text: # Copyright 2015 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.
  # ==============================================================================
  # pylint: disable=protected-access
  """Contains the base Layer class, from which all layers inherit."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import functools
  import inspect  # Necessary supplement to tf_inspect to deal with variadic args.
  import itertools
  
  import numpy as np
  from six.moves import zip  # pylint: disable=redefined-builtin
  
  from tensorflow.core.framework import node_def_pb2
  from tensorflow.python import autograph
  from tensorflow.python.distribute import values as distribute_values
  from tensorflow.python.eager import context
  from tensorflow.python.eager import execute
  from tensorflow.python.eager import function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import func_graph
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import constraints
  from tensorflow.python.keras import initializers
  from tensorflow.python.keras import regularizers
  from tensorflow.python.keras.engine import base_layer_utils
  from tensorflow.python.keras.engine import input_spec
  from tensorflow.python.keras.mixed_precision.experimental import autocast_variable
  from tensorflow.python.keras.mixed_precision.experimental import policy
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.keras.utils import tf_utils
  # A module that only depends on `keras.layers` import these from here.
  from tensorflow.python.keras.utils.generic_utils import to_snake_case  # pylint: disable=unused-import
  from tensorflow.python.keras.utils.tf_utils import is_tensor_or_tensor_list  # pylint: disable=unused-import
  from tensorflow.python.module import module
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variables as tf_variables
  from tensorflow.python.training.tracking import base as trackable
  from tensorflow.python.training.tracking import data_structures
  from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils
  from tensorflow.python.training.tracking import object_identity
  from tensorflow.python.training.tracking import tracking
  from tensorflow.python.util import compat
  from tensorflow.python.util import function_utils
  from tensorflow.python.util import nest
  from tensorflow.python.util import tf_decorator
  from tensorflow.python.util import tf_inspect
  from tensorflow.python.util.tf_export import keras_export
  from tensorflow.tools.docs import doc_controls
  
  
  @keras_export('keras.layers.Layer')
  class Layer(module.Module):
    """Base layer class.
  
    This is the class from which all layers inherit.
  
    A layer is a class implementing common neural networks operations, such
    as convolution, batch norm, etc. These operations require managing weights,
    losses, updates, and inter-layer connectivity.
  
    Users will just instantiate a layer and then treat it as a callable.
  
    We recommend that descendants of `Layer` implement the following methods:
  
    * `__init__()`: Save configuration in member variables
    * `build()`: Called once from `__call__`, when we know the shapes of inputs
      and `dtype`. Should have the calls to `add_weight()`, and then
      call the super's `build()` (which sets `self.built = True`, which is
      nice in case the user wants to call `build()` manually before the
      first `__call__`).
    * `call()`: Called in `__call__` after making sure `build()` has been called
      once. Should actually perform the logic of applying the layer to the
      input tensors (which should be passed in as the first argument).
  
    Arguments:
      trainable: Boolean, whether the layer's variables should be trainable.
      name: String name of the layer.
      dtype: Default dtype of the layer's weights (default of `None` means use the
        type of the first input).
      dynamic: Set this to `True` if your layer should only be run eagerly, and
        should not be used to generate a static computation graph.
        This would be the case for a Tree-RNN or a recursive network,
        for example, or generally for any layer that manipulates tensors
        using Python control flow. If `False`, we assume that the layer can
        safely be used to generate a static computation graph.
  
    Read-only properties:
      name: The name of the layer (string).
      dtype: Default dtype of the layer's weights (default of `None` means use the
        type of the first input).
      updates: List of update ops of this layer.
      losses: List of losses added by this layer.
      trainable_weights: List of variables to be included in backprop.
      non_trainable_weights: List of variables that should not be
        included in backprop.
      weights: The concatenation of the lists trainable_weights and
        non_trainable_weights (in this order).
  
    Mutable properties:
      trainable: Whether the layer should be trained (boolean).
      input_spec: Optional (list of) `InputSpec` object(s) specifying the
        constraints on inputs that can be accepted by the layer.
    """
  
    # See tf.Module for the usage of this property.
    # The key for _obj_reference_counts_dict is a Trackable, which could be a
    # variable or layer etc. tf.Module._flatten will fail to flatten the key
    # since it is trying to convert Trackable to a string. This attribute can be
    # ignored even after the fix of nest lib, since the trackable object should
    # already been available as individual attributes. _obj_reference_counts_dict
    # just contains a copy of them.
    _TF_MODULE_IGNORED_PROPERTIES = frozenset(itertools.chain(
        ('_obj_reference_counts_dict',),
        module.Module._TF_MODULE_IGNORED_PROPERTIES
    ))
  
    @trackable.no_automatic_dependency_tracking
    def __init__(self, trainable=True, name=None, dtype=None, dynamic=False,
                 **kwargs):
      # These properties should be set by the user via keyword arguments.
      # note that 'dtype', 'input_shape' and 'batch_input_shape'
      # are only applicable to input layers: do not pass these keywords
      # to non-input layers.
      allowed_kwargs = {
          'input_shape',
          'batch_input_shape',
          'batch_size',
          'weights',
          'activity_regularizer',
      }
      # Validate optional keyword arguments.
      for kwarg in kwargs:
        if kwarg not in allowed_kwargs:
          raise TypeError('Keyword argument not understood:', kwarg)
  
      # Mutable properties
      # Indicates whether the layer's weights are updated during training
      # and whether the layer's updates are run during training
      self.trainable = trainable
      # A stateful layer is a layer whose updates are run during inference too,
      # for instance stateful RNNs.
      self.stateful = False
      # Indicates whether `build` needs to be called upon layer call, to create
      # the layer's weights.
      self.built = False
      # Provides information about which inputs are compatible with the layer.
      self.input_spec = None
      self.supports_masking = False
  
      self._init_set_name(name)
      self._activity_regularizer = kwargs.pop('activity_regularizer', None)
      self._maybe_create_attribute('_trainable_weights', [])
      self._maybe_create_attribute('_non_trainable_weights', [])
      self._updates = []
      # A list of zero-argument lambdas which return Tensors, used for variable
      # regularizers.
      self._callable_losses = []
      # A list of symbolic Tensors containing activity regularizers and losses
      # manually added through `add_loss` in graph-building mode.
      self._losses = []
      # A list of loss values containing activity regularizers and losses
      # manually added through `add_loss` during eager execution. It is cleared
      # after every batch.
      # Because we plan on eventually allowing a same model instance to be trained
      # in eager mode or graph mode alternatively, we need to keep track of
      # eager losses and symbolic losses via separate attributes.
      self._eager_losses = []
      # A list of metric instances corresponding to the symbolic metric tensors
      # added using the `add_metric` API.
      self._metrics = []
      # TODO(psv): Remove this property.
      # A dictionary that maps metric names to metric result tensors. The results
      # are the running averages of metric values over an epoch.
      self._metrics_tensors = {}
  
      self._set_dtype_and_policy(dtype)
  
      self._call_fn_args = function_utils.fn_args(self.call)
      self._compute_previous_mask = ('mask' in self._call_fn_args or
                                     hasattr(self, 'compute_mask'))
      self._call_convention = (base_layer_utils
                               .CallConvention.EXPLICIT_INPUTS_ARGUMENT)
      # Dependencies tracked via attribute assignment.
      self._maybe_create_attribute('_layers', [])
  
      # These lists will be filled via successive calls
      # to self._add_inbound_node().
      self._inbound_nodes = []
      self._outbound_nodes = []
  
      call_argspec = tf_inspect.getfullargspec(self.call)
      self._expects_training_arg = 'training' in call_argspec.args
  
      # Whether the `call` method can be used to build a TF graph without issues.
      self._dynamic = dynamic
  
      # Manage input shape information if passed.
      if 'input_shape' in kwargs or 'batch_input_shape' in kwargs:
        # In this case we will later create an input layer
        # to insert before the current layer
        if 'batch_input_shape' in kwargs:
          batch_input_shape = tuple(kwargs['batch_input_shape'])
        elif 'input_shape' in kwargs:
          if 'batch_size' in kwargs:
            batch_size = kwargs['batch_size']
          else:
            batch_size = None
          batch_input_shape = (batch_size,) + tuple(kwargs['input_shape'])
        self._batch_input_shape = batch_input_shape
  
      # Manage initial weight values if passed.
      if 'weights' in kwargs:
        self._initial_weights = kwargs['weights']
      else:
        self._initial_weights = None
  
    def build(self, input_shape):
      """Creates the variables of the layer (optional, for subclass implementers).
  
      This is a method that implementers of subclasses of `Layer` or `Model`
      can override if they need a state-creation step in-between
      layer instantiation and layer call.
  
      This is typically used to create the weights of `Layer` subclasses.
  
      Arguments:
        input_shape: Instance of `TensorShape`, or list of instances of
          `TensorShape` if the layer expects a list of inputs
          (one instance per input).
      """
      self.built = True
  
    @doc_controls.for_subclass_implementers
    def call(self, inputs, **kwargs):  # pylint: disable=unused-argument
      """This is where the layer's logic lives.
  
      Arguments:
          inputs: Input tensor, or list/tuple of input tensors.
          **kwargs: Additional keyword arguments.
  
      Returns:
          A tensor or list/tuple of tensors.
      """
      return inputs
  
    @doc_controls.for_subclass_implementers
    def add_weight(self,
                   name=None,
                   shape=None,
                   dtype=None,
                   initializer=None,
                   regularizer=None,
                   trainable=None,
                   constraint=None,
                   partitioner=None,
                   use_resource=None,
                   synchronization=tf_variables.VariableSynchronization.AUTO,
                   aggregation=tf_variables.VariableAggregation.NONE,
                   **kwargs):
      """Adds a new variable to the layer.
  
      Arguments:
        name: Variable name.
        shape: Variable shape. Defaults to scalar if unspecified.
        dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
        initializer: initializer instance (callable).
        regularizer: regularizer instance (callable).
        trainable: whether the variable should be part of the layer's
          "trainable_variables" (e.g. variables, biases)
          or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
          Note, if the current variable scope is marked as non-trainable
          then this parameter is ignored and any added variables are also
          marked as non-trainable. `trainable` defaults to `True` unless
          `synchronization` is set to `ON_READ`.
        constraint: constraint instance (callable).
        partitioner: Partitioner to be passed to the `Trackable` API.
        use_resource: Whether to use `ResourceVariable`.
        synchronization: Indicates when a distributed a variable will be
          aggregated. Accepted values are constants defined in the class
          `tf.VariableSynchronization`. By default the synchronization is set to
          `AUTO` and the current `DistributionStrategy` chooses
          when to synchronize. If `synchronization` is set to `ON_READ`,
          `trainable` must not be set to `True`.
        aggregation: Indicates how a distributed variable will be aggregated.
          Accepted values are constants defined in the class
          `tf.VariableAggregation`.
        **kwargs: Additional keyword arguments. Accepted values are `getter` and
          `collections`.
  
      Returns:
        The created variable.  Usually either a `Variable` or `ResourceVariable`
        instance.  If `partitioner` is not `None`, a `PartitionedVariable`
        instance is returned.
  
      Raises:
        RuntimeError: If called with partioned variable regularization and
          eager execution is enabled.
        ValueError: When giving unsupported dtype and no initializer or when
          trainable has been set to True with synchronization set as `ON_READ`.
      """
      if shape is None:
        shape = ()
      # Validate optional keyword arguments.
      for kwarg in kwargs:
        if kwarg not in ['getter', 'collections', 'experimental_autocast']:
          raise TypeError('Unknown keyword argument:', kwarg)
      getter = kwargs.pop('getter', None)
      collections = kwargs.pop('collections', None)
      # 'experimental_autocast' can be set to False by the caller to indicate an
      # AutoCastVariable should never be created.
      autocast = kwargs.pop('experimental_autocast', True)
  
      if dtype is None:
        dtype = self.dtype or backend.floatx()
      dtype = dtypes.as_dtype(dtype)
      if self._dtype is None:
        self._dtype = dtype.base_dtype.name
      initializer = initializers.get(initializer)
      regularizer = regularizers.get(regularizer)
      constraint = constraints.get(constraint)
  
      if synchronization == tf_variables.VariableSynchronization.ON_READ:
        if trainable:
          raise ValueError(
              'Synchronization value can be set to '
              'VariableSynchronization.ON_READ only for non-trainable variables. '
              'You have specified trainable=True and '
              'synchronization=VariableSynchronization.ON_READ.')
        else:
          # Set trainable to be false when variable is to be synced on read.
          trainable = False
      elif trainable is None:
        trainable = True
  
      # Initialize variable when no initializer provided
      if initializer is None:
        # If dtype is DT_FLOAT, provide a uniform unit scaling initializer
        if dtype.is_floating:
          initializer = initializers.glorot_uniform()
        # If dtype is DT_INT/DT_UINT, provide a default value `zero`
        # If dtype is DT_BOOL, provide a default value `FALSE`
        elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
          initializer = initializers.zeros()
        # NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
        else:
          raise ValueError('An initializer for variable %s of type %s is required'
                           ' for layer %s' % (name, dtype.base_dtype, self.name))
  
      variable = self._add_variable_with_custom_getter(
          name=name,
          shape=shape,
          # TODO(allenl): a `make_variable` equivalent should be added as a
          # `Trackable` method.
          getter=getter or base_layer_utils.make_variable,
          # Manage errors in Layer rather than Trackable.
          overwrite=True,
          initializer=initializer,
          dtype=dtype,
          constraint=constraint,
          trainable=trainable and self.trainable,
          partitioner=partitioner,
          use_resource=use_resource,
          collections=collections,
          synchronization=synchronization,
          aggregation=aggregation)
      backend.track_variable(variable)
  
      if autocast and self._mixed_precision_policy.should_cast_variables:
        if isinstance(variable, distribute_values.DistributedVariable):
          variable = autocast_variable.AutoCastDistributedVariable(variable)
        else:
          variable = autocast_variable.AutoCastVariable(variable)
  
      if regularizer is not None:
        # TODO(fchollet): in the future, this should be handled at the
        # level of variable creation, and weight regularization losses
        # should be variable attributes.
        name_in_scope = variable.name[:variable.name.find(':')]
        self._handle_weight_regularization(name_in_scope,
                                           variable,
                                           regularizer)
      if trainable:
        self._trainable_weights.append(variable)
      else:
        self._non_trainable_weights.append(variable)
      return variable
  
    def get_config(self):
      """Returns the config of the layer.
  
      A layer config is a Python dictionary (serializable)
      containing the configuration of a layer.
      The same layer can be reinstantiated later
      (without its trained weights) from this configuration.
  
      The config of a layer does not include connectivity
      information, nor the layer class name. These are handled
      by `Network` (one layer of abstraction above).
  
      Returns:
          Python dictionary.
      """
      config = {'name': self.name, 'trainable': self.trainable}
      if hasattr(self, '_batch_input_shape'):
        config['batch_input_shape'] = self._batch_input_shape
      if hasattr(self, 'dtype'):
        config['dtype'] = self.dtype
      # TODO(reedwm): Handle serializing self._mixed_precision_policy.
      return config
  
    @classmethod
    def from_config(cls, config):
      """Creates a layer from its config.
  
      This method is the reverse of `get_config`,
      capable of instantiating the same layer from the config
      dictionary. It does not handle layer connectivity
      (handled by Network), nor weights (handled by `set_weights`).
  
      Arguments:
          config: A Python dictionary, typically the
              output of get_config.
  
      Returns:
          A layer instance.
      """
      return cls(**config)
  
    def compute_output_shape(self, input_shape):
      """Computes the output shape of the layer.
  
      Assumes that the layer will be built
      to match that input shape provided.
  
      Arguments:
          input_shape: Shape tuple (tuple of integers)
              or list of shape tuples (one per output tensor of the layer).
              Shape tuples can include None for free dimensions,
              instead of an integer.
  
      Returns:
          An input shape tuple.
      """
      if context.executing_eagerly():
        # In this case we build the model first in order to do shape inference.
        # This is acceptable because the framework only calls
        # `compute_output_shape` on shape values that the layer would later be
        # built for. It would however cause issues in case a user attempts to
        # use `compute_output_shape` manually (these users will have to
        # implement `compute_output_shape` themselves).
        self.build(input_shape)
        with context.graph_mode():
          graph = func_graph.FuncGraph('graph')
          with graph.as_default():
            input_shape = tf_utils.convert_shapes(input_shape, to_tuples=False)
            inputs = nest.map_structure(
                base_layer_utils.generate_placeholders_from_shape, input_shape)
            try:
              if self._expects_training_arg:
                outputs = self(inputs, training=False)
              else:
                outputs = self(inputs)
            except TypeError:
              raise NotImplementedError('We could not automatically infer '
                                        'the static shape of the layer\'s output.'
                                        ' Please implement the '
                                        '`compute_output_shape` method on your '
                                        'layer (%s).' % self.__class__.__name__)
        return nest.map_structure(lambda t: t.shape, outputs)
      raise NotImplementedError
  
    def compute_mask(self, inputs, mask=None):  # pylint: disable=unused-argument
      """Computes an output mask tensor.
  
      Arguments:
          inputs: Tensor or list of tensors.
          mask: Tensor or list of tensors.
  
      Returns:
          None or a tensor (or list of tensors,
              one per output tensor of the layer).
      """
      if not self.supports_masking:
        if any(m is not None for m in nest.flatten(mask)):
          raise TypeError('Layer ' + self.name + ' does not support masking, '
                          'but was passed an input_mask: ' + str(mask))
        # masking not explicitly supported: return None as mask.
        return None
      # if masking is explicitly supported, by default
      # carry over the input mask
      return mask
  
    def __call__(self, inputs, *args, **kwargs):
      """Wraps `call`, applying pre- and post-processing steps.
  
      Arguments:
        inputs: input tensor(s).
        *args: additional positional arguments to be passed to `self.call`.
        **kwargs: additional keyword arguments to be passed to `self.call`.
  
      Returns:
        Output tensor(s).
  
      Note:
        - The following optional keyword arguments are reserved for specific uses:
          * `training`: Boolean scalar tensor of Python boolean indicating
            whether the `call` is meant for training or inference.
          * `mask`: Boolean input mask.
        - If the layer's `call` method takes a `mask` argument (as some Keras
          layers do), its default value will be set to the mask generated
          for `inputs` by the previous layer (if `input` did come from
          a layer that generated a corresponding mask, i.e. if it came from
          a Keras layer with masking support.
  
      Raises:
        ValueError: if the layer's `call` method returns None (an invalid value).
      """
      input_list = nest.flatten(inputs)
      # Accept NumPy inputs by converting to Tensors.
      if any(isinstance(x, (np.ndarray, float, int)) for x in input_list):
        # Don't call `ops.convert_to_tensor` on all `inputs` because
        # `SparseTensors` can't be converted to `Tensor`.
        def _convert_non_tensor(x):
          if isinstance(x, (np.ndarray, float, int)):
            return ops.convert_to_tensor(x)
          return x
  
        inputs = nest.map_structure(_convert_non_tensor, inputs)
        input_list = nest.flatten(inputs)
  
      # We will attempt to build a TF graph if & only if all inputs are symbolic.
      # This is always the case in graph mode. It can also be the case in eager
      # mode when all inputs can be traced back to `keras.Input()` (when building
      # models using the functional API).
      build_graph = tf_utils.are_all_symbolic_tensors(input_list)
  
      if build_graph:
        # Only create Keras history if at least one tensor originates from a
        # `keras.Input`. Otherwise this Layer may be being used outside the Keras
        # framework.
        if base_layer_utils.needs_keras_history(inputs):
          base_layer_utils.create_keras_history(inputs)
  
      # Handle Keras mask propagation from previous layer to current layer.
      previous_mask = None
      if (not hasattr(self, '_compute_previous_mask') or
          self._compute_previous_mask):
        previous_mask = base_layer_utils.collect_previous_mask(inputs)
        if not hasattr(self, '_call_fn_args'):
          self._call_fn_args = function_utils.fn_args(self.call)
        if ('mask' in self._call_fn_args and 'mask' not in kwargs and
            not generic_utils.is_all_none(previous_mask)):
          # The previous layer generated a mask, and mask was not explicitly
          # pass to __call__, hence we set previous_mask as the default value.
          kwargs['mask'] = previous_mask
  
      # Clear eager losses on top level model call.
      # We are clearing the losses only on the top level model call and not on
      # every layer/mode call because layer/model may be reused.
      if (context.executing_eagerly() and
          not base_layer_utils.is_in_call_context()):
        self._clear_losses()
  
      with base_layer_utils.call_context(self):
        # Check input assumptions set after layer building, e.g. input shape.
        if build_graph:
          # Symbolic execution on symbolic tensors. We will attempt to build
          # the corresponding TF subgraph inside `backend.get_graph()`
          input_spec.assert_input_compatibility(self.input_spec, inputs,
                                                self.name)
          graph = backend.get_graph()
          with graph.as_default(), ops.name_scope(self._name_scope()):
            # Build layer if applicable (if the `build` method has been
            # overridden).
            self._maybe_build(inputs)
  
            # Wrapping `call` function in autograph to allow for dynamic control
            # dependencies in call. We are limiting this to subclassed layers as
            # autograph is strictly needed only for subclassed layers.
            if base_layer_utils.is_subclassed(self):
              decorators, original_func = tf_decorator.unwrap(self.call)
              # TODO(psv): Remove optional_features param from the call here
              # after b/129001876 is fixed.
              converted_func = autograph.convert(
                  recursive=True, optional_features=None)(
                      original_func)
              if decorators:
                call_fn = tf_decorator.rewrap(self.call, original_func,
                                              converted_func)
              else:
                call_fn = converted_func
            else:
              call_fn = self.call
  
            # Explicitly pass the learning phase placeholder to `call` if
            # the `training` argument was left unspecified by the user.
            # This behavior is restricted to the managed Keras FuncGraph.
            # TODO(omalleyt): Reconcile this with new `trainable` behavior
            # when available.
            learning_phase_passed_by_framework = False
            if (self._expects_training_arg and
                not base_layer_utils.training_arg_passed_to_call(
                    tf_inspect.getfullargspec(self.call), args, kwargs) and
                getattr(graph, 'name', None) == 'keras_graph'):
              learning_phase_passed_by_framework = True
              kwargs['training'] = backend.learning_phase()
            if not self.dynamic:
              try:
                with base_layer_utils.autocast_context_manager(
                    input_list,
                    self._mixed_precision_policy.should_cast_variables), (
                        base_layer_utils.AutoAddUpdates(self,
                                                        inputs)) as auto_updater:
                  outputs = call_fn(inputs, *args, **kwargs)
                  auto_updater.set_outputs(outputs)
  
              except TypeError as e:
                exception_str = str(e)
                exception_msg = 'Tensor objects are only iterable when eager'
                if exception_msg in exception_str:
                  raise TypeError('You are attempting to use Python control '
                                  'flow in a layer that was not declared to be '
                                  'dynamic. Pass `dynamic=True` to the class '
                                  'constructor.\nEncountered error:\n"""\n' +
                                  exception_str + '\n"""')
                raise
            else:
              # We will use static shape inference to return symbolic tensors
              # matching the specifications of the layer outputs.
              # Since `self.dynamic` is True, we will never attempt to
              # run the underlying TF graph (which is disconnected).
              # TODO(fchollet): consider py_func as an alternative, which
              # would enable us to run the underlying graph if needed.
              outputs = self._symbolic_call(inputs)
  
            if outputs is None:
              raise ValueError('A layer\'s `call` method should return a '
                               'Tensor or a list of Tensors, not None '
                               '(layer: ' + self.name + ').')
            if base_layer_utils.have_all_keras_metadata(inputs):
              if learning_phase_passed_by_framework:
                kwargs.pop('training')
              inputs, outputs = self._set_connectivity_metadata_(
                  inputs, outputs, args, kwargs)
            self._handle_activity_regularization(inputs, outputs)
            self._set_mask_metadata(inputs, outputs, previous_mask)
            if hasattr(self, '_set_inputs') and not self.inputs:
              # Subclassed network: explicitly set metadata normally set by
              # a call to self._set_inputs().
              # TODO(b/120997007): This should be done in Eager as well, but
              # causes garbage collection issues because of the placeholders
              # created on the default Keras graph.
              self._set_inputs(inputs, outputs)
        else:
          # Eager execution on data tensors.
          with ops.name_scope(self._name_scope()):
            self._maybe_build(inputs)
            with base_layer_utils.autocast_context_manager(
                input_list, self._mixed_precision_policy.should_cast_variables):
              outputs = self.call(inputs, *args, **kwargs)
            self._handle_activity_regularization(inputs, outputs)
            self._set_mask_metadata(inputs, outputs, previous_mask)
  
      if not context.executing_eagerly():
        # Optionally load weight values specified at layer instantiation.
        # TODO(fchollet): consider enabling this with eager execution too.
        if (hasattr(self, '_initial_weights') and
            self._initial_weights is not None):
          self.set_weights(self._initial_weights)
          del self._initial_weights
  
      return outputs
  
    @property
    def dtype(self):
      return self._dtype
  
    @property
    def name(self):
      return self._name
  
    @property
    def dynamic(self):
      return self._dynamic
  
    @property
    def activity_regularizer(self):
      """Optional regularizer function for the output of this layer."""
      return self._activity_regularizer
  
    @activity_regularizer.setter
    def activity_regularizer(self, regularizer):
      """Optional regularizer function for the output of this layer."""
      self._activity_regularizer = regularizer
  
    @property
    def trainable_weights(self):
      if self.trainable:
        nested = self._gather_children_attribute('trainable_weights')
        return self._trainable_weights + nested
      else:
        return []
  
    @property
    def non_trainable_weights(self):
      if self.trainable:
        nested = self._gather_children_attribute('non_trainable_weights')
        return self._non_trainable_weights + nested
      else:
        nested = self._gather_children_attribute('weights')
        return self._trainable_weights + self._non_trainable_weights + nested
  
    @property
    def weights(self):
      """Returns the list of all layer variables/weights.
  
      Returns:
        A list of variables.
      """
      return self.trainable_weights + self.non_trainable_weights
  
    @property
    def updates(self):
      return self._get_unfiltered_updates(check_trainable=True)
  
    @property
    def losses(self):
      """Losses which are associated with this `Layer`.
  
      Variable regularization tensors are created when this property is accessed,
      so it is eager safe: accessing `losses` under a `tf.GradientTape` will
      propagate gradients back to the corresponding variables.
  
      Returns:
        A list of tensors.
      """
      collected_losses = []
  
      # If any eager losses are present, we assume the model to be part of an
      # eager training loop (either a custom one or the one used when
      # `run_eagerly=True`), and so we always return just the eager losses in that
      # case.
      if self._eager_losses:
        collected_losses.extend(self._eager_losses)
      else:
        collected_losses.extend(self._losses)
      for regularizer in self._callable_losses:
        loss_tensor = regularizer()
        if loss_tensor is not None:
          collected_losses.append(loss_tensor)
      return collected_losses + self._gather_children_attribute('losses')
  
    @doc_controls.for_subclass_implementers
    def add_loss(self, losses, inputs=None):
      """Add loss tensor(s), potentially dependent on layer inputs.
  
      Some losses (for instance, activity regularization losses) may be dependent
      on the inputs passed when calling a layer. Hence, when reusing the same
      layer on different inputs `a` and `b`, some entries in `layer.losses` may
      be dependent on `a` and some on `b`. This method automatically keeps track
      of dependencies.
  
      This method can be used inside a subclassed layer or model's `call`
      function, in which case `losses` should be a Tensor or list of Tensors.
  
      Example:
  
      ```python
      class MyLayer(tf.keras.layers.Layer):
        def call(inputs, self):
          self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True)
          return 2*inputs
      ```
  
      This method can also be called directly on a Functional Model during
      construction. In this case, any loss Tensors passed to this Model must
      be symbolic and be able to be traced back to the model's `Input`s. These
      losses become part of the model's topology and are tracked in `get_config`.
  
      Example:
  
      ```python
      inputs = tf.keras.Input(shape=(10,))
      x = tf.keras.layers.Dense(10)(inputs)
      outputs = tf.keras.layers.Dense(1)(x)
      model = tf.keras.Model(inputs, outputs)
      # Actvity regularization.
      model.add_loss(tf.abs(tf.reduce_mean(x)))
      ````
  
      If this is not the case for your loss (if, for example, your loss references
      a `Variable` of one of the model's layers), you can wrap your loss in a
      zero-argument lambda. These losses are not tracked as part of the model's
      topology since they can't be serialized.
  
      Example:
  
      ```python
      inputs = tf.keras.Input(shape=(10,))
      x = tf.keras.layers.Dense(10)(inputs)
      outputs = tf.keras.layers.Dense(1)(x)
      model = tf.keras.Model(inputs, outputs)
      # Weight regularization.
      model.add_loss(lambda: tf.reduce_mean(x.kernel))
      ```
  
      The `get_losses_for` method allows to retrieve the losses relevant to a
      specific set of inputs.
  
      Arguments:
        losses: Loss tensor, or list/tuple of tensors. Rather than tensors, losses
          may also be zero-argument callables which create a loss tensor.
        inputs: Ignored when executing eagerly. If anything other than None is
          passed, it signals the losses are conditional on some of the layer's
          inputs, and thus they should only be run where these inputs are
          available. This is the case for activity regularization losses, for
          instance. If `None` is passed, the losses are assumed
          to be unconditional, and will apply across all dataflows of the layer
          (e.g. weight regularization losses).
      """
      def _tag_unconditional(loss):
        if callable(loss):
          loss = loss()
        if loss is None:
          return None  # Will be filtered out when computing the .losses property
        if not tensor_util.is_tensor(loss):
          loss = ops.convert_to_tensor(loss, dtype=backend.floatx())
        loss._unconditional_loss = (inputs is None)  # pylint: disable=protected-access
        return loss
  
      losses = nest.flatten(losses)
  
      callable_losses = []
      eager_losses = []
      symbolic_losses = []
      for loss in losses:
        if callable(loss):
          callable_losses.append(functools.partial(_tag_unconditional, loss))
        elif tf_utils.is_symbolic_tensor(loss):
          symbolic_losses.append(_tag_unconditional(loss))
        elif tensor_util.is_tensor(loss):
          eager_losses.append(_tag_unconditional(loss))
        elif loss is not None:  # `None` is valid but should be ignored.
          raise ValueError('Found non-Tensor loss: ' + str(loss))
  
      self._callable_losses += callable_losses
  
      call_context = base_layer_utils.is_in_call_context()
      if eager_losses and not call_context:
        raise ValueError(
            'Expected a symbolic Tensors or a callable for the loss value. '
            'Please wrap your loss computation in a zero argument `lambda`.')
  
      self._eager_losses += eager_losses
  
      if call_context:
        for symbolic_loss in symbolic_losses:
          self._losses.append(symbolic_loss)
      else:
        for symbolic_loss in symbolic_losses:
          if getattr(self, '_is_graph_network', False):
            new_layers = base_layer_utils.create_keras_history(symbolic_loss)
            # Losses must be keyed on inputs no matter what in order to
            # be supported in DistributionStrategy.
            add_loss_layer = AddLoss(unconditional=False)
            add_loss_layer(symbolic_loss)
            new_layers.append(add_loss_layer)
            self._insert_layers(new_layers)
          else:
            # Possible a loss was added in a Layer's `build`.
            self._losses.append(symbolic_loss)
  
    @trackable.no_automatic_dependency_tracking
    def _clear_losses(self):
      """Used every step in eager to reset losses."""
      self._eager_losses = []
      if hasattr(self, '_layers'):
        for layer in trackable_layer_utils.filter_empty_layer_containers(
            self._layers):
          layer._clear_losses()
  
    @property
    def metrics(self):
      return self._metrics + self._gather_children_attribute('metrics')
  
    @doc_controls.for_subclass_implementers
    def add_metric(self, value, aggregation=None, name=None):
      """Adds metric tensor to the layer.
  
      Args:
        value: Metric tensor.
        aggregation: Sample-wise metric reduction function. If `aggregation=None`,
          it indicates that the metric tensor provided has been aggregated
          already. eg, `bin_acc = BinaryAccuracy(name='acc')` followed by
          `model.add_metric(bin_acc(y_true, y_pred))`. If aggregation='mean', the
          given metric tensor will be sample-wise reduced using `mean` function.
          eg, `model.add_metric(tf.reduce_sum(outputs), name='output_mean',
          aggregation='mean')`.
        name: String metric name.
  
      Raises:
        ValueError: If `aggregation` is anything other than None or `mean`.
      """
      if aggregation is not None and aggregation != 'mean':
        raise ValueError(
            'We currently support only `mean` sample-wise metric aggregation. '
            'You provided aggregation=`%s`' % aggregation)
  
      is_symbolic = tf_utils.is_symbolic_tensor(value)
      call_context = base_layer_utils.is_in_call_context()
  
      if name is None and (not is_symbolic or not hasattr(value, '_metric_obj')):
        # Eg. `self.add_metric(math_ops.reduce_sum(x), aggregation='mean')`
        # In eager mode, we use metric name to lookup a metric. Without a name,
        # a new Mean metric wrapper will be created on every model/layer call.
        # So, we raise an error when no name is provided.
        # We will do the same for symbolic mode for consistency although a name
        # will be generated if no name is provided.
  
        # We will not raise this error in the foll use case for the sake of
        # consistency as name in provided in the metric constructor.
        # mean = metrics.Mean(name='my_metric')
        # model.add_metric(mean(outputs))
        raise ValueError('Please provide a name for your metric like '
                         '`self.add_metric(tf.reduce_sum(inputs), '
                         'name=\'mean_activation\', aggregation=\'mean\')`')
  
      if call_context:
        if is_symbolic:
          with backend.get_graph().as_default():
            self._symbolic_add_metric(value, aggregation, name)
        else:
          self._eager_add_metric(value, aggregation, name)
      else:
        if not is_symbolic:
          raise ValueError('Expected a symbolic Tensor for the metric value, '
                           'received: ' + str(value))
  
        # Possible a metric was added in a Layer's `build`.
        if not getattr(self, '_is_graph_network', False):
          with backend.get_graph().as_default():
            self._symbolic_add_metric(value, aggregation, name)
          return
  
        if getattr(value, '_metric_obj', None):
          raise ValueError('Using the result of calling a `Metric` object '
                           'when calling `add_metric` on a Functional '
                           'Model is not supported. Please pass the '
                           'Tensor to monitor directly.')
  
        # Insert layers into the Keras Graph Network.
        new_layers = base_layer_utils.create_keras_history(value)
        add_metric_layer = AddMetric(aggregation, name)
        add_metric_layer(value)
        new_layers.append(add_metric_layer)
        self._insert_layers(new_layers)
  
    @doc_controls.for_subclass_implementers
    def add_update(self, updates, inputs=None):
      """Add update op(s), potentially dependent on layer inputs.
  
      Weight updates (for instance, the updates of the moving mean and variance
      in a BatchNormalization layer) may be dependent on the inputs passed
      when calling a layer. Hence, when reusing the same layer on
      different inputs `a` and `b`, some entries in `layer.updates` may be
      dependent on `a` and some on `b`. This method automatically keeps track
      of dependencies.
  
      The `get_updates_for` method allows to retrieve the updates relevant to a
      specific set of inputs.
  
      This call is ignored when eager execution is enabled (in that case, variable
      updates are run on the fly and thus do not need to be tracked for later
      execution).
  
      Arguments:
        updates: Update op, or list/tuple of update ops, or zero-arg callable
          that returns an update op. A zero-arg callable should be passed in
          order to disable running the updates by setting `trainable=False`
          on this Layer, when executing in Eager mode.
        inputs: If anything other than None is passed, it signals the updates
          are conditional on some of the layer's inputs,
          and thus they should only be run where these inputs are available.
          This is the case for BatchNormalization updates, for instance.
          If None, the updates will be taken into account unconditionally,
          and you are responsible for making sure that any dependency they might
          have is available at runtime.
          A step counter might fall into this category.
      """
      updates = generic_utils.to_list(updates)
  
      if context.executing_eagerly():
        # Don't run callable updates if currently executing inside the `call`
        # of a Layer/Model with `trainable=False`.
        if not base_layer_utils.is_in_frozen_context():
          for update in updates:
            if callable(update):
              update()
        return  # Updates already applied when in eager mode.
  
      def process_update(x):
        if callable(x):
          x = x()
        if isinstance(x, ops.Operation):
          return x
        elif hasattr(x, 'op'):
          return x.op
        else:
          return ops.convert_to_tensor(x)
  
      updates = [process_update(x) for x in updates]
      self._updates += updates
      if inputs is None:
        for u in updates:
          u._unconditional_update = True  # pylint: disable=protected-access
      else:
        for u in updates:
          u._unconditional_update = False  # pylint: disable=protected-access
  
    def set_weights(self, weights):
      """Sets the weights of the layer, from Numpy arrays.
  
      Arguments:
          weights: a list of Numpy arrays. The number
              of arrays and their shape must match
              number of the dimensions of the weights
              of the layer (i.e. it should match the
              output of `get_weights`).
  
      Raises:
          ValueError: If the provided weights list does not match the
              layer's specifications.
      """
      params = self.weights
      if len(params) != len(weights):
        raise ValueError('You called `set_weights(weights)` on layer "' +
                         self.name + '" with a  weight list of length ' +
                         str(len(weights)) + ', but the layer was expecting ' +
                         str(len(params)) + ' weights. Provided weights: ' +
                         str(weights)[:50] + '...')
      if not params:
        return
      weight_value_tuples = []
      param_values = backend.batch_get_value(params)
      for pv, p, w in zip(param_values, params, weights):
        if pv.shape != w.shape:
          raise ValueError('Layer weight shape ' + str(pv.shape) +
                           ' not compatible with '
                           'provided weight shape ' + str(w.shape))
        weight_value_tuples.append((p, w))
      backend.batch_set_value(weight_value_tuples)
  
    def get_weights(self):
      """Returns the current weights of the layer.
  
      Returns:
          Weights values as a list of numpy arrays.
      """
      params = self.weights
      return backend.batch_get_value(params)
  
    def get_updates_for(self, inputs):
      """Retrieves updates relevant to a specific set of inputs.
  
      Arguments:
        inputs: Input tensor or list/tuple of input tensors.
  
      Returns:
        List of update ops of the layer that depend on `inputs`.
  
      Raises:
        RuntimeError: If called in Eager mode.
      """
      # Updates disabled if layer is not trainable and not explicitly stateful.
      if not self.trainable and not self.stateful:
        return []
  
      if inputs is None:
        # Requesting unconditional updates.
        return [
            x for x in self._get_unfiltered_updates() if x._unconditional_update  # pylint: disable=protected-access
        ]
  
      # Requesting input-conditional updates.
      inputs = nest.flatten(inputs)
      reachable = tf_utils.get_reachable_from_inputs(
          inputs, self._get_unfiltered_updates())
      return [u for u in self._get_unfiltered_updates() if u in reachable]  # pylint: disable=protected-access
  
    def get_losses_for(self, inputs):
      """Retrieves losses relevant to a specific set of inputs.
  
      Arguments:
        inputs: Input tensor or list/tuple of input tensors.
  
      Returns:
        List of loss tensors of the layer that depend on `inputs`.
  
      Raises:
        RuntimeError: If called in Eager mode.
      """
      if inputs is None:
        # Requesting unconditional losses.
        return [x for x in self.losses if x._unconditional_loss]  # pylint: disable=protected-access
  
      # Requesting input-conditional losses.
      inputs = nest.flatten(inputs)
      # Retrieve the set of tensors in the TF graph that depend on `inputs`.
      # The losses we want to return will be part of this set.
      # To avoid unnecessary work, we stop the search in case all of
      # `self.losses` have been retrieved.
      reachable = tf_utils.get_reachable_from_inputs(inputs, self.losses)
      losses = []
      for loss in self.losses:
        if loss in reachable:
          losses.append(loss)
      return losses
  
    def get_input_mask_at(self, node_index):
      """Retrieves the input mask tensor(s) of a layer at a given node.
  
      Arguments:
          node_index: Integer, index of the node
              from which to retrieve the attribute.
              E.g. `node_index=0` will correspond to the
              first time the layer was called.
  
      Returns:
          A mask tensor
          (or list of tensors if the layer has multiple inputs).
      """
      inputs = self.get_input_at(node_index)
      if isinstance(inputs, list):
        return [getattr(x, '_keras_mask', None) for x in inputs]
      else:
        return getattr(inputs, '_keras_mask', None)
  
    def get_output_mask_at(self, node_index):
      """Retrieves the output mask tensor(s) of a layer at a given node.
  
      Arguments:
          node_index: Integer, index of the node
              from which to retrieve the attribute.
              E.g. `node_index=0` will correspond to the
              first time the layer was called.
  
      Returns:
          A mask tensor
          (or list of tensors if the layer has multiple outputs).
      """
      output = self.get_output_at(node_index)
      if isinstance(output, list):
        return [getattr(x, '_keras_mask', None) for x in output]
      else:
        return getattr(output, '_keras_mask', None)
  
    @property
    def input_mask(self):
      """Retrieves the input mask tensor(s) of a layer.
  
      Only applicable if the layer has exactly one inbound node,
      i.e. if it is connected to one incoming layer.
  
      Returns:
          Input mask tensor (potentially None) or list of input
          mask tensors.
  
      Raises:
          AttributeError: if the layer is connected to
          more than one incoming layers.
      """
      inputs = self.input
      if isinstance(inputs, list):
        return [getattr(x, '_keras_mask', None) for x in inputs]
      else:
        return getattr(inputs, '_keras_mask', None)
  
    @property
    def output_mask(self):
      """Retrieves the output mask tensor(s) of a layer.
  
      Only applicable if the layer has exactly one inbound node,
      i.e. if it is connected to one incoming layer.
  
      Returns:
          Output mask tensor (potentially None) or list of output
          mask tensors.
  
      Raises:
          AttributeError: if the layer is connected to
          more than one incoming layers.
      """
      output = self.output
      if isinstance(output, list):
        return [getattr(x, '_keras_mask', None) for x in output]
      else:
        return getattr(output, '_keras_mask', None)
  
    def get_input_shape_at(self, node_index):
      """Retrieves the input shape(s) of a layer at a given node.
  
      Arguments:
          node_index: Integer, index of the node
              from which to retrieve the attribute.
              E.g. `node_index=0` will correspond to the
              first time the layer was called.
  
      Returns:
          A shape tuple
          (or list of shape tuples if the layer has multiple inputs).
  
      Raises:
        RuntimeError: If called in Eager mode.
      """
      return self._get_node_attribute_at_index(node_index, 'input_shapes',
                                               'input shape')
  
    def get_output_shape_at(self, node_index):
      """Retrieves the output shape(s) of a layer at a given node.
  
      Arguments:
          node_index: Integer, index of the node
              from which to retrieve the attribute.
              E.g. `node_index=0` will correspond to the
              first time the layer was called.
  
      Returns:
          A shape tuple
          (or list of shape tuples if the layer has multiple outputs).
  
      Raises:
        RuntimeError: If called in Eager mode.
      """
      return self._get_node_attribute_at_index(node_index, 'output_shapes',
                                               'output shape')
  
    def get_input_at(self, node_index):
      """Retrieves the input tensor(s) of a layer at a given node.
  
      Arguments:
          node_index: Integer, index of the node
              from which to retrieve the attribute.
              E.g. `node_index=0` will correspond to the
              first time the layer was called.
  
      Returns:
          A tensor (or list of tensors if the layer has multiple inputs).
  
      Raises:
        RuntimeError: If called in Eager mode.
      """
      return self._get_node_attribute_at_index(node_index, 'input_tensors',
                                               'input')
  
    def get_output_at(self, node_index):
      """Retrieves the output tensor(s) of a layer at a given node.
  
      Arguments:
          node_index: Integer, index of the node
              from which to retrieve the attribute.
              E.g. `node_index=0` will correspond to the
              first time the layer was called.
  
      Returns:
          A tensor (or list of tensors if the layer has multiple outputs).
  
      Raises:
        RuntimeError: If called in Eager mode.
      """
      return self._get_node_attribute_at_index(node_index, 'output_tensors',
                                               'output')
  
    @property
    def input(self):
      """Retrieves the input tensor(s) of a layer.
  
      Only applicable if the layer has exactly one input,
      i.e. if it is connected to one incoming layer.
  
      Returns:
          Input tensor or list of input tensors.
  
      Raises:
          AttributeError: if the layer is connected to
          more than one incoming layers.
  
      Raises:
        RuntimeError: If called in Eager mode.
        AttributeError: If no inbound nodes are found.
      """
      if not self._inbound_nodes:
        raise AttributeError('Layer ' + self.name +
                             ' is not connected, no input to return.')
      return self._get_node_attribute_at_index(0, 'input_tensors', 'input')
  
    @property
    def output(self):
      """Retrieves the output tensor(s) of a layer.
  
      Only applicable if the layer has exactly one output,
      i.e. if it is connected to one incoming layer.
  
      Returns:
        Output tensor or list of output tensors.
  
      Raises:
        AttributeError: if the layer is connected to more than one incoming
          layers.
        RuntimeError: if called in Eager mode.
      """
      if not self._inbound_nodes:
        raise AttributeError('Layer ' + self.name + ' has no inbound nodes.')
      return self._get_node_attribute_at_index(0, 'output_tensors', 'output')
  
    @property
    def input_shape(self):
      """Retrieves the input shape(s) of a layer.
  
      Only applicable if the layer has exactly one input,
      i.e. if it is connected to one incoming layer, or if all inputs
      have the same shape.
  
      Returns:
          Input shape, as an integer shape tuple
          (or list of shape tuples, one tuple per input tensor).
  
      Raises:
          AttributeError: if the layer has no defined input_shape.
          RuntimeError: if called in Eager mode.
      """
      if not self._inbound_nodes:
        raise AttributeError('The layer has never been called '
                             'and thus has no defined input shape.')
      all_input_shapes = set(
          [str(node.input_shapes) for node in self._inbound_nodes])
      if len(all_input_shapes) == 1:
        return self._inbound_nodes[0].input_shapes
      else:
        raise AttributeError('The layer "' + str(self.name) +
                             ' has multiple inbound nodes, '
                             'with different input shapes. Hence '
                             'the notion of "input shape" is '
                             'ill-defined for the layer. '
                             'Use `get_input_shape_at(node_index)` '
                             'instead.')
  
    def count_params(self):
      """Count the total number of scalars composing the weights.
  
      Returns:
          An integer count.
  
      Raises:
          ValueError: if the layer isn't yet built
            (in which case its weights aren't yet defined).
      """
      if not self.built:
        if self.__class__.__name__ == 'Sequential':
          self.build()  # pylint: disable=no-value-for-parameter
        else:
          raise ValueError('You tried to call `count_params` on ' + self.name +
                           ', but the layer isn\'t built. '
                           'You can build it manually via: `' + self.name +
                           '.build(batch_input_shape)`.')
      return int(sum(np.prod(w.shape.as_list()) for w in self.weights))
  
    @property
    def output_shape(self):
      """Retrieves the output shape(s) of a layer.
  
      Only applicable if the layer has one output,
      or if all outputs have the same shape.
  
      Returns:
          Output shape, as an integer shape tuple
          (or list of shape tuples, one tuple per output tensor).
  
      Raises:
          AttributeError: if the layer has no defined output shape.
          RuntimeError: if called in Eager mode.
      """
      if not self._inbound_nodes:
        raise AttributeError('The layer has never been called '
                             'and thus has no defined output shape.')
      all_output_shapes = set(
          [str(node.output_shapes) for node in self._inbound_nodes])
      if len(all_output_shapes) == 1:
        return self._inbound_nodes[0].output_shapes
      else:
        raise AttributeError('The layer "%s"'
                             ' has multiple inbound nodes, '
                             'with different output shapes. Hence '
                             'the notion of "output shape" is '
                             'ill-defined for the layer. '
                             'Use `get_output_shape_at(node_index)` '
                             'instead.' % self.name)
  
    @property
    @doc_controls.do_not_doc_inheritable
    def inbound_nodes(self):
      """Deprecated, do NOT use! Only for compatibility with external Keras."""
      return self._inbound_nodes
  
    @property
    @doc_controls.do_not_doc_inheritable
    def outbound_nodes(self):
      """Deprecated, do NOT use! Only for compatibility with external Keras."""
      return self._outbound_nodes
  
    ##############################################################################
    # Methods & attributes below are public aliases of other methods.            #
    ##############################################################################
  
    def apply(self, inputs, *args, **kwargs):
      """Apply the layer on a input.
  
      This is an alias of `self.__call__`.
  
      Arguments:
        inputs: Input tensor(s).
        *args: additional positional arguments to be passed to `self.call`.
        **kwargs: additional keyword arguments to be passed to `self.call`.
  
      Returns:
        Output tensor(s).
      """
      return self.__call__(inputs, *args, **kwargs)
  
    @doc_controls.for_subclass_implementers
    def add_variable(self, *args, **kwargs):
      """Alias for `add_weight`."""
      return self.add_weight(*args, **kwargs)
  
    @property
    def variables(self):
      """Returns the list of all layer variables/weights.
  
      Alias of `self.weights`.
  
      Returns:
        A list of variables.
      """
      return self.weights
  
    @property
    def trainable_variables(self):
      return self.trainable_weights
  
    @property
    def non_trainable_variables(self):
      return self.non_trainable_weights
  
    ##############################################################################
    # Methods & attributes below are all private and only used by the framework. #
    ##############################################################################
  
    def _set_dtype_and_policy(self, dtype):
      """Sets self._dtype and self._mixed_precision_policy."""
      if dtype:
        if isinstance(dtype, policy.Policy):
          self._mixed_precision_policy = dtype
          self._dtype = self._mixed_precision_policy.default_variable_dtype
        else:
          # If a non-policy dtype is passed, no casting should be done. So we use
          # the "infer" policy, which does no casting.
          self._mixed_precision_policy = policy.Policy('infer')
          self._dtype = dtypes.as_dtype(dtype).name
      else:
        self._mixed_precision_policy = policy.global_policy()
        # If the global policy has not been set, it will be an "infer" policy
        # without a default variable dtype, and so self._dtype will be None. In
        # that case, self._dtype will be set when the layer is built or called.
        self._dtype = self._mixed_precision_policy.default_variable_dtype
  
    def _name_scope(self):
      return self.name
  
    def _init_set_name(self, name, zero_based=True):
      if not name:
        self._name = base_layer_utils.unique_layer_name(
            generic_utils.to_snake_case(self.__class__.__name__),
            zero_based=zero_based)
      else:
        self._name = name
  
    def _get_existing_metric(self, name=None):
      match = [m for m in self._metrics if m.name == name]
      if not match:
        return
      if len(match) > 1:
        raise ValueError(
            'Please provide different names for the metrics you have added. '
            'We found {} metrics with the name: "{}"'.format(len(match), name))
      return match[0]
  
    def _eager_add_metric(self, value, aggregation=None, name=None):
      # If the given metric is available in `metrics` list we just update state
      # on it, otherwise we create a new metric instance and
      # add it to the `metrics` list.
      metric_obj = getattr(value, '_metric_obj', None)
      if metric_obj:
        name = metric_obj.name
  
      match = self._get_existing_metric(name)
      if match:
        # Tensors that come from a Metric object already updated the Metric state.
        if not metric_obj:
          match(value)
        return
  
      if not metric_obj:
        assert aggregation is not None
        metric_obj, _ = base_layer_utils.create_mean_metric(value, name)
      self._metrics.append(metric_obj)
  
    def _symbolic_add_metric(self, value, aggregation=None, name=None):
      if aggregation is None:
        # Iterate over the metrics and check if the given metric exists already.
        # This can happen when a metric instance is created in subclassed model
        # layer `__init__` and we have tracked that instance already in
        # model.__setattr__.
        match = self._get_existing_metric(name)
        if match:
          result_tensor = value
          if match.name not in self._metrics_tensors:
            self._metrics_tensors[match.name] = result_tensor
            return
          else:
            raise ValueError(
                'We currently do not support reusing a metric instance.')
        elif hasattr(value, '_metric_obj'):
          # We track the instance using the metadata on the result tensor.
          result_tensor = value
          metric_obj = result_tensor._metric_obj
        else:
          raise ValueError(
              'We do not support adding an aggregated metric result tensor that '
              'is not the output of a `tf.keras.metrics.Metric` metric instance. '
              'Without having access to the metric instance we cannot reset the '
              'state of a metric after every epoch during training. You can '
              'create a `tf.keras.metrics.Metric` instance and pass the result '
              'here or pass an un-aggregated result with `aggregation` parameter '
              'set as `mean`. For example: `self.add_metric(tf.reduce_sum(inputs)'
              ', name=\'mean_activation\', aggregation=\'mean\')`')
      else:
        # If a non-aggregated tensor is given as input (ie. `aggregation` is
        # explicitly set to `mean`), we wrap the tensor in `Mean` metric.
        metric_obj, result_tensor = base_layer_utils.create_mean_metric(
            value, name)
      self._metrics.append(metric_obj)
      self._metrics_tensors[metric_obj.name] = result_tensor
  
    def _handle_weight_regularization(self, name, variable, regularizer):
      """Create lambdas which compute regularization losses."""
  
      def _loss_for_variable(v):
        """Creates a regularization loss `Tensor` for variable `v`."""
        with ops.name_scope(name + '/Regularizer'):
          regularization = regularizer(v)
        return regularization
  
      if isinstance(variable, tf_variables.PartitionedVariable):
        for v in variable:
          self.add_loss(functools.partial(_loss_for_variable, v))
      else:
        self.add_loss(functools.partial(_loss_for_variable, variable))
  
    def _handle_activity_regularization(self, inputs, outputs):
      # Apply activity regularization.
      # Note that it should be applied every time the layer creates a new
      # output, since it is output-specific.
      if self._activity_regularizer:
        output_list = nest.flatten(outputs)
        with ops.name_scope('ActivityRegularizer'):
          for output in output_list:
            activity_loss = self._activity_regularizer(output)
            batch_size = math_ops.cast(
                array_ops.shape(output)[0], activity_loss.dtype)
            # Make activity regularization strength batch-agnostic.
            mean_activity_loss = activity_loss / batch_size
            self.add_loss(mean_activity_loss, inputs=inputs)
  
    def _set_mask_metadata(self, inputs, outputs, previous_mask):
      flat_outputs = nest.flatten(outputs)
      mask_already_computed = (
          getattr(self, '_compute_output_and_mask_jointly', False) or
          all(getattr(x, '_keras_mask', None) is not None for x in flat_outputs))
  
      if not mask_already_computed:
        if hasattr(self, 'compute_mask'):
          output_masks = self.compute_mask(inputs, previous_mask)
          # `compute_mask` can return a single `None` even when a Layer
          # has multiple outputs.
          if output_masks is None:
            flat_masks = [None for _ in flat_outputs]
          else:
            flat_masks = nest.flatten(output_masks)
        else:
          flat_masks = [None for _ in flat_outputs]
  
        for output, mask in zip(flat_outputs, flat_masks):
          try:
            output._keras_mask = mask
          except AttributeError:
            # C Type such as np.ndarray.
            pass
  
      if tf_utils.are_all_symbolic_tensors(flat_outputs):
        for output in flat_outputs:
          if getattr(output, '_keras_mask', None) is not None:
            # Do not track masks for `TensorFlowOpLayer` construction.
            output._keras_mask._keras_history_checked = True
  
    def _set_connectivity_metadata_(self, inputs, outputs, args, kwargs):
      call_convention = getattr(
          self, '_call_convention',
          base_layer_utils.CallConvention.EXPLICIT_INPUTS_ARGUMENT)
      if args:
        if call_convention == (base_layer_utils
                               .CallConvention.EXPLICIT_INPUTS_ARGUMENT):
          raise TypeError(
              'This layer ("{}") takes an `inputs` argument in `call()`, '
              'and only the `inputs` argument may be specified as a positional '
              'argument. Pass everything else as a keyword argument '
              '(those arguments will not be tracked '
              'as inputs to the layer).'.format(self.name))
        elif call_convention == (base_layer_utils
                                 .CallConvention.SINGLE_POSITIONAL_ARGUMENT):
          raise TypeError(
              'This layer ("{}") takes a single positional argument in `call()`,'
              ' which is by convention the `inputs` argument, '
              'and only this argument may be specified as a positional argument. '
              'Pass everything else as a keyword argument '
              '(those arguments will not be tracked '
              'as inputs to the layer).'.format(self.name))
  
      # If the layer returns tensors from its inputs, unmodified,
      # we copy them to avoid loss of tensor metadata.
      output_ls = nest.flatten(outputs)
      inputs_ls = nest.flatten(inputs)
      output_ls_copy = []
      for x in output_ls:
        if x in inputs_ls:
          with ops.name_scope(self.name):
            x = array_ops.identity(x)
        output_ls_copy.append(x)
      outputs = nest.pack_sequence_as(outputs, output_ls_copy)
  
      inputs, kwargs = self._inputs_from_call_args(
          call_args=(inputs,) + args, call_kwargs=kwargs)
      # Add an inbound node to the layer, so it can keep track of this call.
      # This updates the layer history of the output tensor(s).
      kwargs.pop('mask', None)  # `mask` should not be serialized.
      self._add_inbound_node(
          input_tensors=inputs, output_tensors=outputs, arguments=kwargs)
      return inputs, outputs
  
    def _inputs_from_call_args(self, call_args, call_kwargs):
      """Get Layer inputs from __call__ *args and **kwargs.
  
      Args:
        call_args: The positional arguments passed to __call__.
        call_kwargs: The keyword argument dict passed to __call__.
  
      Returns:
        A tuple of (inputs, non_input_kwargs). These may be the same objects as
        were passed in (call_args and call_kwargs).
      """
      call_convention = getattr(
          self, '_call_convention',
          base_layer_utils.CallConvention.EXPLICIT_INPUTS_ARGUMENT)
      if (call_convention in (
          base_layer_utils.CallConvention.EXPLICIT_INPUTS_ARGUMENT,
          base_layer_utils.CallConvention.SINGLE_POSITIONAL_ARGUMENT)):
        assert len(call_args) == 1  # TypeError raised earlier in __call__.
        return call_args[0], call_kwargs
      else:
        call_arg_spec = tf_inspect.getfullargspec(self.call)
        # There is no explicit "inputs" argument expected or provided to
        # call(). Arguments which have default values are considered non-inputs,
        # and arguments without are considered inputs.
        if call_arg_spec.defaults:
          if call_arg_spec.varargs is not None:
            raise TypeError(
                'Layers may not accept both positional arguments and '
                'arguments with default values (unable to determine which '
                'are inputs to the layer). '
                'Issue occurred with layer "%s"' % (self.name))
          keyword_arg_names = set(
              call_arg_spec.args[-len(call_arg_spec.defaults):])
        else:
          keyword_arg_names = set()
          # Training is never an input argument name, to allow signatures like
          # call(x, training).
        keyword_arg_names.add('training')
        _, unwrapped_call = tf_decorator.unwrap(self.call)
        bound_args = inspect.getcallargs(
            unwrapped_call, *call_args, **call_kwargs)
        if call_arg_spec.varkw is not None:
          var_kwargs = bound_args.pop(call_arg_spec.varkw)
          bound_args.update(var_kwargs)
          keyword_arg_names = keyword_arg_names.union(var_kwargs.keys())
        all_args = call_arg_spec.args
        if all_args and bound_args[all_args[0]] is self:
          # Ignore the 'self' argument of methods
          bound_args.pop(call_arg_spec.args[0])
          all_args = all_args[1:]
        non_input_arg_values = {}
        input_arg_values = []
        remaining_args_are_keyword = False
        for argument_name in all_args:
          if argument_name in keyword_arg_names:
            remaining_args_are_keyword = True
          else:
            if remaining_args_are_keyword:
              raise TypeError(
                  'Found a positional argument in a layer call after a non-input '
                  'argument. All arguments after "training" must be keyword '
                  'arguments, and are not tracked as inputs to the layer. '
                  'Issue occurred with layer "%s"' % (self.name))
          if remaining_args_are_keyword:
            non_input_arg_values[argument_name] = bound_args[argument_name]
          else:
            input_arg_values.append(bound_args[argument_name])
        if call_arg_spec.varargs is not None:
          input_arg_values.extend(bound_args[call_arg_spec.varargs])
        return input_arg_values, non_input_arg_values
  
    def _add_inbound_node(self,
                          input_tensors,
                          output_tensors,
                          arguments=None):
      """Internal method to create an inbound node for the layer.
  
      Arguments:
          input_tensors: list of input tensors.
          output_tensors: list of output tensors.
          arguments: dictionary of keyword arguments that were passed to the
              `call` method of the layer at the call that created the node.
      """
      inbound_layers = nest.map_structure(lambda t: t._keras_history[0],
                                          input_tensors)
      node_indices = nest.map_structure(lambda t: t._keras_history[1],
                                        input_tensors)
      tensor_indices = nest.map_structure(lambda t: t._keras_history[2],
                                          input_tensors)
  
      # Create node, add it to inbound nodes.
      Node(
          self,
          inbound_layers=inbound_layers,
          node_indices=node_indices,
          tensor_indices=tensor_indices,
          input_tensors=input_tensors,
          output_tensors=output_tensors,
          arguments=arguments)
  
      # Update tensor history metadata.
      # The metadata attribute consists of
      # 1) a layer instance
      # 2) a node index for the layer
      # 3) a tensor index for the node.
      # The allows layer reuse (multiple nodes per layer) and multi-output
      # or multi-input layers (e.g. a layer can return multiple tensors,
      # and each can be sent to a different layer).
      for i, tensor in enumerate(nest.flatten(output_tensors)):
        tensor._keras_history = (self, len(self._inbound_nodes) - 1, i)  # pylint: disable=protected-access
  
    def _get_node_attribute_at_index(self, node_index, attr, attr_name):
      """Private utility to retrieves an attribute (e.g. inputs) from a node.
  
      This is used to implement the methods:
          - get_input_shape_at
          - get_output_shape_at
          - get_input_at
          etc...
  
      Arguments:
          node_index: Integer index of the node from which
              to retrieve the attribute.
          attr: Exact node attribute name.
          attr_name: Human-readable attribute name, for error messages.
  
      Returns:
          The layer's attribute `attr` at the node of index `node_index`.
  
      Raises:
          RuntimeError: If the layer has no inbound nodes, or if called in Eager
          mode.
          ValueError: If the index provided does not match any node.
      """
      if not self._inbound_nodes:
        raise RuntimeError('The layer has never been called '
                           'and thus has no defined ' + attr_name + '.')
      if not len(self._inbound_nodes) > node_index:
        raise ValueError('Asked to get ' + attr_name + ' at node ' +
                         str(node_index) + ', but the layer has only ' +
                         str(len(self._inbound_nodes)) + ' inbound nodes.')
      values = getattr(self._inbound_nodes[node_index], attr)
      if isinstance(values, list) and len(values) == 1:
        return values[0]
      else:
        return values
  
    def _maybe_build(self, inputs):
      # Check input assumptions set before layer building, e.g. input rank.
      if self.built:
        return
  
      input_spec.assert_input_compatibility(
          self.input_spec, inputs, self.name)
      input_list = nest.flatten(inputs)
      if input_list and self._dtype is None:
        try:
          self._dtype = input_list[0].dtype.base_dtype.name
        except AttributeError:
          pass
      input_shapes = None
      if all(hasattr(x, 'shape') for x in input_list):
        input_shapes = nest.map_structure(lambda x: x.shape, inputs)
      # Only call `build` if the user has manually overridden the build method.
      if not hasattr(self.build, '_is_default'):
        self.build(input_shapes)
      # We must set self.built since user defined build functions are not
      # constrained to set self.built.
      self.built = True
  
    def _symbolic_call(self, inputs):
      input_shapes = nest.map_structure(lambda x: x.shape, inputs)
      output_shapes = self.compute_output_shape(input_shapes)
  
      def _make_placeholder_like(shape):
        ph = backend.placeholder(shape=shape, dtype=self.dtype)
        ph._keras_mask = None
        return ph
  
      return nest.map_structure(_make_placeholder_like, output_shapes)
  
    @property
    def _obj_reference_counts(self):
      """A dictionary counting the number of attributes referencing an object."""
      self._maybe_create_attribute('_obj_reference_counts_dict',
                                   object_identity.ObjectIdentityDictionary())
      return self._obj_reference_counts_dict
  
    def _maybe_create_attribute(self, name, default_value):
      """Create the attribute with the default value if it hasn't been created.
  
      This is useful for fields that is used for tracking purpose,
      _trainable_weights, or _layers. Note that user could create a layer subclass
      and assign an internal field before invoking the Layer.__init__(), the
      __setattr__() need to create the tracking fields and __init__() need to not
      override them.
  
      Args:
        name: String, the name of the attribute.
        default_value: Object, the default value of the attribute.
      """
      if not hasattr(self, name):
        super(Layer, self).__setattr__(name, default_value)
  
    def __delattr__(self, name):
      # For any super.__delattr__() call, we will directly use the implementation
      # in Trackable and skip the behavior in AutoTrackable. The Layer was
      # originally use Trackable as base class, the change of using Module as base
      # class forced us to have AutoTrackable in the class hierarchy. Skipping
      # the __delattr__ and __setattr__ in AutoTrackable will keep the status quo.
      existing_value = getattr(self, name, None)
  
      # If this value is replacing an existing object assigned to an attribute, we
      # should clean it out to avoid leaking memory. First we check if there are
      # other attributes referencing it.
      reference_counts = self._obj_reference_counts
      if existing_value not in reference_counts:
        super(tracking.AutoTrackable, self).__delattr__(name)
        return
  
      reference_count = reference_counts[existing_value]
      if reference_count > 1:
        # There are other remaining references. We can't remove this object from
        # _layers etc.
        reference_counts[existing_value] = reference_count - 1
        super(tracking.AutoTrackable, self).__delattr__(name)
        return
      else:
        # This is the last remaining reference.
        del reference_counts[existing_value]
  
      super(tracking.AutoTrackable, self).__delattr__(name)
  
      if (isinstance(existing_value, Layer)
          or trackable_layer_utils.has_weights(existing_value)):
        super(tracking.AutoTrackable, self).__setattr__(
            '_layers',
            [l for l in self._layers if l is not existing_value])
      if isinstance(existing_value, tf_variables.Variable):
        super(tracking.AutoTrackable, self).__setattr__(
            '_trainable_weights',
            [w for w in self._trainable_weights if w is not existing_value])
        super(tracking.AutoTrackable, self).__setattr__(
            '_non_trainable_weights',
            [w for w in self._non_trainable_weights if w is not existing_value])
  
    def __setattr__(self, name, value):
      if (name == '_setattr_tracking' or
          not getattr(self, '_setattr_tracking', True) or
          getattr(self, '_is_graph_network', False) or
          # Exclude @property.setters from tracking
          hasattr(self.__class__, name)):
        super(tracking.AutoTrackable, self).__setattr__(name, value)
        return
  
      # Keep track of trackable objects, for the needs of `Network.save_weights`.
      value = data_structures.sticky_attribute_assignment(
          trackable=self, value=value, name=name)
  
      reference_counts = self._obj_reference_counts
      reference_counts[value] = reference_counts.get(value, 0) + 1
  
      # Clean out the old attribute, which clears _layers and _trainable_weights
      # if necessary.
      try:
        self.__delattr__(name)
      except AttributeError:
        pass
  
      # TODO(scottzhu): Need to track Module object as well for weight tracking.
      # Be careful about metric if it becomes a Module in future.
      # Append value to self._layers if relevant
      if (isinstance(value, Layer) or
          trackable_layer_utils.has_weights(value)):
        self._maybe_create_attribute('_layers', [])
        # We need to check object identity to avoid de-duplicating empty
        # container types which compare equal.
        if not any((layer is value for layer in self._layers)):
          self._layers.append(value)
          if hasattr(value, '_use_resource_variables'):
            # Legacy layers (V1 tf.layers) must always use
            # resource variables.
            value._use_resource_variables = True
  
      # Append value to list of trainable / non-trainable weights if relevant
      # TODO(b/125122625): This won't pick up on any variables added to a
      # list/dict after creation.
      for val in nest.flatten(value):
        # TODO(b/126450014): Remove `_UnreadVariable` check here when assign ops
        # no longer return True for isinstance Variable checks.
        if (isinstance(val, tf_variables.Variable) and
            not isinstance(val, resource_variable_ops._UnreadVariable)):  # pylint: disable=protected-access
          # Users may add extra weights/variables
          # simply by assigning them to attributes (invalid for graph networks)
          self._maybe_create_attribute('_trainable_weights', [])
          self._maybe_create_attribute('_non_trainable_weights', [])
          if val not in self._trainable_weights + self._non_trainable_weights:
            if val.trainable:
              self._trainable_weights.append(val)
            else:
              self._non_trainable_weights.append(val)
            backend.track_variable(val)
  
      # Skip the auto trackable from tf.Module to keep status quo. See the comment
      # at __delattr__.
      super(tracking.AutoTrackable, self).__setattr__(name, value)
  
    def _gather_children_attribute(self, attribute):
      assert attribute in {
          'weights', 'trainable_weights', 'non_trainable_weights', 'updates',
          'losses', 'metrics'
      }
      if hasattr(self, '_layers'):
        nested_layers = trackable_layer_utils.filter_empty_layer_containers(
            self._layers)
        return list(
            itertools.chain.from_iterable(
                getattr(layer, attribute) for layer in nested_layers))
      return []
  
    # This is a hack so that the is_layer (within
    # training/trackable/layer_utils.py) check doesn't get the weights attr.
    # TODO(b/110718070): Remove when fixed.
    def _is_layer(self):
      return True
  
    def _get_unfiltered_updates(self, check_trainable=True):
      if check_trainable and not self.trainable and not self.stateful:
        return []
      return self._updates + self._gather_children_attribute('updates')
  
  
  class Node(object):
    """A `Node` describes the connectivity between two layers.
  
    Each time a layer is connected to some new input,
    a node is added to `layer._inbound_nodes`.
    Each time the output of a layer is used by another layer,
    a node is added to `layer._outbound_nodes`.
  
    Arguments:
        outbound_layer: the layer that takes
            `input_tensors` and turns them into `output_tensors`
            (the node gets created when the `call`
            method of the layer was called).
        inbound_layers: a list of layers, the same length as `input_tensors`,
            the layers from where `input_tensors` originate.
        node_indices: a list of integers, the same length as `inbound_layers`.
            `node_indices[i]` is the origin node of `input_tensors[i]`
            (necessary since each inbound layer might have several nodes,
            e.g. if the layer is being shared with a different data stream).
        tensor_indices: a list of integers,
            the same length as `inbound_layers`.
            `tensor_indices[i]` is the index of `input_tensors[i]` within the
            output of the inbound layer
            (necessary since each inbound layer might
            have multiple tensor outputs, with each one being
            independently manipulable).
        input_tensors: list of input tensors.
        output_tensors: list of output tensors.
        arguments: dictionary of keyword arguments that were passed to the
            `call` method of the layer at the call that created the node.
  
    `node_indices` and `tensor_indices` are basically fine-grained coordinates
    describing the origin of the `input_tensors`.
  
    A node from layer A to layer B is added to:
      - A._outbound_nodes
      - B._inbound_nodes
    """
  
    def __init__(self,
                 outbound_layer,
                 inbound_layers,
                 node_indices,
                 tensor_indices,
                 input_tensors,
                 output_tensors,
                 arguments=None):
      # Layer instance (NOT a sequence)
      if isinstance(outbound_layer, (list, tuple, dict)):
        raise ValueError('`outbound_layer` should be a layer instance, '
                         'not a list, tuple, or, dict.')
  
      # this is the layer that takes a nested structure of input tensors
      # and turns them into a nested structure of output tensors.
      # the current node will be added to
      # the inbound_nodes of outbound_layer.
      self.outbound_layer = outbound_layer
  
      # The following 3 properties describe where
      # the input tensors come from: which layers,
      # and for each layer, which node and which
      # tensor output of each node.
  
      # Nested structure of layer instances.
      self.inbound_layers = inbound_layers
      # Nested structure of integers, 1:1 mapping with inbound_layers.
      self.node_indices = node_indices
      # Nested of integers, 1:1 mapping with inbound_layers.
      self.tensor_indices = tensor_indices
  
      # Following 2 properties:
      # tensor inputs and outputs of outbound_layer.
  
      # Nested structure of tensors. 1:1 mapping with inbound_layers.
      self.input_tensors = input_tensors
      # Nested structure of tensors, created by outbound_layer.call().
      self.output_tensors = output_tensors
  
      # Following 2 properties: input and output shapes.
  
      # Nested structure of shape tuples, shapes of input_tensors.
      self.input_shapes = nest.map_structure(backend.int_shape, input_tensors)
      # Nested structure of shape tuples, shapes of output_tensors.
      self.output_shapes = nest.map_structure(backend.int_shape, output_tensors)
  
      # Optional keyword arguments to layer's `call`.
      self.arguments = arguments
  
      # Add nodes to all layers involved.
      for layer in nest.flatten(inbound_layers):
        if layer is not None:
          # For compatibility with external Keras, we use the deprecated
          # accessor here.
          layer.outbound_nodes.append(self)
      # For compatibility with external Keras, we use the deprecated
      # accessor here.
      outbound_layer.inbound_nodes.append(self)
  
    def iterate_inbound(self):
      """Returns a list of tuples representing the inbound data.
  
      Returns:
        List of tuples like: (inbound_layer, node_index, tensor_index, tensor).
      """
      return zip(
          nest.flatten(self.inbound_layers), nest.flatten(self.node_indices),
          nest.flatten(self.tensor_indices), nest.flatten(self.input_tensors))
  
    def get_config(self):
      inbound_names = nest.map_structure(
          lambda layer: layer.name if layer else None, self.inbound_layers)
      return {
          'outbound_layer': self.outbound_layer.name,
          'inbound_layers': inbound_names,
          'node_indices': self.node_indices,
          'tensor_indices': self.tensor_indices
      }
  
  
  class TensorFlowOpLayer(Layer):
    """Wraps a TensorFlow Operation in a Layer.
  
    This class is used internally by the Functional API. When a user
    uses a raw TensorFlow Operation on symbolic tensors originating
    from an `Input` Layer, the resultant operation will be wrapped
    with this Layer object in order to make the operation compatible
    with the Keras API.
  
    This Layer will create a new, identical operation (except for inputs
    and outputs) every time it is called. If `run_eagerly` is `True`,
    the op creation and calculation will happen inside an Eager function.
  
    Instances of this Layer are created when `autolambda` is called, which
    is whenever a Layer's `__call__` encounters symbolic inputs that do
    not have Keras metadata, or when a Network's `__init__` encounters
    outputs that do not have Keras metadata.
  
    Attributes:
      node_def: String, the serialized NodeDef of the Op this layer will wrap.
      constants: Dict of NumPy arrays, the values of any Tensors needed for this
        Operation that do not originate from a Keras `Input` Layer. Since all
        placeholders must come from Keras `Input` Layers, these Tensors must be
        treated as constant in the Functional API.
      name: String, the name of the Layer.
      trainable: Bool, whether this Layer is trainable. Currently Variables are
        not supported, and so this parameter has no effect.
      dtype: The default dtype of this Layer. Inherited from `Layer` and has no
        effect on this class, however is used in `get_config`.
    """
  
    def __init__(self,
                 node_def,
                 constants=None,
                 name=None,
                 trainable=True,
                 dtype=None):
      super(TensorFlowOpLayer, self).__init__(
          name=name, trainable=trainable, dtype=dtype)
      self.node_def = node_def_pb2.NodeDef.FromString(node_def)
      self.constants = constants or {}
      # Layer uses original op unless it is called on new inputs.
      # This means `built` is not set in `__call__`.
      self.built = True
  
    def call(self, inputs):
      if context.executing_eagerly():
        return self._defun_call(inputs)
      return self._make_op(inputs)
  
    def _make_op(self, inputs):
      inputs = nest.flatten(inputs)
      graph = inputs[0].graph
      with graph.as_default():
        for index, constant in self.constants.items():
          constant = ops.convert_to_tensor(constant)
          inputs.insert(index, constant)
  
        self.node_def.name = graph.unique_name(self.node_def.name)
        # Check for case where first input should be a list of Tensors.
        if 'N' in self.node_def.attr:
          num_tensors = self.node_def.attr['N'].i
          inputs = [inputs[:num_tensors]] + inputs[num_tensors:]
        c_op = ops._create_c_op(graph, self.node_def, inputs, control_inputs=[])
        op = graph._create_op_from_tf_operation(c_op)
  
        # Record the gradient because custom-made ops don't go through the
        # code-gen'd eager call path
        op_type = compat.as_str(op.op_def.name)
        attr_names = [compat.as_str(attr.name) for attr in op.op_def.attr]
        attrs = []
        for attr_name in attr_names:
          attrs.append(attr_name)
          attrs.append(op.get_attr(attr_name))
        attrs = tuple(attrs)
        execute.record_gradient(op_type, op.inputs, attrs, op.outputs,
                                op.name)
  
        if len(op.outputs) == 1:
          return op.outputs[0]
        return op.outputs
  
    @function.defun
    def _defun_call(self, inputs):
      """Wraps the op creation method in an Eager function for `run_eagerly`."""
      return self._make_op(inputs)
  
    def get_config(self):
      config = super(TensorFlowOpLayer, self).get_config()
      config.update({
          'node_def': self.node_def.SerializeToString(),
          'constants': {
              i: backend.get_value(c) for i, c in self.constants.items()
          }
      })
      return config
  
  
  class AddLoss(Layer):
    """Adds its inputs as a loss.
  
    Attributes:
      unconditional: Whether or not the loss should be conditioned on the inputs.
    """
  
    def __init__(self, unconditional, **kwargs):
      super(AddLoss, self).__init__(**kwargs)
      self.unconditional = unconditional
  
    def call(self, inputs):
      self.add_loss(inputs, inputs=(not self.unconditional))
      return inputs
  
    def get_config(self):
      config = super(AddLoss, self).get_config()
      config.update({'unconditional': self.unconditional})
      return config
  
  
  class AddMetric(Layer):
    """Adds its inputs as a metric.
  
    Attributes:
      aggregation: 'mean' or None. How the inputs should be aggregated.
      metric_name: The name to use for this metric.
    """
  
    def __init__(self, aggregation=None, metric_name=None, **kwargs):
      super(AddMetric, self).__init__(**kwargs)
      self.aggregation = aggregation
      self.metric_name = metric_name
  
    def call(self, inputs):
      self.add_metric(inputs, self.aggregation, self.metric_name)
      return inputs
  
    def get_config(self):
      config = super(AddMetric, self).get_config()
      config.update({
          'aggregation': self.aggregation,
          'metric_name': self.metric_name
      })
      return config
  
  
  def default(method):
    """Decorates a method to detect overrides in subclasses."""
    method._is_default = True
    return method
  
  
  # Avoid breaking users who directly import this symbol from this file.
  # TODO(fchollet): remove this.
  InputSpec = input_spec.InputSpec  # pylint:disable=invalid-name
  