument duplicates positional value supplied."
                            )
                        kwind.append(ind)
                        kwval.append(kw[kwkey])
                        del kw[kwkey]
                # Build new argument list
                # Append keyword specified args first
                if kwind:
                    kwargs = list(zip(kwind, kwval))
                    kwargs.sort()
                    kwindsorted, kwvalsorted = list(zip(*kwargs))
                    newargs = newargs + list(kwvalsorted)
                if subinds:
                    subargs = list(zip(subinds, subvals))
                    subargs.sort()
                    # subindsorted, subvalsorted = list(zip(*subargs))
                    # The substitutions must be inserted in order
                    for ind, val in subargs:
                        newargs.insert(ind, val)
                return self.left(*newargs, **kw)

        @property
        def param_names(self):
            """An ordered list of parameter names."""
            return self._param_names

        def _make_leaflist(self):
            tdict = {}
            leaflist = []
            make_subtree_dict(self, "", tdict, leaflist)
            self._leaflist = leaflist
            self._tdict = tdict

        def __getattr__(self, name):
            """
            If someone accesses an attribute not already defined, map the
            parameters, and then see if the requested attribute is one of
            the parameters.
            """
            # The following test is needed to avoid infinite recursion
            # caused by deepcopy. There may be other such cases discovered.
            if name == "__setstate__":
                raise AttributeError
            if name in self._param_names:
                return self.__dict__[name]
            else:
                raise AttributeError(f'Attribute "{name}" not found')

        def __getitem__(self, index):
            if self._leaflist is None:
                self._make_leaflist()
            leaflist = self._leaflist
            tdict = self._tdict
            if isinstance(index, slice):
                if index.step:
                    raise ValueError("Steps in slices not supported for compound models")
                if index.start is not None:
                    if isinstance(index.start, str):
                        start = self._str_index_to_int(index.start)
                    else:
                        start = index.start
                else:
                    start = 0
                if index.stop is not None:
                    if isinstance(index.stop, str):
                        stop = self._str_index_to_int(index.stop)
                    else:
                        stop = index.stop - 1
                else:
                    stop = len(leaflist) - 1
                if index.stop == 0:
                    raise ValueError("Slice endpoint cannot be 0")
                if start < 0:
                    start = len(leaflist) + start
                if stop < 0:
                    stop = len(leaflist) + stop
                # now search for matching node:
                if stop == start:  # only single value, get leaf instead in code below
                    index = start
                else:
                    for key in tdict:
                        node, leftind, rightind = tdict[key]
                        if leftind == start and rightind == stop:
                            return node
                    raise IndexError("No appropriate subtree matches slice")
            if np.issubdtype(type(index), np.integer):
                return leaflist[index]
            elif isinstance(index, str):
                return leaflist[self._str_index_to_int(index)]
            else:
                raise TypeError("index must be integer, slice, or model name string")

        def _str_index_to_int(self, str_index):
            # Search through leaflist for item with that name
            found = []
            for nleaf, leaf in enumerate(self._leaflist):
                if getattr(leaf, "name", None) == str_index:
                    found.append(nleaf)
            if len(found) == 0:
                raise IndexError(f"No component with name '{str_index}' found")
            if len(found) > 1:
                raise IndexError(
                    f"Multiple components found using '{str_index}' as name\n"
                    f"at indices {found}"
                )
            return found[0]

        @property
        def n_inputs(self):
            """The number of inputs of a model."""
            return self._n_inputs

        @n_inputs.setter
        def n_inputs(self, value):
            self._n_inputs = value

        @property
        def n_outputs(self):
            """The number of outputs of a model."""
            return self._n_outputs

        @n_outputs.setter
        def n_outputs(self, value):
            self._n_outputs = value

        @property
        def eqcons(self):
            return self._eqcons

        @eqcons.setter
        def eqcons(self, value):
            self._eqcons = value

        @property
        def ineqcons(self):
            return self._eqcons

        @ineqcons.setter
        def ineqcons(self, value):
            self._eqcons = value

        def traverse_postorder(self, include_operator=False):
            """Postorder traversal of the CompoundModel tree."""
            res = []
            if isinstance(self.left, CompoundModel):
                res = res + self.left.traverse_postorder(include_operator)
            else:
                res = res + [self.left]
            if isinstance(self.right, CompoundModel):
                res = res + self.right.traverse_postorder(include_operator)
            else:
                res = res + [self.right]
            if include_operator:
                res.append(self.op)
            else:
                res.append(self)
            return res

        def _format_expression(self, format_leaf=None):
            leaf_idx = 0
            operands = deque()

            if format_leaf is None:
                format_leaf = lambda i, l: f"[{i}]"

            for node in self.traverse_postorder():
                if not isinstance(node, CompoundModel):
                    operands.append(format_leaf(leaf_idx, node))
                    leaf_idx += 1
                    continue

                right = operands.pop()
                left = operands.pop()
                if node.op in OPERATOR_PRECEDENCE:
                    oper_order = OPERATOR_PRECEDENCE[node.op]

                    if isinstance(node, CompoundModel):
                        if (
                            isinstance(node.left, CompoundModel)
                            and OPERATOR_PRECEDENCE[node.left.op] < oper_order
                        ):
                            left = f"({left})"
                        if (
                            isinstance(node.right, CompoundModel)
                            and OPERATOR_PRECEDENCE[node.right.op] < oper_order
                        ):
                            right = f"({right})"

                    operands.append(f"{left} {node.op} {right}")
                else:
                    left = f"(({left}),"
                    right = f"({right}))"
                    operands.append(" ".join((node.op[0], left, right)))

            return "".join(operands)

        def _format_components(self):
            if self._parameters_ is None:
                self._map_parameters()
            return "\n\n".join(f"[{idx}]: {m!r}" for idx, m in enumerate(self._leaflist))

        def __str__(self):
            expression = self._format_expression()
            components = self._format_components()
            keywords = [
                ("Expression", expression),
                ("Components", "\n" + indent(components, 4 * " ")),
            ]
            return super()._format_str(keywords=keywords)

        def rename(self, name):
            self.name = name
            return self

        @property
        def isleaf(self):
            return False

        @property
        def inverse(self):
            if self.op == "|":
                return self.right.inverse | self.left.inverse
            elif self.op == "&":
                return self.left.inverse & self.right.inverse
            else:
                return NotImplemented

        @property
        def fittable(self):
            """Set the fittable attribute on a compound model."""
            if self._fittable is None:
                if self._leaflist is None:
                    self._map_parameters()
                self._fittable = all(m.fittable for m in self._leaflist)
            return self._fittable

        __add__ = _model_oper("+")
        __sub__ = _model_oper("-")
        __mul__ = _model_oper("*")
        __truediv__ = _model_oper("/")
        __pow__ = _model_oper("**")
        __or__ = _model_oper("|")
        __and__ = _model_oper("&")

        def _map_parameters(self):
            """
            Map all the constituent model parameters to the compound object,
            renaming as necessary by appending a suffix number.

            This can be an expensive operation, particularly for a complex
            expression tree.

            All the corresponding parameter attributes are created that one
            expects for the Model class.

            The parameter objects that the attributes point to are the same
            objects as in the constiutent models. Changes made to parameter
            values to either are seen by both.

            Prior to calling this, none of the associated attributes will
            exist. This method must be called to make the model usable by
            fitting engines.

            If oldnames=True, then parameters are named as in the original
            implementation of compound models.
            """
            if self._parameters is not None:
                # do nothing
                return
            if self._leaflist is None:
                self._make_leaflist()
            self._parameters_ = {}
            param_map = {}
            self._param_names = []
            for lindex, leaf in enumerate(self._leaflist):
                if not isinstance(leaf, dict):
                    for param_name in leaf.param_names:
                        param = getattr(leaf, param_name)
                        new_param_name = f"{param_name}_{lindex}"
                        self.__dict__[new_param_name] = param
                        self._parameters_[new_param_name] = param
                        self._param_names.append(new_param_name)
                        param_map[new_param_name] = (lindex, param_name)
            self._param_metrics = defaultdict(dict)
            self._param_map = param_map
            self._param_map_inverse = {v: k for k, v in param_map.items()}
            self._initialize_slices()
            self._param_names = tuple(self._param_names)

        @staticmethod
        def _recursive_lookup(branch, adict, key):
            if isinstance(branch, CompoundModel):
                return adict[key]
            return branch, key

        def inputs_map(self):
            """
            Map the names of the inputs to this ExpressionTree to the inputs to the leaf models.
            """
            inputs_map = {}
            if not isinstance(
                self.op, str
            ):  # If we don't have an operator the mapping is trivial
                return {inp: (self, inp) for inp in self.inputs}

            elif self.op == "|":
                if isinstance(self.left, CompoundModel):
                    l_inputs_map = self.left.inputs_map()
                for inp in self.inputs:
                    if isinstance(self.left, CompoundModel):
                        inputs_map[inp] = l_inputs_map[inp]
                    else:
                        inputs_map[inp] = self.left, inp
            elif self.op == "&":
                if isinstance(self.left, CompoundModel):
                    l_inputs_map = self.left.inputs_map()
                if isinstance(self.right, CompoundModel):
                    r_inputs_map = self.right.inputs_map()
                for i, inp in enumerate(self.inputs):
                    if i < len(self.left.inputs):  # Get from left
                        if isinstance(self.left, CompoundModel):
                            inputs_map[inp] = l_inputs_map[self.left.inputs[i]]
                        else:
                            inputs_map[inp] = self.left, self.left.inputs[i]
                    else:  # Get from right
                        if isinstance(self.right, CompoundModel):
                            inputs_map[inp] = r_inputs_map[
                                self.right.inputs[i - len(self.left.inputs)]
                            ]
                        else:
                            inputs_map[inp] = (
                                self.right,
                                self.right.inputs[i - len(self.left.inputs)],
                            )
            elif self.op == "fix_inputs":
                fixed_ind = list(self.right.keys())
                ind = [
                    list(self.left.inputs).index(i) if isinstance(i, str) else i
                    for i in fixed_ind
                ]
                inp_ind = list(range(self.left.n_inputs))
                for i in ind:
                    inp_ind.remove(i)
                for i in inp_ind:
                    inputs_map[self.left.inputs[i]] = self.left, self.left.inputs[i]
            else:
                if isinstance(self.left, CompoundModel):
                    l_inputs_map = self.left.inputs_map()
                for inp in self.left.inputs:
                    if isinstance(self.left, CompoundModel):
                        inputs_map[inp] = l_inputs_map[inp]
                    else:
                        inputs_map[inp] = self.left, inp
            return inputs_map

        def _parameter_units_for_data_units(self, input_units, output_units):
            if self._leaflist is None:
                self._map_parameters()
            units_for_data = {}
            for imodel, model in enumerate(self._leaflist):
                units_for_data_leaf = model._parameter_units_for_data_units(
                    input_units, output_units
                )
                for param_leaf in units_for_data_leaf:
                    param = self._param_map_inverse[(imodel, param_leaf)]
                    units_for_data[param] = units_for_data_leaf[param_leaf]
            return units_for_data

        @property
        def input_units(self):
            inputs_map = self.inputs_map()
            input_units_dict = {
                key: inputs_map[key][0].input_units[orig_key]
                for key, (mod, orig_key) in inputs_map.items()
                if inputs_map[key][0].input_units is not None
            }
            if input_units_dict:
                return input_units_dict
            return None

        @property
        def input_units_equivalencies(self):
            inputs_map = self.inputs_map()
            input_units_equivalencies_dict = {
                key: inputs_map[key][0].input_units_equivalencies[orig_key]
                for key, (mod, orig_key) in inputs_map.items()
                if inputs_map[key][0].input_units_equivalencies is not None
            }
            if not input_units_equivalencies_dict:
                return None

            return input_units_equivalencies_dict

        @property
        def input_units_allow_dimensionless(self):
            inputs_map = self.inputs_map()
            return {
                key: inputs_map[key][0].input_units_allow_dimensionless[orig_key]
                for key, (mod, orig_key) in inputs_map.items()
            }

        @property
        def input_units_strict(self):
            inputs_map = self.inputs_map()
            return {
                key: inputs_map[key][0].input_units_strict[orig_key]
                for key, (mod, orig_key) in inputs_map.items()
            }

        @property
        def return_units(self):
            outputs_map = self.outputs_map()
            return {
                key: outputs_map[key][0].return_units[orig_key]
                for key, (mod, orig_key) in outputs_map.items()
                if outputs_map[key][0].return_units is not None
            }

        def outputs_map(self):
            """
            Map the names of the outputs to this ExpressionTree to the outputs to the leaf models.
            """
            outputs_map = {}
            if not isinstance(
                self.op, str
            ):  # If we don't have an operator the mapping is trivial
                return {out: (self, out) for out in self.outputs}

            elif self.op == "|":
                if isinstance(self.right, CompoundModel):
                    r_outputs_map = self.right.outputs_map()
                for out in self.outputs:
                    if isinstance(self.right, CompoundModel):
                        outputs_map[out] = r_outputs_map[out]
                    else:
                        outputs_map[out] = self.right, out

            elif self.op == "&":
                if isinstance(self.left, CompoundModel):
                    l_outputs_map = self.left.outputs_map()
                if isinstance(self.right, CompoundModel):
                    r_outputs_map = self.right.outputs_map()
                for i, out in enumerate(self.outputs):
                    if i < len(self.left.outputs):  # Get from left
                        if isinstance(self.left, CompoundModel):
                            outputs_map[out] = l_outputs_map[self.left.outputs[i]]
                        else:
                            outputs_map[out] = self.left, self.left.outputs[i]
                    else:  # Get from right
                        if isinstance(self.right, CompoundModel):
                            outputs_map[out] = r_outputs_map[
                                self.right.outputs[i - len(self.left.outputs)]
                            ]
                        else:
                            outputs_map[out] = (
                                self.right,
                                self.right.outputs[i - len(self.left.outputs)],
                            )
            elif self.op == "fix_inputs":
                return self.left.outputs_map()
            else:
                if isinstance(self.left, CompoundModel):
                    l_outputs_map = self.left.outputs_map()
                for out in self.left.outputs:
                    if isinstance(self.left, CompoundModel):
                        outputs_map[out] = l_outputs_map()[out]
                    else:
                        outputs_map[out] = self.left, out
            return outputs_map

        @property
        def has_user_bounding_box(self):
            """
            A flag indicating whether or not a custom bounding_box has been
            assigned to this model by a user, via assignment to
            ``model.bounding_box``.
            """
            return self._user_bounding_box is not None

        def render(self, out=None, coords=None):
            """
            Evaluate a model at fixed positions, respecting the ``bounding_box``.

            The key difference relative to evaluating the model directly
            is that this method is limited to a bounding box if the
            `~astropy.modeling.Model.bounding_box` attribute is set.

            Parameters
            ----------
            out : `numpy.ndarray`, optional
                An array that the evaluated model will be added to.  If this is not
                given (or given as ``None``), a new array will be created.
            coords : array-like, optional
                An array to be used to translate from the model's input coordinates
                to the ``out`` array. It should have the property that
                ``self(coords)`` yields the same shape as ``out``.  If ``out`` is
                not specified, ``coords`` will be used to determine the shape of
                the returned array. If this is not provided (or None), the model
                will be evaluated on a grid determined by
                `~astropy.modeling.Model.bounding_box`.

            Returns
            -------
            out : `numpy.ndarray`
                The model added to ``out`` if  ``out`` is not ``None``, or else a
                new array from evaluating the model over ``coords``.
                If ``out`` and ``coords`` are both `None`, the returned array is
                limited to the `~astropy.modeling.Model.bounding_box`
                limits. If `~astropy.modeling.Model.bounding_box` is `None`,
                ``arr`` or ``coords`` must be passed.

            Raises
            ------
            ValueError
                If ``coords`` are not given and the
                `~astropy.modeling.Model.bounding_box` of this model is not
                set.

            Examples
            --------
            :ref:`astropy:bounding-boxes`
            """
            bbox = self.get_bounding_box()

            ndim = self.n_inputs

            if (coords is None) and (out is None) and (bbox is None):
                raise ValueError("If no bounding_box is set, coords or out must be input.")

            # for consistent indexing
            if ndim == 1:
                if coords is not None:
                    coords = [coords]
                if bbox is not None:
                    bbox = [bbox]

            if coords is not None:
                coords = np.asanyarray(coords, dtype=float)
                # Check dimensions match out and model
                assert len(coords) == ndim
                if out is not None:
                    if coords[0].shape != out.shape:
                        raise ValueError("inconsistent shape of the output.")
                else:
                    out = np.zeros(coords[0].shape)

            if out is not None:
                out = np.asanyarray(out)
                if out.ndim != ndim:
                    raise ValueError(
                        "the array and model must have the same number of dimensions."
                    )

            if bbox is not None:
                # Assures position is at center pixel, important when using
                # add_array.
                pd = (
                    np.array([(np.mean(bb), np.ceil((bb[1] - bb[0]) / 2)) for bb in bbox])
                    .astype(int)
                    .T
                )
                pos, delta = pd

                if coords is not None:
                    sub_shape = tuple(delta * 2 + 1)
                    sub_coords = np.array(
                        [extract_array(c, sub_shape, pos) for c in coords]
                    )
                else:
                    limits = [slice(p - d, p + d + 1, 1) for p, d in pd.T]
                    sub_coords = np.mgrid[limits]

                sub_coords = sub_coords[::-1]

                if out is None:
                    out = self(*sub_coords)
                else:
                    try:
                        out = add_array(out, self(*sub_coords), pos)
                    except ValueError:
                        raise ValueError(
                            "The `bounding_box` is larger than the input out in "
                            "one or more dimensions. Set "
                            "`model.bounding_box = None`."
                        )
            else:
                if coords is None:
                    im_shape = out.shape
                    limits = [slice(i) for i in im_shape]
                    coords = np.mgrid[limits]

                coords = coords[::-1]

                out += self(*coords)

            return out

        def replace_submodel(self, name, model):
            """
            Construct a new `~astropy.modeling.CompoundModel` instance from an
            existing CompoundModel, replacing the named submodel with a new model.

            In order to ensure that inverses and names are kept/reconstructed, it's
            necessary to rebuild the CompoundModel from the replaced node all the
            way back to the base. The original CompoundModel is left untouched.

            Parameters
            ----------
            name : str
                name of submodel to be replaced
            model : `~astropy.modeling.Model`
                replacement model
            """
            submodels = [
                m for m in self.traverse_postorder() if getattr(m, "name", None) == name
            ]
            if submodels:
                if len(submodels) > 1:
                    raise ValueError(f"More than one submodel named {name}")

                old_model = submodels.pop()
                if len(old_model) != len(model):
                    raise ValueError(
                        "New and old models must have equal values for n_models"
                    )

                # Do this check first in order to raise a more helpful Exception,
                # although it would fail trying to construct the new CompoundModel
                if (
                    old_model.n_inputs != model.n_inputs
                    or old_model.n_outputs != model.n_outputs
                ):
                    raise ValueError(
                        "New model must match numbers of inputs and "
                        "outputs of existing model"
                    )

                tree = _get_submodel_path(self, name)
                while tree:
                    branch = self.copy()
                    for node in tree[:-1]:
                        branch = getattr(branch, node)
                    setattr(branch, tree[-1], model)
                    model = CompoundModel(
                        branch.op, branch.left, branch.right, name=branch.name
                    )
                    tree = tree[:-1]
                return model

            else:
                raise ValueError(f"No submodels found named {name}")

        def without_units_for_data(self, **kwargs):
            """
            See `~astropy.modeling.Model.without_units_for_data` for overview
            of this method.

            Notes
            -----
            This modifies the behavior of the base method to account for the
            case where the sub-models of a compound model have different output
            units. This is only valid for compound * and / compound models as
            in that case it is reasonable to mix the output units. It does this
            by modifying the output units of each sub model by using the output
            units of the other sub model so that we can apply the original function
            and get the desired result.

            Additional data has to be output in the mixed output unit case
            so that the units can be properly rebuilt by
            `~astropy.modeling.CompoundModel.with_units_from_data`.

            Outside the mixed output units, this method is identical to the
            base method.
            """
            if self.op in ["*", "/"]:
                inputs = {inp: kwargs[inp] for inp in self.inputs}

                left_units = self.left.output_units(**kwargs)
                right_units = self.right.output_units(**kwargs)

                if self.op == "*":
                    left_kwargs = {
                        out: kwargs[out] / right_units[out]
                        for out in self.left.outputs
                        if kwargs[out] is not None
                    }
                    right_kwargs = {
                        out: kwargs[out] / left_units[out]
                        for out in self.right.outputs
                        if kwargs[out] is not None
                    }
                else:
                    left_kwargs = {
                        out: kwargs[out] * right_units[out]
                        for out in self.left.outputs
                        if kwargs[out] is not None
                    }
                    right_kwargs = {
                        out: 1 / kwargs[out] * left_units[out]
                        for out in self.right.outputs
                        if kwargs[out] is not None
                    }

                left_kwargs.update(inputs.copy())
                right_kwargs.update(inputs.copy())

                left = self.left.without_units_for_data(**left_kwargs)
                if isinstance(left, tuple):
                    left_kwargs["_left_kwargs"] = left[1]
                    left_kwargs["_right_kwargs"] = left[2]
                    left = left[0]

                right = self.right.without_units_for_data(**right_kwargs)
                if isinstance(right, tuple):
                    right_kwargs["_left_kwargs"] = right[1]
                    right_kwargs["_right_kwargs"] = right[2]
                    right = right[0]

                model = CompoundModel(self.op, left, right, name=self.name)

                return model, left_kwargs, right_kwargs
            else:
                return super().without_units_for_data(**kwargs)

        def with_units_from_data(self, **kwargs):
            """
            See `~astropy.modeling.Model.with_units_from_data` for overview
            of this method.

            Notes
            -----
            This modifies the behavior of the base method to account for the
            case where the sub-models of a compound model have different output
            units. This is only valid for compound * and / compound models as
            in that case it is reasonable to mix the output units. In order to
            do this it requires some additional information output by
            `~astropy.modeling.CompoundModel.without_units_for_data` passed as
            keyword arguments under the keywords ``_left_kwargs`` and ``_right_kwargs``.

            Outside the mixed output units, this method is identical to the
            base method.
            """
            if self.op in ["*", "/"]:
                left_kwargs = kwargs.pop("_left_kwargs")
                right_kwargs = kwargs.pop("_right_kwargs")

                left = self.left.with_units_from_data(**left_kwargs)
                right = self.right.with_units_from_data(**right_kwargs)

                return CompoundModel(self.op, left, right, name=self.name)

            else:
                return super().with_units_from_data(**kwargs)

    def _get_submodel_path(model, name):
        """Find the route down a CompoundModel's tree to the model with the
        specified name (whether it's a leaf or not).
        """
        if getattr(model, "name", None) == name:
            return []
        try:
            return ["left"] + _get_submodel_path(model.left, name)
        except (AttributeError, TypeError):
            pass
        try:
            return ["right"] + _get_submodel_path(model.right, name)
        except (AttributeError, TypeError):
            pass

    def binary_operation(binoperator, left, right):
        """
        Perform binary operation. Operands may be matching tuples of operands.
        """
        if isinstance(left, tuple) and isinstance(right, tuple):
            return tuple(binoperator(item[0], item[1]) for item in zip(left, right))
        return binoperator(left, right)

    def get_ops(tree, opset):
        """
        Recursive function to collect operators used.
        """
        if isinstance(tree, CompoundModel):
            opset.add(tree.op)
            get_ops(tree.left, opset)
            get_ops(tree.right, opset)
        else:
            return

    def make_subtree_dict(tree, nodepath, tdict, leaflist):
        """Traverse a tree noting each node by a key.

        The key indicates all the left/right choices necessary to reach that node.
        Each key will reference a tuple that contains:

        - reference to the compound model for that node.
        - left most index contained within that subtree
           (relative to all indices for the whole tree)
        - right most index contained within that subtree
        """
        # if this is a leaf, just append it to the leaflist
        if not hasattr(tree, "isleaf"):
            leaflist.append(tree)
        else:
            leftmostind = len(leaflist)
            make_subtree_dict(tree.left, nodepath + "l", tdict, leaflist)
            make_subtree_dict(tree.right, nodepath + "r", tdict, leaflist)
            rightmostind = len(leaflist) - 1
            tdict[nodepath] = (tree, leftmostind, rightmostind)

    _ORDER_OF_OPERATORS = [("fix_inputs",), ("|",), ("&",), ("+", "-"), ("*", "/"), ("**",)]
    OPERATOR_PRECEDENCE = {}
    for idx, ops in enumerate(_ORDER_OF_OPERATORS):
        for op in ops:
            OPERATOR_PRECEDENCE[op] = idx
    del idx, op, ops

    def fix_inputs(modelinstance, values, bounding_boxes=None, selector_args=None):
        """
        This function creates a compound model with one or more of the input
        values of the input model assigned fixed values (scalar or array).

        Parameters
        ----------
        modelinstance : `~astropy.modeling.Model` instance
            This is the model that one or more of the
            model input values will be fixed to some constant value.
        values : dict
            A dictionary where the key identifies which input to fix
            and its value is the value to fix it at. The key may either be the
            name of the input or a number reflecting its order in the inputs.

        Examples
        --------
        >>> from astropy.modeling.models import Gaussian2D
        >>> g = Gaussian2D(1, 2, 3, 4, 5)
        >>> gv = fix_inputs(g, {0: 2.5})

        Results in a 1D function equivalent to Gaussian2D(1, 2, 3, 4, 5)(x=2.5, y)
        """
        model = CompoundModel("fix_inputs", modelinstance, values)
        if bounding_boxes is not None:
            if selector_args is None:
                selector_args = tuple((key, True) for key in values.keys())
            bbox = CompoundBoundingBox.validate(
                modelinstance, bounding_boxes, selector_args
            )
            _selector = bbox.selector_args.get_fixed_values(modelinstance, values)

            new_bbox = bbox[_selector]
            new_bbox = new_bbox.__class__.validate(model, new_bbox)

            model.bounding_box = new_bbox
        return model

    def bind_bounding_box(modelinstance, bounding_box, ignored=None, order="C"):
        """
        Set a validated bounding box to a model instance.

        Parameters
        ----------
        modelinstance : `~astropy.modeling.Model` instance
            This is the model that the validated bounding box will be set on.
        bounding_box : tuple
            A bounding box tuple, see :ref:`astropy:bounding-boxes` for details
        ignored : list
            List of the inputs to be ignored by the bounding box.
        order : str, optional
            The ordering of the bounding box tuple, can be either ``'C'`` or
            ``'F'``.
        """
        modelinstance.bounding_box = ModelBoundingBox.validate(
            modelinstance, bounding_box, ignored=ignored, order=order
        )

    def bind_compound_bounding_box(
        modelinstance,
        bounding_boxes,
        selector_args,
        create_selector=None,
        ignored=None,
        order="C",
    ):
        """
        Add a validated compound bounding box to a model instance.

        Parameters
        ----------
        modelinstance : `~astropy.modeling.Model` instance
            This is the model that the validated compound bounding box will be set on.
        bounding_boxes : dict
            A dictionary of bounding box tuples, see :ref:`astropy:bounding-boxes`
            for details.
        selector_args : list
            List of selector argument tuples to define selection for compound
            bounding box, see :ref:`astropy:bounding-boxes` for details.
        create_selector : callable, optional
            An optional callable with interface (selector_value, model) which
            can generate a bounding box based on a selector value and model if
            there is no bounding box in the compound bounding box listed under
            that selector value. Default is ``None``, meaning new bounding
            box entries will not be automatically generated.
        ignored : list
            List of the inputs to be ignored by the bounding box.
        order : str, optional
            The ordering of the bounding box tuple, can be either ``'C'`` or
            ``'F'``.
        """
        modelinstance.bounding_box = CompoundBoundingBox.validate(
            modelinstance,
            bounding_boxes,
            selector_args,
            create_selector=create_selector,
            ignored=ignored,
            order=order,
        )

    def custom_model(*args, fit_deriv=None):
        """
        Create a model from a user defined function. The inputs and parameters of
        the model will be inferred from the arguments of the function.

        This can be used either as a function or as a decorator.  See below for
        examples of both usages.

        The model is separable only if there is a single input.

        .. note::

            All model parameters have to be defined as keyword arguments with
            default values in the model function.  Use `None` as a default argument
            value if you do not want to have a default value for that parameter.

            The standard settable model properties can be configured by default
            using keyword arguments matching the name of the property; however,
            these values are not set as model "parameters". Moreover, users
            cannot use keyword arguments matching non-settable model properties,
            with the exception of ``n_outputs`` which should be set to the number of
            outputs of your function.

        Parameters
        ----------
        func : function
            Function which defines the model.  It should take N positional
            arguments where ``N`` is dimensions of the model (the number of
            independent variable in the model), and any number of keyword arguments
            (the parameters).  It must return the value of the model (typically as
            an array, but can also be a scalar for scalar inputs).  This
            corresponds to the `~astropy.modeling.Model.evaluate` method.
        fit_deriv : function, optional
            Function which defines the Jacobian derivative of the model. I.e., the
            derivative with respect to the *parameters* of the model.  It should
            have the same argument signature as ``func``, but should return a
            sequence where each element of the sequence is the derivative
            with respect to the corresponding argument. This corresponds to the
            :meth:`~astropy.modeling.FittableModel.fit_deriv` method.

        Examples
        --------
        Define a sinusoidal model function as a custom 1D model::

            >>> from astropy.modeling.models import custom_model
            >>> import numpy as np
            >>> def sine_model(x, amplitude=1., frequency=1.):
            ...     return amplitude * np.sin(2 * np.pi * frequency * x)
            >>> def sine_deriv(x, amplitude=1., frequency=1.):
            ...     return 2 * np.pi * amplitude * np.cos(2 * np.pi * frequency * x)
            >>> SineModel = custom_model(sine_model, fit_deriv=sine_deriv)

        Create an instance of the custom model and evaluate it::

            >>> model = SineModel()
            >>> model(0.25)  # doctest: +FLOAT_CMP
            1.0

        This model instance can now be used like a usual astropy model.

        The next example demonstrates a 2D Moffat function model, and also
        demonstrates the support for docstrings (this example could also include
        a derivative, but it has been omitted for simplicity)::

            >>> @custom_model
            ... def Moffat2D(x, y, amplitude=1.0, x_0=0.0, y_0=0.0, gamma=1.0,
            ...            alpha=1.0):
            ...     \"\"\"Two dimensional Moffat function.\"\"\"
            ...     rr_gg = ((x - x_0) ** 2 + (y - y_0) ** 2) / gamma ** 2
            ...     return amplitude * (1 + rr_gg) ** (-alpha)
            ...
            >>> print(Moffat2D.__doc__)
            Two dimensional Moffat function.
            >>> model = Moffat2D()
            >>> model(1, 1)  # doctest: +FLOAT_CMP
            0.3333333333333333
        """
        if len(args) == 1 and callable(args[0]):
            return _custom_model_wrapper(args[0], fit_deriv=fit_deriv)
        elif not args:
            return functools.partial(_custom_model_wrapper, fit_deriv=fit_deriv)
        else:
            raise TypeError(
                f"{__name__} takes at most one positional argument (the callable/"
                "function to be turned into a model.  When used as a decorator "
                "it should be passed keyword arguments only (if "
                "any)."
            )

    def _custom_model_inputs(func):
        """
        Processes the inputs to the `~astropy.modeling.custom_model`'s
        function into the appropriate categories.

        Parameters
        ----------
        func : callable

        Returns
        -------
        inputs : list
            list of evaluation inputs
        special_params : dict
            dictionary of model properties which require special treatment
        settable_params : dict
            dictionary of defaults for settable model properties
        params : dict
            dictionary of model parameters set by
            `~astropyl.modeling.custom_model`'s function
        """
        inputs, parameters = get_inputs_and_params(func)

        special = ["n_outputs"]
        settable = [
            attr
            for attr, value in vars(Model).items()
            if isinstance(value, property) and value.fset is not None
        ]
        properties = [
            attr
            for attr, value in vars(Model).items()
            if isinstance(value, property) and value.fset is None and attr not in special
        ]

        special_params = {}
        settable_params = {}
        params = {}
        for param in parameters:
            if param.name in special:
                special_params[param.name] = param.default
            elif param.name in settable:
                settable_params[param.name] = param.default
            elif param.name in properties:
                raise ValueError(
                    f"Parameter '{param.name}' cannot be a model property: {properties}."
                )
            else:
                params[param.name] = param.default

        return inputs, special_params, settable_params, params

    def _custom_model_wrapper(func, fit_deriv=None):
        """
        Internal implementation `~astropy.modeling.custom_model`.

        When `~astropy.modeling.custom_model` is called as a function its
        arguments are passed to this function, and the result of this
        function is returned.

        When `~astropy.modeling.custom_model` is used as a decorator
        a partial evaluation of this function is returned by
        `~astropy.modeling.custom_model`.
        """
        if not callable(func):
            raise ModelDefinitionError(
                "func is not callable; it must be a function or other callable object"
            )

        if fit_deriv is not None and not callable(fit_deriv):
            raise ModelDefinitionError(
                "fit_deriv not callable; it must be a function or other callable object"
            )

        model_name = func.__name__

        inputs, special_params, settable_params, params = _custom_model_inputs(func)

        if fit_deriv is not None and len(fit_deriv.__defaults__) != len(params):
            raise ModelDefinitionError(
                "derivative function should accept same number of parameters as func."
            )

        params = {
            param: Parameter(param, default=default) for param, default in params.items()
        }

        mod = find_current_module(2)
        if mod:
            modname = mod.__name__
        else:
            modname = "__main__"

        members = {
            "__module__": str(modname),
            "__doc__": func.__doc__,
            "n_inputs": len(inputs),
            "n_outputs": special_params.pop("n_outputs", 1),
            "evaluate": staticmethod(func),
            "_settable_properties": settable_params,
        }

        if fit_deriv is not None:
            members["fit_deriv"] = staticmethod(fit_deriv)

        members.update(params)

        cls = type(model_name, (FittableModel,), members)
        cls._separable = len(inputs) == 1
        return cls

    def render_model(model, arr=None, coords=None):
        """
        Evaluates a model on an input array. Evaluation is limited to a
        bounding box if the `~astropy.modeling.Model.bounding_box` attribute
        is set.

        Parameters
        ----------
        model : `~astropy.modeling.Model`
            Model to be evaluated.
        arr : `numpy.ndarray`, optional
            Array on which the model is evaluated.
        coords : array-like, optional
            Coordinate arrays mapping to ``arr``, such that
            ``arr[coords] == arr``.

        Returns
        -------
        array : `numpy.ndarray`
            The model evaluated on the input ``arr`` or a new array from
            ``coords``.
            If ``arr`` and ``coords`` are both `None`, the returned array is
            limited to the `~astropy.modeling.Model.bounding_box` limits.
            If `~astropy.modeling.Model.bounding_box` is `None`, ``arr`` or
            ``coords`` must be passed.

        Examples
        --------
        :ref:`astropy:bounding-boxes`
        """
        bbox = model.bounding_box

        if (coords is None) & (arr is None) & (bbox is None):
            raise ValueError("If no bounding_box is set, coords or arr must be input.")

        # for consistent indexing
        if model.n_inputs == 1:
            if coords is not None:
                coords = [coords]
            if bbox is not None:
                bbox = [bbox]

        if arr is not None:
            arr = arr.copy()
            # Check dimensions match model
            if arr.ndim != model.n_inputs:
                raise ValueError(
                    "number of array dimensions inconsistent with number of model inputs."
                )
        if coords is not None:
            # Check dimensions match arr and model
            coords = np.array(coords)
            if len(coords) != model.n_inputs:
                raise ValueError(
                    "coordinate length inconsistent with the number of model inputs."
                )
            if arr is not None:
                if coords[0].shape != arr.shape:
                    raise ValueError("coordinate shape inconsistent with the array shape.")
            else:
                arr = np.zeros(coords[0].shape)

        if bbox is not None:
            # assures position is at center pixel, important when using add_array
            pd = pos, delta = (
                np.array([(np.mean(bb), np.ceil((bb[1] - bb[0]) / 2)) for bb in bbox])
                .astype(int)
                .T
            )

            if coords is not None:
                sub_shape = tuple(delta * 2 + 1)
                sub_coords = np.array([extract_array(c, sub_shape, pos) for c in coords])
            else:
                limits = [slice(p - d, p + d + 1, 1) for p, d in pd.T]
                sub_coords = np.mgrid[limits]

            sub_coords = sub_coords[::-1]

            if arr is None:
                arr = model(*sub_coords)
            else:
                try:
                    arr = add_array(arr, model(*sub_coords), pos)
                except ValueError:
                    raise ValueError(
                        "The `bounding_box` is larger than the input"
                        " arr in one or more dimensions. Set "
                        "`model.bounding_box = None`."
                    )
        else:
            if coords is None:
                im_shape = arr.shape
                limits = [slice(i) for i in im_shape]
                coords = np.mgrid[limits]

            arr += model(*coords[::-1])

        return arr

    def hide_inverse(model):
        """
        This is a convenience function intended to disable automatic generation
        of the inverse in compound models by disabling one of the constituent
        model's inverse. This is to handle cases where user provided inverse
        functions are not compatible within an expression.

        For example::

            compound_model.inverse = hide_inverse(m1) + m2 + m3

        This will insure that the defined inverse itself won't attempt to
        build its own inverse, which would otherwise fail in this example
        (e.g., m = m1 + m2 + m3 happens to raises an exception for this
        reason.)

        Note that this permanently disables it. To prevent that either copy
        the model or restore the inverse later.
        """
        del model.inverse
        return model