# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Keras SavedModel serialization.

TODO (kathywu): Move to layer_serialization.py. Some model-specific logic should
go to model_serialization.py.
"""

import functools
import threading
import weakref

import tensorflow.compat.v1.logging as logging
import tensorflow.compat.v2 as tf

from keras import backend
from keras.engine import base_layer_utils
from keras.engine import input_spec
from keras.mixed_precision import autocast_variable
from keras.saving.legacy import saving_utils
from keras.saving.legacy.saved_model import constants
from keras.saving.legacy.saved_model import load as keras_load
from keras.saving.legacy.saved_model import serialized_attributes
from keras.saving.legacy.saved_model import utils
from keras.utils import layer_utils
from keras.utils import tf_contextlib
from keras.utils import tf_utils
from keras.utils import version_utils
from keras.utils.generic_utils import LazyLoader

# To avoid circular dependencies between keras/engine and keras/saving,
# code in keras/saving must delay imports.

# TODO(b/134426265): Switch back to single-quotes to match the rest of the file
# once the issue with copybara is fixed.

base_layer = LazyLoader("base_layer", globals(), "keras.engine.base_layer")
metrics = LazyLoader("metrics", globals(), "keras.metrics")
input_layer = LazyLoader("input_layer", globals(), "keras.engine.input_layer")
training_lib = LazyLoader("training_lib", globals(), "keras.engine.training")
sequential_lib = LazyLoader(
    "sequential_lib", globals(), "keras.engine.sequential"
)


def should_skip_serialization(layer):
    """Skip serializing extra objects and functions if layer inputs aren't
    set."""
    saved_model_input_spec_set = (
        isinstance(layer, training_lib.Model)
        and layer._saved_model_inputs_spec is not None
    )
    if not layer.built and not saved_model_input_spec_set:
        logging.warning(
            "Skipping full serialization of Keras layer {}, because "
            "it is not built.".format(layer)
        )
        return True
    return False


def _filter_shards(variables):
    return [var for var in variables if not hasattr(var, "_sharded_container")]


def wrap_layer_objects(layer, serialization_cache):
    """Returns extra trackable objects to attach to the serialized layer.

    Args:
      layer: Keras Layer object.
      serialization_cache: Dictionary shared between all objects during
        serialization.

    Returns:
      A dictionary containing all checkpointable objects from a
      SerializedAttributes object. See LayerAttributes and ModelAttributes for
      entire list of objects
    """
    # Wrap all regularization losses as tf.functions.
    # First, generate list of all regularization losses in this layer and
    # sublayers.
    all_losses = layer._callable_losses[:]
    for child_layer in utils.list_all_layers(layer):
        all_losses.extend(child_layer._callable_losses)
    # Next, wrap all loss functions as tf.functions. Use the serialization cache
    # to store already-wrapped functions.
    keras_loss_cache = serialization_cache.setdefault("keras_losses", {})
    wrapped_loss_functions = []
    for loss_fn in all_losses:
        if loss_fn in keras_loss_cache:
            wrapped_loss_functions.append(keras_loss_cache[loss_fn])
        else:
            wrapped_loss = _wrap_unconditional_loss(
                loss_fn, len(keras_loss_cache)
            )
            keras_loss_cache[loss_fn] = wrapped_loss
            wrapped_loss_functions.append(wrapped_loss)
    wrapped_layer_losses = [
        keras_loss_cache[fn] for fn in layer._callable_losses[:]
    ]

    layer_metrics = tf.__internal__.tracking.wrap(
        {m.name: m for m in layer._metrics}
    )

    # Avoid duplicate creation of shard Variables on loading.
    # `layer.variables` will return the shard Variables rather than the
    # ShardedVariables (b/224541446), but Keras loading will create new
    # ShardedVariables (and thus shard Variables) from Keras metadata if needed.
    # There's no need to also save the shard Variables here, so filter them out.
    variables = _filter_shards(layer.variables)
    trainable_variables = _filter_shards(layer.trainable_variables)
    non_trainable_variables = _filter_shards(layer.non_trainable_variables)
    return dict(
        variables=tf.__internal__.tracking.wrap(variables),
        trainable_variables=tf.__internal__.tracking.wrap(trainable_variables),
        non_trainable_variables=tf.__internal__.tracking.wrap(
            non_trainable_variables
        ),
        layers=tf.__internal__.tracking.wrap(utils.list_all_layers(layer)),
        metrics=tf.__internal__.tracking.wrap(layer.metrics),
        regularization_losses=tf.__internal__.tracking.wrap(
            wrapped_loss_functions
        ),
        layer_regularization_losses=tf.__internal__.tracking.wrap(
            wrapped_layer_losses
        ),
        layer_metrics=layer_metrics,
    )


def wrap_layer_functions(layer, serialization_cache):
    """Returns dict of wrapped layer call function and losses in tf.functions.

    Args:
      layer: Keras Layer object.
      serialization_cache: Dictionary shared between all objects during
        serialization.

    Returns:
      A dictionary containing all keras tf.functions to serialize. See
      LayerAttributes and ModelAttributes for the list of all attributes.
    """
    # Since Sequential models may be modified in place using model.add() or
    # model.pop(), don't use saved functions.
    if isinstance(layer, keras_load.RevivedLayer) and not isinstance(
        layer, sequential_lib.Sequential
    ):
        return {
            fn_name: getattr(layer.keras_api, fn_name, None)
            for fn_name in serialized_attributes.LayerAttributes.all_functions
        }

    # Reset the losses of the layer and its children. The call function in each
    # child layer is replaced with tf.functions.
    original_fns = _replace_child_layer_functions(layer, serialization_cache)
    original_losses = _reset_layer_losses(layer)

    # Wrap all the layer call and activity regularizer functions.

    # Use LayerCallCollection to ensure that all layer call functions (__call__,
    # call with losses) are traced with the same inputs.
    call_collection = LayerCallCollection(layer)
    call_fn_with_losses = call_collection.add_function(
        _wrap_call_and_conditional_losses(layer),
        f"{layer.name}_layer_call_and_return_conditional_losses",
        # If any of this layer's child layers use the training arg, the traced
        # call functions of this layer will have a training keyword argument. If
        # the original layer does not expect the training arg, then it will have
        # to be removed (by setting `match_layer_training_arg`).
        match_layer_training_arg=True,
    )
    call_fn = call_collection.add_function(
        _extract_outputs_from_fn(layer, call_fn_with_losses),
        f"{layer.name}_layer_call_fn",
        # Since `call_fn` wraps call_fn_with_losses and not the original call
        # function, `match_layer_training_arg` should be set to False.
        match_layer_training_arg=False,
    )

    fns = {
        "call_and_return_conditional_losses": call_fn_with_losses,
        "__call__": call_fn,
    }

    if layer._activity_regularizer is not None:
        fns["activity_regularizer_fn"] = _wrap_activity_regularizer(layer)
        fns[
            "call_and_return_all_conditional_losses"
        ] = call_collection.add_function(
            _append_activity_regularizer_loss(
                layer, call_fn_with_losses, fns["activity_regularizer_fn"]
            ),
            f"{layer.name}_layer_call_and_return_all_conditional_losses",
            match_layer_training_arg=False,
        )
    else:
        fns["activity_regularizer_fn"] = None
        fns["call_and_return_all_conditional_losses"] = call_fn_with_losses

    # Manually trigger traces before restoring the overwritten functions. The
    # functions are traced within the layer call context to ensure that layer
    # functions (e.g. add_loss) behave as though running in graph mode.
    with tracing_scope():
        call_collection.trace_with_input_signature()
        with base_layer_utils.call_context().enter(
            layer, inputs=None, build_graph=True, training=None, saving=True
        ):
            for fn in fns.values():
                if fn is not None and not isinstance(fn, LayerCall):
                    fn.get_concrete_function()

    # Restore overwritten functions and losses
    _restore_child_layer_functions(original_fns)
    _restore_layer_losses(original_losses)

    return fns


def default_save_signature(layer):
    original_losses = _reset_layer_losses(layer)
    fn = saving_utils.trace_model_call(layer)
    _restore_layer_losses(original_losses)
    return fn


def _replace_child_layer_functions(layer, serialization_cache):
    """Replaces functions in the children layers with wrapped tf.functions.

    This step allows functions from parent layers to reference the wrapped
    functions from their children layers instead of retracing the ops.

    This function also resets all losses stored in the layer. These are stored
    in the returned dictionary. Use `_restore_child_layer_functions` to restore
    the original attributes.

    Args:
      layer: Keras Layer object.
      serialization_cache: Dictionary shared between all objects during
        serialization.

    Returns:
      Dictionary mapping layer objects -> original functions and losses:
        { Child layer 1: {
            'losses': Original losses,
            'call': Original call function
            '_activity_regularizer': Original activity regularizer},
          Child layer 2: ...
        }
    """

    original_fns = {}

    def replace_layer_functions(child_layer, serialized_fns):
        """Replaces layer call and activity regularizer with wrapped
        functions."""
        original_fns[child_layer] = {
            "call": child_layer.call,
            "_activity_regularizer": child_layer._activity_regularizer,
        }
        with utils.no_automatic_dependency_tracking_scope(child_layer):
            try:
                child_layer._activity_regularizer = serialized_fns.get(
                    "activity_regularizer_fn"
                )
            except AttributeError:
                # Some layers have an unsettable activity regularizer.
                pass
            child_layer.call = utils.use_wrapped_call(
                child_layer,
                serialized_fns["call_and_return_conditional_losses"],
                child_layer._call_spec,
                default_training_value=False,
            )

    def replace_metric_functions(child_layer, serialized_fns):
        """Replaces metric functions with wrapped functions."""
        original_fns[child_layer] = {
            "__call__": child_layer.__call__,
            "result": child_layer.result,
            "update_state": child_layer.update_state,
        }
        with utils.no_automatic_dependency_tracking_scope(child_layer):
            child_layer.__call__ = serialized_fns["__call__"]
            child_layer.result = serialized_fns["result"]
            child_layer.update_state = serialized_fns["update_state"]

    for child_layer in utils.list_all_layers(layer):
        if isinstance(child_layer, input_layer.InputLayer):
            continue

        if child_layer not in serialization_cache[constants.KERAS_CACHE_KEY]:
            serialized_functions = child_layer._trackable_saved_model_saver._get_serialized_attributes(  # noqa: E501
                serialization_cache
            ).functions
        else:
            serialized_functions = serialization_cache[
                constants.KERAS_CACHE_KEY
            ][child_layer].functions
        if not serialized_functions:
            # This indicates either:
            #   - circular dependency, which means the current layer's functions
            #     should be wrapped first.
            #   - Child layer's inputs are not defined, so its functions have
            #     not been wrapped. In this case, no replacement is necessary so
            #     move on to the next child.
            continue

        if isinstance(child_layer, metrics.Metric):
            replace_metric_functions(child_layer, serialized_functions)
        else:
            replace_layer_functions(child_layer, serialized_functions)

    return original_fns


def _restore_child_layer_functions(original_fns):
    """Restores attributes replaced with `_replace_child_layer_functions`."""
    for child_layer, fns in original_fns.items():
        with utils.no_automatic_dependency_tracking_scope(child_layer):
            for fn_name, fn in fns.items():
                try:
                    setattr(child_layer, fn_name, fn)
                except AttributeError:
                    # In the case of _activity_regularizer, setting the
                    # attribute may be disallowed.
                    pass


def _reset_layer_losses(parent_layer):
    """Resets losses of layer and its sublayers, and returns original losses."""
    losses_dict = {}
    for layer in utils.list_all_layers_and_sublayers(parent_layer):
        losses_dict[layer] = {
            "losses": layer._losses[:],
            "eager_losses": layer._eager_losses[:],
        }
        with utils.no_automatic_dependency_tracking_scope(layer):
            layer._losses = []
            layer._eager_losses = []
    return losses_dict


def _restore_layer_losses(losses_dict):
    for layer in losses_dict:
        with utils.no_automatic_dependency_tracking_scope(layer):
            layer._losses = losses_dict[layer]["losses"]
            layer._eager_losses = losses_dict[layer]["eager_losses"]


class LayerTracingContext(threading.local):
    def __init__(self):
        super().__init__()
        self.enable_call_tracing = False
        self.trace_queue = []


_thread_local_data = LayerTracingContext()


@tf_contextlib.contextmanager
def tracing_scope():
    """Enables tracing scope."""
    # This enables the LayerCallCollection's tracing mechanism to trace all call
    # functions in the collection.
    previous_value = _thread_local_data.enable_call_tracing
    previous_queue = _thread_local_data.trace_queue
    try:
        _thread_local_data.enable_call_tracing = True
        _thread_local_data.trace_queue = []
        yield
    finally:
        # Run traces from the queue.
        while _thread_local_data.trace_queue:
            fn, args, kwargs, training = _thread_local_data.trace_queue.pop(0)
            if training is not None:
                with backend.deprecated_internal_learning_phase_scope(training):
                    fn.get_concrete_function(*args, **kwargs)
            else:
                fn.get_concrete_function(*args, **kwargs)
        _thread_local_data.trace_queue = previous_queue
        _thread_local_data.enable_call_tracing = previous_value


def add_trace_to_queue(fn, args, kwargs, training=None):
    if tracing_enabled():
        _thread_local_data.trace_queue.append(
            (fn, args[:], kwargs.copy(), training)
        )


def tracing_enabled():
    """Whether to add extra traces to the queue."""
    return _thread_local_data.enable_call_tracing


class LayerCallCollection:
    """Groups wrapped layer call functions.

    This is used to ensure that all layer call functions are traced with the
    same inputs-
      - call
      - call_and_return_conditional_losses
      - call_and_return_all_conditional_losses
    """

    def __init__(self, layer):
        self.layer = layer

        self.layer_call_method = _get_layer_call_method(layer)
        self._expects_training_arg = utils.layer_uses_training_bool(layer)
        self._call_spec = layer._call_spec

        # Create new call spec if the layer itself does not accept a training
        # arg, but one of its child layers does. When this layer's call
        # functions are traced, they will be traced with an added `training`
        # keyword argument.
        if not self.layer._expects_training_arg and self._expects_training_arg:
            arg_spec = utils.set_training_arg_spec(
                self._call_spec.full_argspec, False
            )
            self._call_spec = layer_utils.CallFunctionSpec(arg_spec)

        self._layer_inputs = self._get_layer_inputs(layer)
        self._functions = weakref.WeakValueDictionary()

        # Get the input argument name from the args.
        if self._call_spec.arg_names:
            self._input_arg_name = self._call_spec.arg_names[0]
        else:
            # Layer could be defined with only varargs, in which case use a
            # default name.
            self._input_arg_name = "inputs"

    def _get_layer_inputs(self, layer):
        """Inspects layer object and returns the inferred input signature.

        Args:
          layer: Layer object.

        Returns:
          List of possibly nested TensorSpecs of the layer call function inputs
          in the form of `(args, kwargs)`
        """
        if (
            isinstance(layer.call, tf.__internal__.function.Function)
            and layer.call.input_signature is not None
        ):
            return layer.call.input_signature, {}
        elif isinstance(layer, training_lib.Model):
            return saving_utils.model_call_inputs(layer)
        elif (
            layer.input_spec is not None
            and layer._use_input_spec_as_call_signature
        ):

            def to_tensor_spec_or_none(x):
                spec = input_spec.to_tensor_spec(x, layer._compute_dtype)
                # If the shape is too general (e.g. multiple dimensions are
                # allowed), return None so that separate functions can be
                # generated for each inferred input signature.
                # TODO(b/134962016): currently partial signatures are not
                # supported.
                if spec.shape == tf.TensorShape(None):
                    return None, None
                return spec

            input_signature = [
                tf.nest.map_structure(to_tensor_spec_or_none, layer.input_spec)
            ]

            return input_signature, {}
        else:
            return None, None

    def add_trace(self, *args, **kwargs):
        """Traces all functions with the same args and kwargs.

        Args:
          *args: Positional args passed to the original function.
          **kwargs: Keyword args passed to the original function.
        """
        args = list(args)
        kwargs = kwargs.copy()

        for fn in self._functions.values():
            # TODO(kathywu): Replace arguments with broader shapes defined in
            # the input signature.
            if self._expects_training_arg:

                def trace_with_training(value, fn=fn):
                    nonlocal args, kwargs
                    (args, kwargs,) = self._call_spec.set_arg_value(
                        "training", value, args, kwargs, inputs_in_args=True
                    )
                    add_trace_to_queue(fn, args, kwargs, value)

                trace_with_training(True)
                trace_with_training(False)
            else:
                add_trace_to_queue(fn, args, kwargs)

    def training_arg_was_passed(self, args, kwargs):
        return self._call_spec.arg_was_passed(
            "training", args, kwargs, inputs_in_args=True
        )

    def get_training_arg_value(self, args, kwargs):
        try:
            return self._call_spec.get_arg_value(
                "training", args, kwargs, inputs_in_args=True
            )
        except KeyError:  # Training is not in args or kwargs.
            return None

    def get_input_arg_value(self, args, kwargs):
        return self._call_spec.get_arg_value(
            self._input_arg_name, args, kwargs, inputs_in_args=True
        )

    def _maybe_wrap_with_training_arg(self, call_fn, match_layer_training_arg):
        """Wraps call function with added training argument if necessary."""
        if not self.layer._expects_training_arg and self._expects_training_arg:
            # Add training arg to wrapper function.
            def wrap_with_training_arg(*args, **kwargs):
                if match_layer_training_arg:
                    # Remove the training value, since the original call_fn does
                    # not expect a training arg. Instead, the training value
                    # will be propagated using the call context created in
                    # LayerCall.
                    args = list(args)
                    kwargs = kwargs.copy()
                    (args, kwargs,) = self._call_spec.set_arg_value(
                        "training",
                        None,
                        args,
                        kwargs,
                        inputs_in_args=True,
                        pop_kwarg_if_none=True,
                    )
                return call_fn(*args, **kwargs)

            return tf.__internal__.decorator.make_decorator(
                target=call_fn,
                decorator_func=wrap_with_training_arg,
                decorator_argspec=self._call_spec.full_argspec,
            )

        return call_fn

    def add_function(self, call_fn, name, match_layer_training_arg):
        """Adds a layer call function to the collection.

        Args:
          call_fn: a python function
          name: Name of call function
          match_layer_training_arg: If True, removes the `training` from the
            function arguments when calling `call_fn`.

        Returns:
          LayerCall (tf.function)
        """
        fn = LayerCall(
            self,
            self._maybe_wrap_with_training_arg(
                call_fn, match_layer_training_arg
            ),
            name,
        )
        self._functions[name] = fn.wrapped_call
        return fn

    def trace_with_input_signature(self):
        """Trace with the layer/models inferred input signature if possible."""
        if self._layer_inputs[0] is None:
            return

        args, kwargs = self._layer_inputs
        if self._expects_training_arg:
            args, kwargs = self._call_spec.set_arg_value(
                "training", False, args, kwargs, inputs_in_args=True
            )
        if None not in tf.nest.flatten([args, kwargs]):
            # Manually add traces for layers that have keyword arguments and
            # have a fully defined input signature.
            self.add_trace(*args, **kwargs)


def _filtered_inputs(inputs):
    return list(filter(tf_utils.is_tensor_or_variable, tf.nest.flatten(inputs)))


def layer_call_wrapper(call_collection, method, name):
    """Ensures layer losses are kept the same, and runs method in call
    context."""

    # Create wrapper that deals with losses and call context.
    def wrapper(*args, **kwargs):
        """Calls method within call context."""
        layer = call_collection.layer
        training = None
        inputs = _filtered_inputs([args, kwargs])

        if (args or kwargs) and call_collection.training_arg_was_passed(
            args, kwargs
        ):
            training = call_collection.get_training_arg_value(args, kwargs)

        original_losses = _reset_layer_losses(layer)
        with base_layer_utils.call_context().enter(
            layer,
            inputs=inputs,
            build_graph=False,
            training=training,
            saving=True,
        ):
            with autocast_variable.enable_auto_cast_variables(
                layer._compute_dtype_object
            ):
                ret = method(*args, **kwargs)
        _restore_layer_losses(original_losses)
        return ret

    # Rename to `name`, since tf.function doesn't have a name argument. Without
    # this, all functions returned by this method will be named "call", which
    # would be a nightmare to debug.
    fn = tf.__internal__.decorator.make_decorator(
        target=method, decorator_func=wrapper
    )
    fn.__name__ = name
    return fn


class LayerCall:
    """Function that triggers traces of other functions in the same
    collection."""

    def __init__(self, call_collection, call_fn, name):
        """Initializes a LayerCall object.

        Args:
          call_collection: a LayerCallCollection, which contains the other layer
            call functions (e.g. call_with_conditional_losses, call). These
            functions should be traced with the same arguments.
          call_fn: A call function.
          name: Name of the call function.
        """
        self.call_collection = call_collection
        self.wrapped_call = tf.function(
            layer_call_wrapper(call_collection, call_fn, name)
        )

    def _maybe_trace(self, args, kwargs):
        # Trigger traces of other call functions + extra training-arg traces.
        if tracing_enabled():
            self.call_collection.add_trace(*args, **kwargs)

    def __call__(self, *args, **kwargs):
        self._maybe_trace(args, kwargs)
        return self.wrapped_call(*args, **kwargs)

    def get_concrete_function(self, *args, **kwargs):
        self._maybe_trace(args, kwargs)
        return self.wrapped_call.get_concrete_function(*args, **kwargs)


def _wrap_call_and_conditional_losses(layer):
    """Wraps call function that returns a tuple of (outputs, losses).

    The losses returned are conditional on the inputs passed to the call
    function.  Unconditional losses (e.g. weight regularizeration) are wrapped
    separately.

    Args:
      layer: a Keras layer object

    Returns:
      python call function that returns outputs and conditional losses --
      excludes activity regularizer
    """
    # Create function that generates both outputs and losses
    layer_call = _get_layer_call_method(layer)

    def call_and_return_conditional_losses(*args, **kwargs):
        """Returns layer (call_output, conditional losses) tuple."""
        call_output = layer_call(*args, **kwargs)
        if version_utils.is_v1_layer_or_model(layer):
            conditional_losses = layer.get_losses_for(
                _filtered_inputs([args, kwargs])
            )
        else:
            conditional_losses = [
                l for l in layer.losses if not hasattr(l, "_unconditional_loss")
            ]
        return call_output, conditional_losses

    return _create_call_fn_decorator(layer, call_and_return_conditional_losses)


def _extract_outputs_from_fn(layer, call_and_return_conditional_losses):
    """Returns a function that returns only call function outputs."""
    if isinstance(layer, keras_load.RevivedLayer):
        return layer.keras_api.__call__

    def call(inputs, *args, **kwargs):
        return call_and_return_conditional_losses(inputs, *args, **kwargs)[0]

    return _create_call_fn_decorator(layer, call)


def _append_activity_regularizer_loss(
    layer, call_fn_with_losses, activity_regularizer_fn
):
    """Appends activity regularizer loss to losses returned by the wrapped
    fn."""

    def fn(inputs, *args, **kwargs):
        outputs, losses = call_fn_with_losses(inputs, *args, **kwargs)
        losses.append(activity_regularizer_fn(outputs))
        return outputs, losses

    return _create_call_fn_decorator(layer, fn)


def _create_call_fn_decorator(layer, wrapped_call):
    call_fn = _get_layer_call_method(layer)
    fn, arg_spec = utils.maybe_add_training_arg(
        layer._call_spec,
        wrapped_call,
        layer._expects_training_arg,
        default_training_value=False,
    )
    return tf.__internal__.decorator.make_decorator(
        target=call_fn, decorator_func=fn, decorator_argspec=arg_spec
    )


def _wrap_unconditional_loss(loss_fn, index):
    """Wraps callable/unconditional loss, returning a serializable function."""
    # Extract original loss function from partial function
    fn = loss_fn.args[0] if isinstance(loss_fn, functools.partial) else loss_fn
    if isinstance(fn, tf.__internal__.function.Function):
        return fn
    else:
        return tf.__internal__.function.Function(
            fn, f"loss_fn_{index}", input_signature=[]
        )


def _wrap_activity_regularizer(layer):
    """Wraps the activity regularizer."""

    if isinstance(
        layer._activity_regularizer, tf.__internal__.function.Function
    ):
        return layer._activity_regularizer
    return tf.__internal__.function.Function(
        layer._activity_regularizer,
        f"{layer.name}_activity_regularizer",
        input_signature=[
            tf.TensorSpec(None, layer._compute_dtype or backend.floatx())
        ],
    )


def _get_layer_call_method(layer):
    if isinstance(layer.call, (tf.__internal__.function.Function)):
        return layer.call.python_function
    return layer.call