code: # 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.
  # ==============================================================================
  """Training-related part of the Keras engine.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  import copy
  import itertools
  import json
  import os
  import warnings
  
  import six
  from tensorflow.python.distribute import values as ds_values
  from tensorflow.python.eager import context
  from keras import backend
  from keras import callbacks as callbacks_module
  from keras import optimizer_v1
  from keras import optimizers
  from keras.distribute import distributed_training_utils as dist_utils
  from keras.engine import base_layer
  from keras.engine import base_layer_utils
  from keras.engine import compile_utils
  from keras.engine import data_adapter
  from keras.engine import training_utils
  from keras.mixed_precision import loss_scale_optimizer as lso
  from keras.mixed_precision import policy
  from keras.saving import hdf5_format
  from keras.saving import save
  from keras.saving import saving_utils
  from keras.saving.saved_model import json_utils
  from keras.saving.saved_model import model_serialization
  from keras.utils import generic_utils
  from keras.utils import layer_utils
  from keras.utils import tf_inspect
  from keras.utils import tf_utils
  from keras.utils import version_utils
  from keras.utils.io_utils import ask_to_proceed_with_overwrite
  from keras.utils.io_utils import path_to_string
  from keras.utils.mode_keys import ModeKeys
  from tensorflow.python.ops import summary_ops_v2
  from tensorflow.python.platform import tf_logging as logging
  from tensorflow.python.training import checkpoint_management
  from tensorflow.python.training.tracking import base as trackable
  from tensorflow.python.training.tracking import data_structures
  from tensorflow.python.training.tracking import util as trackable_utils
  from tensorflow.python.util.tf_export import keras_export
  from tensorflow.tools.docs import doc_controls
  
  
  # pylint: disable=g-import-not-at-top
  try:
    import h5py
  except ImportError:
    h5py = None
  
  try:
    import yaml
  except ImportError:
    yaml = None
  # pylint: enable=g-import-not-at-top
  
  
  def disable_multi_worker(method):
    """Decorator that disallows multi-worker use of `method`."""
  
    def _method_wrapper(self, *args, **kwargs):
      if self._in_multi_worker_mode():  # pylint: disable=protected-access
        raise ValueError('{} is not supported in multi-worker mode.'.format(
            method.__name__))
      return method(self, *args, **kwargs)
  
    return tf.__internal__.decorator.make_decorator(
        target=method, decorator_func=_method_wrapper)
  
  
  def inject_functional_model_class(cls):
    """Inject `Functional` into the hierarchy of this class if needed."""
    from keras.engine import functional  # pylint: disable=g-import-not-at-top
    from keras.engine import training_v1  # pylint: disable=g-import-not-at-top
    if cls == Model or cls == training_v1.Model:
      return functional.Functional
    # In case there is any multiple inheritance, we stop injecting the
    # class if keras model is not in its class hierarchy.
    if cls == object:
      return object
  
    cls.__bases__ = tuple(inject_functional_model_class(base)
                          for base in cls.__bases__)
    # Trigger any `__new__` class swapping that needed to happen on `Functional`
    # but did not because functional was not in the class hierarchy.
    cls.__new__(cls)
  
    return cls
  
  
  def is_functional_model_init_params(args, kwargs):
    return (len(args) == 2 or
            len(args) == 1 and 'outputs' in kwargs or
            'inputs' in kwargs and 'outputs' in kwargs)
  
  
  @keras_export('keras.Model', 'keras.models.Model')
  class Model(base_layer.Layer, version_utils.ModelVersionSelector):
    """`Model` groups layers into an object with training and inference features.
  
    Args:
        inputs: The input(s) of the model: a `keras.Input` object or list of
            `keras.Input` objects.
        outputs: The output(s) of the model. See Functional API example below.
        name: String, the name of the model.
  
    There are two ways to instantiate a `Model`:
  
    1 - With the "Functional API", where you start from `Input`,
    you chain layer calls to specify the model's forward pass,
    and finally you create your model from inputs and outputs:
  
    ```python
    import tensorflow as tf
  
    inputs = tf.keras.Input(shape=(3,))
    x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
    outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    ```
  
    2 - By subclassing the `Model` class: in that case, you should define your
    layers in `__init__` and you should implement the model's forward pass
    in `call`.
  
    ```python
    import tensorflow as tf
  
    class MyModel(tf.keras.Model):
  
      def __init__(self):
        super(MyModel, self).__init__()
        self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
  
      def call(self, inputs):
        x = self.dense1(inputs)
        return self.dense2(x)
  
    model = MyModel()
    ```
  
    If you subclass `Model`, you can optionally have
    a `training` argument (boolean) in `call`, which you can use to specify
    a different behavior in training and inference:
  
    ```python
    import tensorflow as tf
  
    class MyModel(tf.keras.Model):
  
      def __init__(self):
        super(MyModel, self).__init__()
        self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
        self.dropout = tf.keras.layers.Dropout(0.5)
  
      def call(self, inputs, training=False):
        x = self.dense1(inputs)
        if training:
          x = self.dropout(x, training=training)
        return self.dense2(x)
  
    model = MyModel()
    ```
  
    Once the model is created, you can config the model with losses and metrics
    with `model.compile()`, train the model with `model.fit()`, or use the model
    to do prediction with `model.predict()`.
    """
    _TF_MODULE_IGNORED_PROPERTIES = frozenset(
        itertools.chain(('_train_counter', '_test_counter', '_predict_counter',
                         '_steps_per_execution'),
                        base_layer.Layer._TF_MODULE_IGNORED_PROPERTIES))  # pylint: disable=protected-access
  
    def __new__(cls, *args, **kwargs):
      # Signature detection
      if is_functional_model_init_params(args, kwargs) and cls == Model:
        # Functional model
        from keras.engine import functional  # pylint: disable=g-import-not-at-top
        return functional.Functional(skip_init=True, *args, **kwargs)
      else:
        return super(Model, cls).__new__(cls, *args, **kwargs)
  
    @trackable.no_automatic_dependency_tracking
    def __init__(self, *args, **kwargs):
      self._is_model_for_instrumentation = True
      base_layer.keras_api_gauge.get_cell('model').set(True)
  
      # Special case for Subclassed Functional Model, which we couldn't detect
      # when __new__ is called. We only realize it is a functional model when it
      # calls super.__init__ with input and output tensor.
      from keras.engine import functional  # pylint: disable=g-import-not-at-top
      if (is_functional_model_init_params(args, kwargs) and
          not isinstance(self, functional.Functional)):
        # Filter the kwargs for multiple inheritance.
        supported_kwargs = ['inputs', 'outputs', 'name', 'trainable', 'skip_init']
        model_kwargs = {k: kwargs[k] for k in kwargs if k in supported_kwargs}
        other_kwargs = {k: kwargs[k] for k in kwargs if k not in supported_kwargs}
        inject_functional_model_class(self.__class__)
        functional.Functional.__init__(self, *args, **model_kwargs)
  
        # In case there is any multiple inheritance here, we need to call the
        # __init__ for any class that appears after the Functional class.
        clz_to_init = []
        found_functional_class = False
        for clz in self.__class__.__bases__:
          if issubclass(clz, functional.Functional):
            found_functional_class = True
            continue
          if found_functional_class:
            clz_to_init.append(clz)
  
        if clz_to_init:
          for clz in clz_to_init:
            clz.__init__(self, *args, **other_kwargs)
        elif other_kwargs:
          # In case there are unused kwargs, we should raise an error to user, in
          # case they have a typo in the param name.
          raise TypeError(
              'The following keyword arguments aren\'t supported: {}'.format(
                  other_kwargs))
        return
  
      base_layer.keras_api_gauge.get_cell('Model subclass').set(True)
      # The following are implemented as property functions:
      # self.trainable_weights
      # self.non_trainable_weights
      # `inputs` / `outputs` will only appear in kwargs if either are misspelled.
      generic_utils.validate_kwargs(kwargs, {
          'trainable', 'dtype', 'dynamic', 'name', 'autocast', 'inputs', 'outputs'
      })
      super(Model, self).__init__(**kwargs)
      # By default, Model is a subclass model, which is not in graph network.
      self._is_graph_network = False
  
      self.inputs = None
      self.outputs = None
      self.input_names = None
      self.output_names = None
      # stop_training is used by callback to stop training when error happens
      self.stop_training = False
      self.history = None
      # These objects are used in the default `Model.compile`. They are not
      # guaranteed to be set after `Model.compile` is called, as users can
      # override compile with custom logic.
      self.compiled_loss = None
      self.compiled_metrics = None
  
      # This is True for Sequential networks and Functional networks.
      self._compute_output_and_mask_jointly = False
  
      # Don't reset compilation if already done. This may occur if calling
      # `__init__` (or `_init_graph_network`) on an already-compiled model
      # such as a Sequential model. Sequential models may need to rebuild
      # themselves after compilation.
      self._maybe_create_attribute('_is_compiled', False)
      self._maybe_create_attribute('optimizer', None)
  
      # Model must be created under scope of DistStrat it will be trained with.
      if tf.distribute.has_strategy():
        self._distribution_strategy = tf.distribute.get_strategy()
      else:
        self._distribution_strategy = None
      # Defaults to value of `tf.config.experimental_functions_run_eagerly`.
      self._run_eagerly = None
      # Initialize cache attrs.
      self._reset_compile_cache()
  
      # Fault-tolerance handler. Set in `ModelCheckpoint`.
      self._training_state = None
      self._saved_model_inputs_spec = None
      self._trackable_saver = (
          trackable_utils.saver_with_op_caching(self))
  
      self._steps_per_execution = None
  
      self._init_batch_counters()
      self._base_model_initialized = True
  
    @trackable.no_automatic_dependency_tracking
    def _init_batch_counters(self):
      # Untracked Variables, used to keep track of mini-batches seen in `fit`,
      # `evaluate`, and `predict`.
      agg = tf.VariableAggregation.ONLY_FIRST_REPLICA
      self._train_counter = tf.Variable(0, dtype='int64', aggregation=agg)
      self._test_counter = tf.Variable(0, dtype='int64', aggregation=agg)
      self._predict_counter = tf.Variable(
          0, dtype='int64', aggregation=agg)
  
    def __setattr__(self, name, value):
      if not getattr(self, '_self_setattr_tracking', True):
        super(Model, self).__setattr__(name, value)
        return
  
      if all(
          isinstance(v, (base_layer.Layer,
                         data_structures.TrackableDataStructure)) or
          base_layer_utils.has_weights(v) for v in tf.nest.flatten(value)):
        try:
          self._base_model_initialized
        except AttributeError:
          # six.raise_from supresses the original AttributeError from being raised
          six.raise_from(
              RuntimeError('It looks like you are subclassing `Model` and you '
                           'forgot to call `super(YourClass, self).__init__()`.'
                           ' Always start with this line.'), None)
  
      super(Model, self).__setattr__(name, value)
  
    @generic_utils.default
    def build(self, input_shape):
      """Builds the model based on input shapes received.
  
      This is to be used for subclassed models, which do not know at instantiation
      time what their inputs look like.
  
      This method only exists for users who want to call `model.build()` in a
      standalone way (as a substitute for calling the model on real data to
      build it). It will never be called by the framework (and thus it will
      never throw unexpected errors in an unrelated workflow).
  
      Args:
       input_shape: Single tuple, TensorShape, or list/dict of shapes, where
           shapes are tuples, integers, or TensorShapes.
  
      Raises:
        ValueError:
          1. In case of invalid user-provided data (not of type tuple,
             list, TensorShape, or dict).
          2. If the model requires call arguments that are agnostic
             to the input shapes (positional or kwarg in call signature).
          3. If not all layers were properly built.
          4. If float type inputs are not supported within the layers.
  
        In each of these cases, the user should build their model by calling it
        on real tensor data.
      """
      if self._is_graph_network:
        super(Model, self).build(input_shape)
        return
  
      if input_shape is None:
        raise ValueError('Input shape must be defined when calling build on a '
                         'model subclass network.')
      valid_types = (tuple, list, tf.TensorShape, dict)
      if not isinstance(input_shape, valid_types):
        raise ValueError('Specified input shape is not one of the valid types. '
                         'Please specify a batch input shape of type tuple or '
                         'list of input shapes. User provided '
                         'input type: {}'.format(type(input_shape)))
  
      if input_shape and not self.inputs:
        # We create placeholders for the `None`s in the shape and build the model
        # in a Graph. Since tf.Variable is compatible with both eager execution
        # and graph building, the variables created after building the model in
        # a Graph are still valid when executing eagerly.
        if tf.executing_eagerly():
          graph = tf.__internal__.FuncGraph('build_graph')
        else:
          graph = backend.get_graph()
        with graph.as_default():
          if (isinstance(input_shape, list) and
              all(d is None or isinstance(d, int) for d in input_shape)):
            input_shape = tuple(input_shape)
          if isinstance(input_shape, list):
            x = [base_layer_utils.generate_placeholders_from_shape(shape)
                 for shape in input_shape]
          elif isinstance(input_shape, dict):
            x = {
                k: base_layer_utils.generate_placeholders_from_shape(shape)
                for k, shape in input_shape.items()
            }
          else:
            x = base_layer_utils.generate_placeholders_from_shape(input_shape)
  
          kwargs = {}
          call_signature = self._call_full_argspec
          call_args = call_signature.args
          # Exclude `self`, `inputs`, and any argument with a default value.
          if len(call_args) > 2:
            if call_signature.defaults:
              call_args = call_args[2:-len(call_signature.defaults)]
            else:
              call_args = call_args[2:]
            for arg in call_args:
              if arg == 'training':
                # Case where `training` is a positional arg with no default.
                kwargs['training'] = False
              else:
                # Has invalid call signature with unknown positional arguments.
                raise ValueError(
                    'Currently, you cannot build your model if it has '
                    'positional or keyword arguments that are not '
                    'inputs to the model, but are required for its '
                    '`call` method. Instead, in order to instantiate '
                    'and build your model, `call` your model on real '
                    'tensor data with all expected call arguments.')
          elif len(call_args) < 2:
            # Signature without `inputs`.
            raise ValueError('You can only call `build` on a model if its `call` '
                             'method accepts an `inputs` argument.')
          try:
            self.call(x, **kwargs)
          except (tf.errors.InvalidArgumentError, TypeError):
            raise ValueError('You cannot build your model by calling `build` '
                             'if your layers do not support float type inputs. '
                             'Instead, in order to instantiate and build your '
                             'model, `call` your model on real tensor data (of '
                             'the correct dtype).')
      super(Model, self).build(input_shape)
  
    @doc_controls.doc_in_current_and_subclasses
    def call(self, inputs, training=None, mask=None):
      """Calls the model on new inputs.
  
      In this case `call` just reapplies
      all ops in the graph to the new inputs
      (e.g. build a new computational graph from the provided inputs).
  
      Note: This method should not be called directly. It is only meant to be
      overridden when subclassing `tf.keras.Model`.
      To call a model on an input, always use the `__call__` method,
      i.e. `model(inputs)`, which relies on the underlying `call` method.
  
      Args:
          inputs: A tensor or list of tensors.
          training: Boolean or boolean scalar tensor, indicating whether to run
            the `Network` in training mode or inference mode.
          mask: A mask or list of masks. A mask can be
              either a tensor or None (no mask).
  
      Returns:
          A tensor if there is a single output, or
          a list of tensors if there are more than one outputs.
      """
      raise NotImplementedError('When subclassing the `Model` class, you should '
                                'implement a `call` method.')
  
    def compile(self,
                optimizer='rmsprop',
                loss=None,
                metrics=None,
                loss_weights=None,
                weighted_metrics=None,
                run_eagerly=None,
                steps_per_execution=None,
                **kwargs):
      """Configures the model for training.
  
      Args:
          optimizer: String (name of optimizer) or optimizer instance. See
            `tf.keras.optimizers`.
          loss: String (name of objective function), objective function or
            `tf.keras.losses.Loss` instance. See `tf.keras.losses`. An objective
            function is any callable with the signature `loss = fn(y_true,
            y_pred)`, where y_true = ground truth values with shape =
            `[batch_size, d0, .. dN]`, except sparse loss functions such as sparse
            categorical crossentropy where shape = `[batch_size, d0, .. dN-1]`.
            y_pred = predicted values with shape = `[batch_size, d0, .. dN]`. It
            returns a weighted loss float tensor. If a custom `Loss` instance is
            used and reduction is set to NONE, return value has the shape
            [batch_size, d0, .. dN-1] ie. per-sample or per-timestep loss values;
            otherwise, it is a scalar. If the model has multiple outputs, you can
            use a different loss on each output by passing a dictionary or a list
            of losses. The loss value that will be minimized by the model will
            then be the sum of all individual losses.
          metrics: List of metrics to be evaluated by the model during training
            and testing. Each of this can be a string (name of a built-in
            function), function or a `tf.keras.metrics.Metric` instance. See
            `tf.keras.metrics`. Typically you will use `metrics=['accuracy']`. A
            function is any callable with the signature `result = fn(y_true,
            y_pred)`. To specify different metrics for different outputs of a
            multi-output model, you could also pass a dictionary, such as
              `metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}`.
                You can also pass a list (len = len(outputs)) of lists of metrics
                such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or
                `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the
                strings 'accuracy' or 'acc', we convert this to one of
                `tf.keras.metrics.BinaryAccuracy`,
                `tf.keras.metrics.CategoricalAccuracy`,
                `tf.keras.metrics.SparseCategoricalAccuracy` based on the loss
                function used and the model output shape. We do a similar
                conversion for the strings 'crossentropy' and 'ce' as well.
          loss_weights: Optional list or dictionary specifying scalar coefficients
            (Python floats) to weight the loss contributions of different model
            outputs. The loss value that will be minimized by the model will then
            be the *weighted sum* of all individual losses, weighted by the
            `loss_weights` coefficients.
              If a list, it is expected to have a 1:1 mapping to the model's
                outputs. If a dict, it is expected to map output names (strings)
                to scalar coefficients.
          weighted_metrics: List of metrics to be evaluated and weighted by
            sample_weight or class_weight during training and testing.
          run_eagerly: Bool. Defaults to `False`. If `True`, this `Model`'s
            logic will not be wrapped in a `tf.function`. Recommended to leave
            this as `None` unless your `Model` cannot be run inside a
            `tf.function`.
          steps_per_execution: Int. Defaults to 1. The number of batches to
            run during each `tf.function` call. Running multiple batches
            inside a single `tf.function` call can greatly improve performance
            on TPUs or small models with a large Python overhead.
            At most, one full epoch will be run each
            execution. If a number larger than the size of the epoch is passed,
            the execution will be truncated to the size of the epoch.
            Note that if `steps_per_execution` is set to `N`,
            `Callback.on_batch_begin` and `Callback.on_batch_end` methods
            will only be called every `N` batches
            (i.e. before/after each `tf.function` execution).
          **kwargs: Arguments supported for backwards compatibility only.
  
      Raises:
          ValueError: In case of invalid arguments for
              `optimizer`, `loss` or `metrics`.
      """
      base_layer.keras_api_gauge.get_cell('compile').set(True)
      with self.distribute_strategy.scope():
        if 'experimental_steps_per_execution' in kwargs:
          logging.warn('The argument `steps_per_execution` is no longer '
                       'experimental. Pass `steps_per_execution` instead of '
                       '`experimental_steps_per_execution`.')
          if not steps_per_execution:
            steps_per_execution = kwargs.pop('experimental_steps_per_execution')
  
        self._validate_compile(optimizer, metrics, **kwargs)
        self._run_eagerly = run_eagerly
  
        self.optimizer = self._get_optimizer(optimizer)
        self.compiled_loss = compile_utils.LossesContainer(
            loss, loss_weights, output_names=self.output_names)
        self.compiled_metrics = compile_utils.MetricsContainer(
            metrics, weighted_metrics, output_names=self.output_names)
  
        self._configure_steps_per_execution(steps_per_execution or 1)
  
        # Initializes attrs that are reset each time `compile` is called.
        self._reset_compile_cache()
        self._is_compiled = True
  
        self.loss = loss or {}  # Backwards compat.
  
    def _get_optimizer(self, optimizer):
      """Wraps `optimizer` in `LossScaleOptimizer` if necessary."""
      # The deprecated PolicyV1 has a loss_scale, which we use for backwards
      # compatibility to match TF 2.3 behavior. The new Policy does not have a
      # loss_scale, so we use dynamic loss scaling if the mixed_float16 policy is
      # used.
      if isinstance(self._dtype_policy, policy.PolicyV1):
        loss_scale = self._dtype_policy.loss_scale
      elif self._dtype_policy.name == 'mixed_float16':
        loss_scale = 'dynamic'
      else:
        loss_scale = None
  
      def _get_single_optimizer(opt):
        opt = optimizers.get(opt)
        if (loss_scale is not None and
            not isinstance(opt, lso.LossScaleOptimizer)):
          if loss_scale == 'dynamic':
            opt = lso.LossScaleOptimizer(opt)
          else:
            opt = lso.LossScaleOptimizerV1(opt, loss_scale)
        return opt
  
      return tf.nest.map_structure(_get_single_optimizer, optimizer)
  
    @trackable.no_automatic_dependency_tracking
    def _reset_compile_cache(self):
      self.train_function = None
      self.test_function = None
      self.predict_function = None
  
      # Used to cache `trainable` attr of `Layer`s for `fit`.
      self._compiled_trainable_state = self._get_trainable_state()
  
    @trackable.no_automatic_dependency_tracking
    def _configure_steps_per_execution(self, steps_per_execution):
      self._steps_per_execution = tf.Variable(
          steps_per_execution,
          dtype='int64',
          aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)
  
    @property
    def _should_compute_mask(self):
      return False
  
    @property
    def metrics(self):
      """Returns the model's metrics added using `compile`, `add_metric` APIs.
  
      Note: Metrics passed to `compile()` are available only after a `keras.Model`
      has been trained/evaluated on actual data.
  
      Examples:
  
      >>> inputs = tf.keras.layers.Input(shape=(3,))
      >>> outputs = tf.keras.layers.Dense(2)(inputs)
      >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
      >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
      >>> [m.name for m in model.metrics]
      []
  
      >>> x = np.random.random((2, 3))
      >>> y = np.random.randint(0, 2, (2, 2))
      >>> model.fit(x, y)
      >>> [m.name for m in model.metrics]
      ['loss', 'mae']
  
      >>> inputs = tf.keras.layers.Input(shape=(3,))
      >>> d = tf.keras.layers.Dense(2, name='out')
      >>> output_1 = d(inputs)
      >>> output_2 = d(inputs)
      >>> model = tf.keras.models.Model(
      ...    inputs=inputs, outputs=[output_1, output_2])
      >>> model.add_metric(
      ...    tf.reduce_sum(output_2), name='mean', aggregation='mean')
      >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
      >>> model.fit(x, (y, y))
      >>> [m.name for m in model.metrics]
      ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
      'out_1_acc', 'mean']
  
      """
      metrics = []
      if self._is_compiled:
        # TODO(omalleyt): Track `LossesContainer` and `MetricsContainer` objects
        # so that attr names are not load-bearing.
        if self.compiled_loss is not None:
          metrics += self.compiled_loss.metrics
        if self.compiled_metrics is not None:
          metrics += self.compiled_metrics.metrics
  
      for l in self._flatten_layers():
        metrics.extend(l._metrics)  # pylint: disable=protected-access
      return metrics
  
    @property
    def metrics_names(self):
      """Returns the model's display labels for all outputs.
  
      Note: `metrics_names` are available only after a `keras.Model` has been
      trained/evaluated on actual data.
  
      Examples:
  
      >>> inputs = tf.keras.layers.Input(shape=(3,))
      >>> outputs = tf.keras.layers.Dense(2)(inputs)
      >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
      >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
      >>> model.metrics_names
      []
  
      >>> x = np.random.random((2, 3))
      >>> y = np.random.randint(0, 2, (2, 2))
      >>> model.fit(x, y)
      >>> model.metrics_names
      ['loss', 'mae']
  
      >>> inputs = tf.keras.layers.Input(shape=(3,))
      >>> d = tf.keras.layers.Dense(2, name='out')
      >>> output_1 = d(inputs)
      >>> output_2 = d(inputs)
      >>> model = tf.keras.models.Model(
      ...    inputs=inputs, outputs=[output_1, output_2])
      >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
      >>> model.fit(x, (y, y))
      >>> model.metrics_names
      ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
      'out_1_acc']
  
      """
  
      # This property includes all output names including `loss` and per-output
      # losses for backward compatibility.
      return [m.name for m in self.metrics]
  
    @property
    def distribute_strategy(self):
      """The `tf.distribute.Strategy` this model was created under."""
      return self._distribution_strategy or tf.distribute.get_strategy()
  
    @property
    def run_eagerly(self):
      """Settable attribute indicating whether the model should run eagerly.
  
      Running eagerly means that your model will be run step by step,
      like Python code. Your model might run slower, but it should become easier
      for you to debug it by stepping into individual layer calls.
  
      By default, we will attempt to compile your model to a static graph to
      deliver the best execution performance.
  
      Returns:
        Boolean, whether the model should run eagerly.
      """
      if self.dynamic and self._run_eagerly is False:  # pylint:disable=g-bool-id-comparison
        # TODO(fchollet): consider using py_func to enable this.
        raise ValueError('Your model contains layers that can only be '
                         'successfully run in eager execution (layers '
                         'constructed with `dynamic=True`). '
                         'You cannot set `run_eagerly=False`.')
  
      # Run eagerly logic, by priority:
      # (1) Dynamic models must be run eagerly.
      # (2) Explicitly setting run_eagerly causes a Model to be run eagerly.
      # (3) Not explicitly setting run_eagerly defaults to TF's global setting.
      return (self.dynamic or self._run_eagerly or
              (tf.config.functions_run_eagerly() and
               self._run_eagerly is None))
  
    @run_eagerly.setter
    def run_eagerly(self, value):
      self._run_eagerly = value
  
    def train_step(self, data):
      """The logic for one training step.
  
      This method can be overridden to support custom training logic.
      This method is called by `Model.make_train_function`.
  
      This method should contain the mathematical logic for one step of training.
      This typically includes the forward pass, loss calculation, backpropagation,
      and metric updates.
  
      Configuration details for *how* this logic is run (e.g. `tf.function` and
      `tf.distribute.Strategy` settings), should be left to
      `Model.make_train_function`, which can also be overridden.
  
      Args:
        data: A nested structure of `Tensor`s.
  
      Returns:
        A `dict` containing values that will be passed to
        `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
        values of the `Model`'s metrics are returned. Example:
        `{'loss': 0.2, 'accuracy': 0.7}`.
  
      """
      # These are the only transformations `Model.fit` applies to user-input
      # data when a `tf.data.Dataset` is provided.
      data = data_adapter.expand_1d(data)
      x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)
  
      with tf.GradientTape() as tape:
        y_pred = self(x, training=True)
        loss = self.compiled_loss(
            y, y_pred, sample_weight, regularization_losses=self.losses)
      self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
      self.compiled_metrics.update_state(y, y_pred, sample_weight)
      return {m.name: m.result() for m in self.metrics}
  
    def make_train_function(self):
      """Creates a function that executes one step of training.
  
      This method can be overridden to support custom training logic.
      This method is called by `Model.fit` and `Model.train_on_batch`.
  
      Typically, this method directly controls `tf.function` and
      `tf.distribute.Strategy` settings, and delegates the actual training
      logic to `Model.train_step`.
  
      This function is cached the first time `Model.fit` or
      `Model.train_on_batch` is called. The cache is cleared whenever
      `Model.compile` is called.
  
      Returns:
        Function. The function created by this method should accept a
        `tf.data.Iterator`, and return a `dict` containing values that will
        be passed to `tf.keras.Callbacks.on_train_batch_end`, such as
        `{'loss': 0.2, 'accuracy': 0.7}`.
      """
      if self.train_function is not None:
        return self.train_function
  
      def step_function(model, iterator):
        """Runs a single training step."""
  
        def run_step(data):
          outputs = model.train_step(data)
          # Ensure counter is updated only if `train_step` succeeds.
          with tf.control_dependencies(_minimum_control_deps(outputs)):
            model._train_counter.assign_add(1)  # pylint: disable=protected-access
          return outputs
  
        data = next(iterator)
        outputs = model.distribute_strategy.run(run_step, args=(data,))
        outputs = reduce_per_replica(
            outputs, self.distribute_strategy, reduction='first')
        write_scalar_summaries(outputs, step=model._train_counter)  # pylint: disable=protected-access
        return outputs
  
      if self._steps_per_execution.numpy().item() == 1:
  
        def train_function(iterator):
          """Runs a training execution with one step."""
          return step_function(self, iterator)
  
      else:
  
        def train_function(iterator):
          """Runs a training execution with multiple steps."""
          for _ in tf.range(self._steps_per_execution):
            outputs = step_function(self, iterator)
          return outputs
  
      if not self.run_eagerly:
        train_function = tf.function(
            train_function, experimental_relax_shapes=True)
  
      self.train_function = train_function
      return self.train_function
  
    def fit(self,
            x=None,
            y=None,
            batch_size=None,
            epochs=1,
            verbose=1,
            callbacks=None,
            validation_split=0.,
            validation_data=None,
            shuffle=True,
            class_weight=None,
            sample_weight=None,
            initial_epoch=0,
            steps_per_epoch=None,
            validation_steps=None,
            validation_batch_size=None,
            validation_freq=1,
            max_queue_size=10,
            workers=1,
            use_multiprocessing=False):
      """Trains the model for a fixed number of epochs (iterations on a dataset).
  
      Args:
          x: Input data. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A dict mapping input names to the corresponding array/tensors,
              if the model has named inputs.
            - A `tf.data` dataset. Should return a tuple
              of either `(inputs, targets)` or
              `(inputs, targets, sample_weights)`.
            - A generator or `keras.utils.Sequence` returning `(inputs, targets)`
              or `(inputs, targets, sample_weights)`.
            A more detailed description of unpacking behavior for iterator types
            (Dataset, generator, Sequence) is given below.
          y: Target data. Like the input data `x`,
            it could be either Numpy array(s) or TensorFlow tensor(s).
            It should be consistent with `x` (you cannot have Numpy inputs and
            tensor targets, or inversely). If `x` is a dataset, generator,
            or `keras.utils.Sequence` instance, `y` should
            not be specified (since targets will be obtained from `x`).
          batch_size: Integer or `None`.
              Number of samples per gradient update.
              If unspecified, `batch_size` will default to 32.
              Do not specify the `batch_size` if your data is in the
              form of datasets, generators, or `keras.utils.Sequence` instances
              (since they generate batches).
          epochs: Integer. Number of epochs to train the model.
              An epoch is an iteration over the entire `x` and `y`
              data provided.
              Note that in conjunction with `initial_epoch`,
              `epochs` is to be understood as "final epoch".
              The model is not trained for a number of iterations
              given by `epochs`, but merely until the epoch
              of index `epochs` is reached.
          verbose: 0, 1, or 2. Verbosity mode.
              0 = silent, 1 = progress bar, 2 = one line per epoch.
              Note that the progress bar is not particularly useful when
              logged to a file, so verbose=2 is recommended when not running
              interactively (eg, in a production environment).
          callbacks: List of `keras.callbacks.Callback` instances.
              List of callbacks to apply during training.
              See `tf.keras.callbacks`. Note `tf.keras.callbacks.ProgbarLogger`
              and `tf.keras.callbacks.History` callbacks are created automatically
              and need not be passed into `model.fit`.
              `tf.keras.callbacks.ProgbarLogger` is created or not based on
              `verbose` argument to `model.fit`.
          validation_split: Float between 0 and 1.
              Fraction of the training data to be used as validation data.
              The model will set apart this fraction of the training data,
              will not train on it, and will evaluate
              the loss and any model metrics
              on this data at the end of each epoch.
              The validation data is selected from the last samples
              in the `x` and `y` data provided, before shuffling. This argument is
              not supported when `x` is a dataset, generator or
             `keras.utils.Sequence` instance.
          validation_data: Data on which to evaluate
              the loss and any model metrics at the end of each epoch.
              The model will not be trained on this data. Thus, note the fact
              that the validation loss of data provided using `validation_split`
              or `validation_data` is not affected by regularization layers like
              noise and dropout.
              `validation_data` will override `validation_split`.
              `validation_data` could be:
                - tuple `(x_val, y_val)` of Numpy arrays or tensors
                - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays
                - dataset
              For the first two cases, `batch_size` must be provided.
              For the last case, `validation_steps` could be provided.
              Note that `validation_data` does not support all the data types that
              are supported in `x`, eg, dict, generator or `keras.utils.Sequence`.
          shuffle: Boolean (whether to shuffle the training data
              before each epoch) or str (for 'batch'). This argument is ignored
              when `x` is a generator. 'batch' is a special option for dealing
              with the limitations of HDF5 data; it shuffles in batch-sized
              chunks. Has no effect when `steps_per_epoch` is not `None`.
          class_weight: Optional dictionary mapping class indices (integers)
              to a weight (float) value, used for weighting the loss function
              (during training only).
              This can be useful to tell the model to
              "pay more attention" to samples from
              an under-represented class.
          sample_weight: Optional Numpy array of weights for
              the training samples, used for weighting the loss function
              (during training only). You can either pass a flat (1D)
              Numpy array with the same length as the input samples
              (1:1 mapping between weights and samples),
              or in the case of temporal data,
              you can pass a 2D array with shape
              `(samples, sequence_length)`,
              to apply a different weight to every timestep of every sample. This
              argument is not supported when `x` is a dataset, generator, or
             `keras.utils.Sequence` instance, instead provide the sample_weights
              as the third element of `x`.
          initial_epoch: Integer.
              Epoch at which to start training
              (useful for resuming a previous training run).
          steps_per_epoch: Integer or `None`.
              Total number of steps (batches of samples)
              before declaring one epoch finished and starting the
              next epoch. When training with input tensors such as
              TensorFlow data tensors, the default `None` is equal to
              the number of samples in your dataset divided by
              the batch size, or 1 if that cannot be determined. If x is a
              `tf.data` dataset, and 'steps_per_epoch'
              is None, the epoch will run until the input dataset is exhausted.
              When passing an infinitely repeating dataset, you must specify the
              `steps_per_epoch` argument. This argument is not supported with
              array inputs.
          validation_steps: Only relevant if `validation_data` is provided and
              is a `tf.data` dataset. Total number of steps (batches of
              samples) to draw before stopping when performing validation
              at the end of every epoch. If 'validation_steps' is None, validation
              will run until the `validation_data` dataset is exhausted. In the
              case of an infinitely repeated dataset, it will run into an
              infinite loop. If 'validation_steps' is specified and only part of
              the dataset will be consumed, the evaluation will start from the
              beginning of the dataset at each epoch. This ensures that the same
              validation samples are used every time.
          validation_batch_size: Integer or `None`.
              Number of samples per validation batch.
              If unspecified, will default to `batch_size`.
              Do not specify the `validation_batch_size` if your data is in the
              form of datasets, generators, or `keras.utils.Sequence` instances
              (since they generate batches).
          validation_freq: Only relevant if validation data is provided. Integer
              or `collections.abc.Container` instance (e.g. list, tuple, etc.).
              If an integer, specifies how many training epochs to run before a
              new validation run is performed, e.g. `validation_freq=2` runs
              validation every 2 epochs. If a Container, specifies the epochs on
              which to run validation, e.g. `validation_freq=[1, 2, 10]` runs
              validation at the end of the 1st, 2nd, and 10th epochs.
          max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
              input only. Maximum size for the generator queue.
              If unspecified, `max_queue_size` will default to 10.
          workers: Integer. Used for generator or `keras.utils.Sequence` input
              only. Maximum number of processes to spin up
              when using process-based threading. If unspecified, `workers`
              will default to 1. If 0, will execute the generator on the main
              thread.
          use_multiprocessing: Boolean. Used for generator or
              `keras.utils.Sequence` input only. If `True`, use process-based
              threading. If unspecified, `use_multiprocessing` will default to
              `False`. Note that because this implementation relies on
              multiprocessing, you should not pass non-picklable arguments to
              the generator as they can't be passed easily to children processes.
  
      Unpacking behavior for iterator-like inputs:
          A common pattern is to pass a tf.data.Dataset, generator, or
        tf.keras.utils.Sequence to the `x` argument of fit, which will in fact
        yield not only features (x) but optionally targets (y) and sample weights.
        Keras requires that the output of such iterator-likes be unambiguous. The
        iterator should return a tuple of length 1, 2, or 3, where the optional
        second and third elements will be used for y and sample_weight
        respectively. Any other type provided will be wrapped in a length one
        tuple, effectively treating everything as 'x'. When yielding dicts, they
        should still adhere to the top-level tuple structure.
        e.g. `({"x0": x0, "x1": x1}, y)`. Keras will not attempt to separate
        features, targets, and weights from the keys of a single dict.
          A notable unsupported data type is the namedtuple. The reason is that
        it behaves like both an ordered datatype (tuple) and a mapping
        datatype (dict). So given a namedtuple of the form:
            `namedtuple("example_tuple", ["y", "x"])`
        it is ambiguous whether to reverse the order of the elements when
        interpreting the value. Even worse is a tuple of the form:
            `namedtuple("other_tuple", ["x", "y", "z"])`
        where it is unclear if the tuple was intended to be unpacked into x, y,
        and sample_weight or passed through as a single element to `x`. As a
        result the data processing code will simply raise a ValueError if it
        encounters a namedtuple. (Along with instructions to remedy the issue.)
  
      Returns:
          A `History` object. Its `History.history` attribute is
          a record of training loss values and metrics values
          at successive epochs, as well as validation loss values
          and validation metrics values (if applicable).
  
      Raises:
          RuntimeError: 1. If the model was never compiled or,
          2. If `model.fit` is  wrapped in `tf.function`.
  
          ValueError: In case of mismatch between the provided input data
              and what the model expects or when the input data is empty.
      """
      base_layer.keras_api_gauge.get_cell('fit').set(True)
      # Legacy graph support is contained in `training_v1.Model`.
      version_utils.disallow_legacy_graph('Model', 'fit')
      self._assert_compile_was_called()
      self._check_call_args('fit')
      _disallow_inside_tf_function('fit')
  
      if validation_split:
        # Create the validation data using the training data. Only supported for
        # `Tensor` and `NumPy` input.
        (x, y, sample_weight), validation_data = (
            data_adapter.train_validation_split(
                (x, y, sample_weight), validation_split=validation_split))
  
      if validation_data:
        val_x, val_y, val_sample_weight = (
            data_adapter.unpack_x_y_sample_weight(validation_data))
  
      with self.distribute_strategy.scope(), \
           training_utils.RespectCompiledTrainableState(self):
        # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
        data_handler = data_adapter.DataHandler(
            x=x,
            y=y,
            sample_weight=sample_weight,
            batch_size=batch_size,
            steps_per_epoch=steps_per_epoch,
            initial_epoch=initial_epoch,
            epochs=epochs,
            shuffle=shuffle,
            class_weight=class_weight,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
            model=self,
            steps_per_execution=self._steps_per_execution)
  
        # Container that configures and calls `tf.keras.Callback`s.
        if not isinstance(callbacks, callbacks_module.CallbackList):
          callbacks = callbacks_module.CallbackList(
              callbacks,
              add_history=True,
              add_progbar=verbose != 0,
              model=self,
              verbose=verbose,
              epochs=epochs,
              steps=data_handler.inferred_steps)
  
        self.stop_training = False
        self.train_function = self.make_train_function()
        self._train_counter.assign(0)
        callbacks.on_train_begin()
        training_logs = None
        # Handle fault-tolerance for multi-worker.
        # TODO(omalleyt): Fix the ordering issues that mean this has to
        # happen after `callbacks.on_train_begin`.
        data_handler._initial_epoch = (  # pylint: disable=protected-access
            self._maybe_load_initial_epoch_from_ckpt(initial_epoch))
        logs = None
        for epoch, iterator in data_handler.enumerate_epochs():
          self.reset_metrics()
          callbacks.on_epoch_begin(epoch)
          with data_handler.catch_stop_iteration():
            for step in data_handler.steps():
              with tf.profiler.experimental.Trace(
                  'train',
                  epoch_num=epoch,
                  step_num=step,
                  batch_size=batch_size,
                  _r=1):
                callbacks.on_train_batch_begin(step)
                tmp_logs = self.train_function(iterator)
                if data_handler.should_sync:
                  context.async_wait()
                logs = tmp_logs  # No error, now safe to assign to logs.
                end_step = step + data_handler.step_increment
                callbacks.on_train_batch_end(end_step, logs)
                if self.stop_training:
                  break
  
          if logs is None:
            raise ValueError('Expect x to be a non-empty array or dataset.')
          epoch_logs = copy.copy(logs)
  
          # Run validation.
          if validation_data and self._should_eval(epoch, validation_freq):
            # Create data_handler for evaluation and cache it.
            if getattr(self, '_eval_data_handler', None) is None:
              self._fit_frame = tf_inspect.currentframe()
              self._eval_data_handler = data_adapter.DataHandler(
                  x=val_x,
                  y=val_y,
                  sample_weight=val_sample_weight,
                  batch_size=validation_batch_size or batch_size,
                  steps_per_epoch=validation_steps,
                  initial_epoch=0,
                  epochs=1,
                  max_queue_size=max_queue_size,
                  workers=workers,
                  use_multiprocessing=use_multiprocessing,
                  model=self,
                  steps_per_execution=self._steps_per_execution)
            val_logs = self.evaluate(
                x=val_x,
                y=val_y,
                sample_weight=val_sample_weight,
                batch_size=validation_batch_size or batch_size,
                steps=validation_steps,
                callbacks=callbacks,
                max_queue_size=max_queue_size,
                workers=workers,
                use_multiprocessing=use_multiprocessing,
                return_dict=True)
            val_logs = {'val_' + name: val for name, val in val_logs.items()}
            epoch_logs.update(val_logs)
  
          callbacks.on_epoch_end(epoch, epoch_logs)
          training_logs = epoch_logs
          if self.stop_training:
            break
  
        # If eval data_hanlder exists, delete it after all epochs are done.
        if getattr(self, '_eval_data_handler', None) is not None:
          del self._eval_data_handler
          del self._fit_frame
        callbacks.on_train_end(logs=training_logs)
        return self.history
  
    def test_step(self, data):
      """The logic for one evaluation step.
  
      This method can be overridden to support custom evaluation logic.
      This method is called by `Model.make_test_function`.
  
      This function should contain the mathematical logic for one step of
      evaluation.
      This typically includes the forward pass, loss calculation, and metrics
      updates.
  
      Configuration details for *how* this logic is run (e.g. `tf.function` and
      `tf.distribute.Strategy` settings), should be left to
      `Model.make_test_function`, which can also be overridden.
  
      Args:
        data: A nested structure of `Tensor`s.
  
      Returns:
        A `dict` containing values that will be passed to
        `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
        values of the `Model`'s metrics are returned.
      """
      data = data_adapter.expand_1d(data)
      x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)
  
      y_pred = self(x, training=False)
      # Updates stateful loss metrics.
      self.compiled_loss(
          y, y_pred, sample_weight, regularization_losses=self.losses)
  
      self.compiled_metrics.update_state(y, y_pred, sample_weight)
      return {m.name: m.result() for m in self.metrics}
  
    def make_test_function(self):
      """Creates a function that executes one step of evaluation.
  
      This method can be overridden to support custom evaluation logic.
      This method is called by `Model.evaluate` and `Model.test_on_batch`.
  
      Typically, this method directly controls `tf.function` and
      `tf.distribute.Strategy` settings, and delegates the actual evaluation
      logic to `Model.test_step`.
  
      This function is cached the first time `Model.evaluate` or
      `Model.test_on_batch` is called. The cache is cleared whenever
      `Model.compile` is called.
  
      Returns:
        Function. The function created by this method should accept a
        `tf.data.Iterator`, and return a `dict` containing values that will
        be passed to `tf.keras.Callbacks.on_test_batch_end`.
      """
      if self.test_function is not None:
        return self.test_function
  
      def step_function(model, iterator):
        """Runs a single evaluation step."""
  
        def run_step(data):
          outputs = model.test_step(data)
          # Ensure counter is updated only if `test_step` succeeds.
          with tf.control_dependencies(_minimum_control_deps(outputs)):
            model._test_counter.assign_add(1)  # pylint: disable=protected-access
          return outputs
  
        data = next(iterator)
        outputs = model.distribute_strategy.run(run_step, args=(data,))
        outputs = reduce_per_replica(
            outputs, self.distribute_strategy, reduction='first')
        return outputs
  
      if self._steps_per_execution.numpy().item() == 1:
  
        def test_function(iterator):
          """Runs an evaluation execution with one step."""
          return step_function(self, iterator)
  
      else:
  
        def test_function(iterator):
          """Runs an evaluation execution with multiple steps."""
          for _ in tf.range(self._steps_per_execution):
            outputs = step_function(self, iterator)
          return outputs
  
      if not self.run_eagerly:
        test_function = tf.function(
            test_function, experimental_relax_shapes=True)
  
      self.test_function = test_function
      return self.test_function
  
    def evaluate(self,
                 x=None,
                 y=None,
                 batch_size=None,
                 verbose=1,
                 sample_weight=None,
                 steps=None,
                 callbacks=None,
                 max_queue_size=10,
                 workers=1,
                 use_multiprocessing=False,
                 return_dict=False):
      """Returns the loss value & metrics values for the model in test mode.
  
      Computation is done in batches (see the `batch_size` arg.)
  
      Args:
          x: Input data. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A dict mapping input names to the corresponding array/tensors,
              if the model has named inputs.
            - A `tf.data` dataset. Should return a tuple
              of either `(inputs, targets)` or
              `(inputs, targets, sample_weights)`.
            - A generator or `keras.utils.Sequence` returning `(inputs, targets)`
              or `(inputs, targets, sample_weights)`.
            A more detailed description of unpacking behavior for iterator types
            (Dataset, generator, Sequence) is given in the `Unpacking behavior
            for iterator-like inputs` section of `Model.fit`.
          y: Target data. Like the input data `x`, it could be either Numpy
            array(s) or TensorFlow tensor(s). It should be consistent with `x`
            (you cannot have Numpy inputs and tensor targets, or inversely). If
            `x` is a dataset, generator or `keras.utils.Sequence` instance, `y`
            should not be specified (since targets will be obtained from the
            iterator/dataset).
          batch_size: Integer or `None`. Number of samples per batch of
            computation. If unspecified, `batch_size` will default to 32. Do not
            specify the `batch_size` if your data is in the form of a dataset,
            generators, or `keras.utils.Sequence` instances (since they generate
            batches).
          verbose: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
          sample_weight: Optional Numpy array of weights for the test samples,
            used for weighting the loss function. You can either pass a flat (1D)
            Numpy array with the same length as the input samples
              (1:1 mapping between weights and samples), or in the case of
                temporal data, you can pass a 2D array with shape `(samples,
                sequence_length)`, to apply a different weight to every timestep
                of every sample. This argument is not supported when `x` is a
                dataset, instead pass sample weights as the third element of `x`.
          steps: Integer or `None`. Total number of steps (batches of samples)
            before declaring the evaluation round finished. Ignored with the
            default value of `None`. If x is a `tf.data` dataset and `steps` is
            None, 'evaluate' will run until the dataset is exhausted. This
            argument is not supported with array inputs.
          callbacks: List of `keras.callbacks.Callback` instances. List of
            callbacks to apply during evaluation. See
            [callbacks](/api_docs/python/tf/keras/callbacks).
          max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
            input only. Maximum size for the generator queue. If unspecified,
            `max_queue_size` will default to 10.
          workers: Integer. Used for generator or `keras.utils.Sequence` input
            only. Maximum number of processes to spin up when using process-based
            threading. If unspecified, `workers` will default to 1. If 0, will
            execute the generator on the main thread.
          use_multiprocessing: Boolean. Used for generator or
            `keras.utils.Sequence` input only. If `True`, use process-based
            threading. If unspecified, `use_multiprocessing` will default to
            `False`. Note that because this implementation relies on
            multiprocessing, you should not pass non-picklable arguments to the
            generator as they can't be passed easily to children processes.
          return_dict: If `True`, loss and metric results are returned as a dict,
            with each key being the name of the metric. If `False`, they are
            returned as a list.
  
      See the discussion of `Unpacking behavior for iterator-like inputs` for
      `Model.fit`.
  
      Returns:
          Scalar test loss (if the model has a single output and no metrics)
          or list of scalars (if the model has multiple outputs
          and/or metrics). The attribute `model.metrics_names` will give you
          the display labels for the scalar outputs.
  
      Raises:
          RuntimeError: If `model.evaluate` is wrapped in `tf.function`.
          ValueError: in case of invalid arguments.
      """
      base_layer.keras_api_gauge.get_cell('evaluate').set(True)
      version_utils.disallow_legacy_graph('Model', 'evaluate')
      self._assert_compile_was_called()
      self._check_call_args('evaluate')
      _disallow_inside_tf_function('evaluate')
  
      with self.distribute_strategy.scope():
        # Use cached evaluation data only when it's called in `Model.fit`
        if (getattr(self, '_fit_frame', None) is not None
            and tf_inspect.currentframe().f_back is self._fit_frame
            and getattr(self, '_eval_data_handler', None) is not None):
          data_handler = self._eval_data_handler
        else:
          # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
          data_handler = data_adapter.DataHandler(
              x=x,
              y=y,
              sample_weight=sample_weight,
              batch_size=batch_size,
              steps_per_epoch=steps,
              initial_epoch=0,
              epochs=1,
              max_queue_size=max_queue_size,
              workers=workers,
              use_multiprocessing=use_multiprocessing,
              model=self,
              steps_per_execution=self._steps_per_execution)
  
        # Container that configures and calls `tf.keras.Callback`s.
        if not isinstance(callbacks, callbacks_module.CallbackList):
          callbacks = callbacks_module.CallbackList(
              callbacks,
              add_history=True,
              add_progbar=verbose != 0,
              model=self,
              verbose=verbose,
              epochs=1,
              steps=data_handler.inferred_steps)
  
        logs = {}
        self.test_function = self.make_test_function()
        self._test_counter.assign(0)
        callbacks.on_test_begin()
        for _, iterator in data_handler.enumerate_epochs():  # Single epoch.
          self.reset_metrics()
          with data_handler.catch_stop_iteration():
            for step in data_handler.steps():
              with tf.profiler.experimental.Trace('test', step_num=step, _r=1):
                callbacks.on_test_batch_begin(step)
                tmp_logs = self.test_function(iterator)
                if data_handler.should_sync:
                  context.async_wait()
                logs = tmp_logs  # No error, now safe to assign to logs.
                end_step = step + data_handler.step_increment
                callbacks.on_test_batch_end(end_step, logs)
        logs = tf_utils.to_numpy_or_python_type(logs)
        callbacks.on_test_end(logs=logs)
  
        if return_dict:
          return logs
        else:
          results = []
          for name in self.metrics_names:
            if name in logs:
              results.append(logs[name])
          for key in sorted(logs.keys()):
            if key not in self.metrics_names:
              results.append(logs[key])
          if len(results) == 1:
            return results[0]
          return results
  
    def predict_step(self, data):
      """The logic for one inference step.
  
      This method can be overridden to support custom inference logic.
      This method is called by `Model.make_predict_function`.
  
      This method should contain the mathematical logic for one step of inference.
      This typically includes the forward pass.
  
      Configuration details for *how* this logic is run (e.g. `tf.function` and
      `tf.distribute.Strategy` settings), should be left to
      `Model.make_predict_function`, which can also be overridden.
  
      Args:
        data: A nested structure of `Tensor`s.
  
      Returns:
        The result of one inference step, typically the output of calling the
        `Model` on data.
      """
      data = data_adapter.expand_1d(data)
      x, _, _ = data_adapter.unpack_x_y_sample_weight(data)
      return self(x, training=False)
  
    def make_predict_function(self):
      """Creates a function that executes one step of inference.
  
      This method can be overridden to support custom inference logic.
      This method is called by `Model.predict` and `Model.predict_on_batch`.
  
      Typically, this method directly controls `tf.function` and
      `tf.distribute.Strategy` settings, and delegates the actual evaluation
      logic to `Model.predict_step`.
  
      This function is cached the first time `Model.predict` or
      `Model.predict_on_batch` is called. The cache is cleared whenever
      `Model.compile` is called.
  
      Returns:
        Function. The function created by this method should accept a
        `tf.data.Iterator`, and return the outputs of the `Model`.
      """
      if self.predict_function is not None:
        return self.predict_function
  
      def step_function(model, iterator):
        """Runs a single evaluation step."""
  
        def run_step(data):
          outputs = model.predict_step(data)
          # Ensure counter is updated only if `test_step` succeeds.
          with tf.control_dependencies(_minimum_control_deps(outputs)):
            model._predict_counter.assign_add(1)  # pylint: disable=protected-access
          return outputs
  
        data = next(iterator)
        outputs = model.distribute_strategy.run(run_step, args=(data,))
        outputs = reduce_per_replica(
            outputs, self.distribute_strategy, reduction='concat')
        return outputs
  
      if (self._steps_per_execution is None or
          self._steps_per_execution.numpy().item() == 1):
  
        def predict_function(iterator):
          """Runs an evaluation execution with one step."""
          return step_function(self, iterator)
  
      else:
  
        def predict_function(iterator):
          """Runs an evaluation execution with multiple steps."""
          outputs = step_function(self, iterator)
          for _ in tf.range(self._steps_per_execution - 1):
            tf.autograph.experimental.set_loop_options(
                shape_invariants=[(
                    t, tf_utils.get_tensor_spec(t, dynamic_batch=True).shape)
                                  for t in tf.nest.flatten(outputs)])
            step_outputs = step_function(self, iterator)
            outputs = tf.nest.map_structure(lambda t1, t2: concat([t1, t2]), outputs,
                                         step_outputs)
          return outputs
  
      if not self.run_eagerly:
        predict_function = tf.function(
            predict_function, experimental_relax_shapes=True)
  
      self.predict_function = predict_function
      return self.predict_function
  
    def predict(self,
                x,
                batch_size=None,
                verbose=0,
                steps=None,
                callbacks=None,
                max_queue_size=10,
                workers=1,
                use_multiprocessing=False):
      """Generates output predictions for the input samples.
  
      Computation is done in batches. This method is designed for performance in
      large scale inputs. For small amount of inputs that fit in one batch,
      directly using `__call__` is recommended for faster execution, e.g.,
      `model(x)`, or `model(x, training=False)` if you have layers such as
      `tf.keras.layers.BatchNormalization` that behaves differently during
      inference. Also, note the fact that test loss is not affected by
      regularization layers like noise and dropout.
  
      Args:
          x: Input samples. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A `tf.data` dataset.
            - A generator or `keras.utils.Sequence` instance.
            A more detailed description of unpacking behavior for iterator types
            (Dataset, generator, Sequence) is given in the `Unpacking behavior
            for iterator-like inputs` section of `Model.fit`.
          batch_size: Integer or `None`.
              Number of samples per batch.
              If unspecified, `batch_size` will default to 32.
              Do not specify the `batch_size` if your data is in the
              form of dataset, generators, or `keras.utils.Sequence` instances
              (since they generate batches).
          verbose: Verbosity mode, 0 or 1.
          steps: Total number of steps (batches of samples)
              before declaring the prediction round finished.
              Ignored with the default value of `None`. If x is a `tf.data`
              dataset and `steps` is None, `predict` will
              run until the input dataset is exhausted.
          callbacks: List of `keras.callbacks.Callback` instances.
              List of callbacks to apply during prediction.
              See [callbacks](/api_docs/python/tf/keras/callbacks).
          max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
              input only. Maximum size for the generator queue.
              If unspecified, `max_queue_size` will default to 10.
          workers: Integer. Used for generator or `keras.utils.Sequence` input
              only. Maximum number of processes to spin up when using
              process-based threading. If unspecified, `workers` will default
              to 1. If 0, will execute the generator on the main thread.
          use_multiprocessing: Boolean. Used for generator or
              `keras.utils.Sequence` input only. If `True`, use process-based
              threading. If unspecified, `use_multiprocessing` will default to
              `False`. Note that because this implementation relies on
              multiprocessing, you should not pass non-picklable arguments to
              the generator as they can't be passed easily to children processes.
  
      See the discussion of `Unpacking behavior for iterator-like inputs` for
      `Model.fit`. Note that Model.predict uses the same interpretation rules as
      `Model.fit` and `Model.evaluate`, so inputs must be unambiguous for all
      three methods.
  
      Returns:
          Numpy array(s) of predictions.
  
      Raises:
          RuntimeError: If `model.predict` is wrapped in `tf.function`.
          ValueError: In case of mismatch between the provided
              input data and the model's expectations,
              or in case a stateful model receives a number of samples
              that is not a multiple of the batch size.
      """
      base_layer.keras_api_gauge.get_cell('predict').set(True)
      version_utils.disallow_legacy_graph('Model', 'predict')
      self._check_call_args('predict')
      _disallow_inside_tf_function('predict')
  
      outputs = None
      with self.distribute_strategy.scope():
        # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
        dataset_types = (tf.compat.v1.data.Dataset, tf.data.Dataset)
        if (self._in_multi_worker_mode() or _is_tpu_multi_host(
            self.distribute_strategy)) and isinstance(x, dataset_types):
          try:
            options = tf.data.Options()
            data_option = tf.data.experimental.AutoShardPolicy.DATA
            options.experimental_distribute.auto_shard_policy = data_option
            x = x.with_options(options)
          except ValueError:
            warnings.warn('Using Model.predict with '
                          'MultiWorkerDistributionStrategy or TPUStrategy and '
                          'AutoShardPolicy.FILE might lead to out-of-order result'
                          '. Consider setting it to AutoShardPolicy.DATA.')
  
        data_handler = data_adapter.DataHandler(
            x=x,
            batch_size=batch_size,
            steps_per_epoch=steps,
            initial_epoch=0,
            epochs=1,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
            model=self,
            steps_per_execution=self._steps_per_execution)
  
        # Container that configures and calls `tf.keras.Callback`s.
        if not isinstance(callbacks, callbacks_module.CallbackList):
          callbacks = callbacks_module.CallbackList(
              callbacks,
              add_history=True,
              add_progbar=verbose != 0,
              model=self,
              verbose=verbose,
              epochs=1,
              steps=data_handler.inferred_steps)
  
        self.predict_function = self.make_predict_function()
        self._predict_counter.assign(0)
        callbacks.on_predict_begin()
        batch_outputs = None
        for _, iterator in data_handler.enumerate_epochs():  # Single epoch.
          with data_handler.catch_stop_iteration():
            for step in data_handler.steps():
              callbacks.on_predict_batch_begin(step)
              tmp_batch_outputs = self.predict_function(iterator)
              if data_handler.should_sync:
                context.async_wait()
              batch_outputs = tmp_batch_outputs  # No error, now safe to assign.
              if outputs is None:
                outputs = tf.nest.map_structure(lambda batch_output: [batch_output],
                                             batch_outputs)
              else:
                tf.__internal__.nest.map_structure_up_to(
                    batch_outputs,
                    lambda output, batch_output: output.append(batch_output),
                    outputs, batch_outputs)
              end_step = step + data_handler.step_increment
              callbacks.on_predict_batch_end(end_step, {'outputs': batch_outputs})
        if batch_outputs is None:
          raise ValueError('Expect x to be a non-empty array or dataset.')
        callbacks.on_predict_end()
      all_outputs = tf.__internal__.nest.map_structure_up_to(batch_outputs, concat, outputs)
      return tf_utils.to_numpy_or_python_type(all_outputs)
  
    def reset_metrics(self):
      """Resets the state of all the metrics in the model.
  
      Examples:
  
      >>> inputs = tf.keras.layers.Input(shape=(3,))
      >>> outputs = tf.keras.layers.Dense(2)(inputs)
      >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
      >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
  
      >>> x = np.random.random((2, 3))
      >>> y = np.random.randint(0, 2, (2, 2))
      >>> _ = model.fit(x, y, verbose=0)
      >>> assert all(float(m.result()) for m in model.metrics)
  
      >>> model.reset_metrics()
      >>> assert all(float(m.result()) == 0 for m in model.metrics)
  
      """
      for m in self.metrics:
        m.reset_states()
  
    def train_on_batch(self,
                       x,
                       y=None,
                       sample_weight=None,
                       class_weight=None,
                       reset_metrics=True,
                       return_dict=False):
      """Runs a single gradient update on a single batch of data.
  
      Args:
          x: Input data. It could be:
            - A Numpy array (or array-like), or a list of arrays
                (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
                (in case the model has multiple inputs).
            - A dict mapping input names to the corresponding array/tensors,
                if the model has named inputs.
          y: Target data. Like the input data `x`, it could be either Numpy
            array(s) or TensorFlow tensor(s). It should be consistent with `x`
            (you cannot have Numpy inputs and tensor targets, or inversely).
          sample_weight: Optional array of the same length as x, containing
            weights to apply to the model's loss for each sample. In the case of
            temporal data, you can pass a 2D array with shape (samples,
            sequence_length), to apply a different weight to every timestep of
            every sample.
          class_weight: Optional dictionary mapping class indices (integers) to a
            weight (float) to apply to the model's loss for the samples from this
            class during training. This can be useful to tell the model to "pay
            more attention" to samples from an under-represented class.
          reset_metrics: If `True`, the metrics returned will be only for this
            batch. If `False`, the metrics will be statefully accumulated across
            batches.
          return_dict: If `True`, loss and metric results are returned as a dict,
            with each key being the name of the metric. If `False`, they are
            returned as a list.
  
      Returns:
          Scalar training loss
          (if the model has a single output and no metrics)
          or list of scalars (if the model has multiple outputs
          and/or metrics). The attribute `model.metrics_names` will give you
          the display labels for the scalar outputs.
  
      Raises:
        RuntimeError: If `model.train_on_batch` is wrapped in `tf.function`.
        ValueError: In case of invalid user-provided arguments.
      """
      self._assert_compile_was_called()
      self._check_call_args('train_on_batch')
      _disallow_inside_tf_function('train_on_batch')
      with self.distribute_strategy.scope(), \
           training_utils.RespectCompiledTrainableState(self):
        iterator = data_adapter.single_batch_iterator(self.distribute_strategy, x,
                                                      y, sample_weight,
                                                      class_weight)
        self.train_function = self.make_train_function()
        logs = self.train_function(iterator)
  
      if reset_metrics:
        self.reset_metrics()
      logs = tf_utils.to_numpy_or_python_type(logs)
      if return_dict:
        return logs
      else:
        results = [logs.get(name, None) for name in self.metrics_names]
        if len(results) == 1:
          return results[0]
        return results
  
    def test_on_batch(self,
                      x,
                      y=None,
                      sample_weight=None,
                      reset_metrics=True,
                      return_dict=False):
      """Test the model on a single batch of samples.
  
      Args:
          x: Input data. It could be: - A Numpy array (or array-like), or a list
            of arrays (in case the model has multiple inputs). - A TensorFlow
            tensor, or a list of tensors (in case the model has multiple inputs).
            - A dict mapping input names to the corresponding array/tensors, if
            the model has named inputs.
          y: Target data. Like the input data `x`, it could be either Numpy
            array(s) or TensorFlow tensor(s). It should be consistent with `x`
            (you cannot have Numpy inputs and tensor targets, or inversely).
          sample_weight: Optional array of the same length as x, containing
            weights to apply to the model's loss for each sample. In the case of
            temporal data, you can pass a 2D array with shape (samples,
            sequence_length), to apply a different weight to every timestep of
            every sample.
          reset_metrics: If `True`, the metrics returned will be only for this
            batch. If `False`, the metrics will be statefully accumulated across
            batches.
          return_dict: If `True`, loss and metric results are returned as a dict,
            with each key being the name of the metric. If `False`, they are
            returned as a list.
  
      Returns:
          Scalar test loss (if the model has a single output and no metrics)
          or list of scalars (if the model has multiple outputs
          and/or metrics). The attribute `model.metrics_names` will give you
          the display labels for the scalar outputs.
  
      Raises:
          RuntimeError: If `model.test_on_batch` is wrapped in `tf.function`.
          ValueError: In case of invalid user-provided arguments.
      """
      self._assert_compile_was_called()
      self._check_call_args('test_on_batch')
      _disallow_inside_tf_function('test_on_batch')
      with self.distribute_strategy.scope():
        iterator = data_adapter.single_batch_iterator(self.distribute_strategy, x,
                                                      y, sample_weight)
        self.test_function = self.make_test_function()
        logs = self.test_function(iterator)
  
      if reset_metrics:
        self.reset_metrics()
      logs = tf_utils.to_numpy_or_python_type(logs)
      if return_dict:
        return logs
      else:
        results = [logs.get(name, None) for name in self.metrics_names]
        if len(results) == 1:
          return results[0]
        return results
  
    def predict_on_batch(self, x):
      """Returns predictions for a single batch of samples.
  
      Args:
          x: Input data. It could be: - A Numpy array (or array-like), or a list
            of arrays (in case the model has multiple inputs). - A TensorFlow
            tensor, or a list of tensors (in case the model has multiple inputs).
  
      Returns:
          Numpy array(s) of predictions.
  
      Raises:
          RuntimeError: If `model.predict_on_batch` is wrapped in `tf.function`.
          ValueError: In case of mismatch between given number of inputs and
            expectations of the model.
      """
      self._check_call_args('predict_on_batch')
      _disallow_inside_tf_function('predict_on_batch')
      with self.distribute_strategy.scope():
        iterator = data_adapter.single_batch_iterator(self.distribute_strategy, x)
        self.predict_function = self.make_predict_function()
        outputs = self.predict_function(iterator)
      return tf_utils.to_numpy_or_python_type(outputs)
  
    def fit_generator(self,
                      generator,
                      steps_per_epoch=None,
                      epochs=1,
                      verbose=1,
                      callbacks=None,
                      validation_data=None,
                      validation_steps=None,
                      validation_freq=1,
                      class_weight=None,
                      max_queue_size=10,
                      workers=1,
                      use_multiprocessing=False,
                      shuffle=True,
                      initial_epoch=0):
      """Fits the model on data yielded batch-by-batch by a Python generator.
  
      DEPRECATED:
        `Model.fit` now supports generators, so there is no longer any need to use
        this endpoint.
      """
      warnings.warn('`Model.fit_generator` is deprecated and '
                    'will be removed in a future version. '
                    'Please use `Model.fit`, which supports generators.')
      return self.fit(
          generator,
          steps_per_epoch=steps_per_epoch,
          epochs=epochs,
          verbose=verbose,
          callbacks=callbacks,
          validation_data=validation_data,
          validation_steps=validation_steps,
          validation_freq=validation_freq,
          class_weight=class_weight,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          shuffle=shuffle,
          initial_epoch=initial_epoch)
  
    def evaluate_generator(self,
                           generator,
                           steps=None,
                           callbacks=None,
                           max_queue_size=10,
                           workers=1,
                           use_multiprocessing=False,
                           verbose=0):
      """Evaluates the model on a data generator.
  
      DEPRECATED:
        `Model.evaluate` now supports generators, so there is no longer any need
        to use this endpoint.
      """
      warnings.warn('`Model.evaluate_generator` is deprecated and '
                    'will be removed in a future version. '
                    'Please use `Model.evaluate`, which supports generators.')
      self._check_call_args('evaluate_generator')
  
      return self.evaluate(
          generator,
          steps=steps,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          verbose=verbose,
          callbacks=callbacks)
  
    def predict_generator(self,
                          generator,
                          steps=None,
                          callbacks=None,
                          max_queue_size=10,
                          workers=1,
                          use_multiprocessing=False,
                          verbose=0):
      """Generates predictions for the input samples from a data generator.
  
      DEPRECATED:
        `Model.predict` now supports generators, so there is no longer any need
        to use this endpoint.
      """
      warnings.warn('`Model.predict_generator` is deprecated and '
                    'will be removed in a future version. '
                    'Please use `Model.predict`, which supports generators.')
      return self.predict(
          generator,
          steps=steps,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          verbose=verbose,
          callbacks=callbacks)
  
    ######################################################################
    # Functions below are not training related. They are for model weights
    # tracking, save/load, serialization, etc.
    ######################################################################
  
    @property
    def trainable_weights(self):
      self._assert_weights_created()
      if not self._trainable:
        return []
      trainable_variables = []
      for trackable_obj in self._self_tracked_trackables:
        trainable_variables += trackable_obj.trainable_variables
      trainable_variables += self._trainable_weights
      return self._dedup_weights(trainable_variables)
  
    @property
    def non_trainable_weights(self):
      self._assert_weights_created()
      non_trainable_variables = []
      for trackable_obj in self._self_tracked_trackables:
        non_trainable_variables += trackable_obj.non_trainable_variables
  
      if not self._trainable:
        # Return order is all trainable vars, then all non-trainable vars.
        trainable_variables = []
        for trackable_obj in self._self_tracked_trackables:
          trainable_variables += trackable_obj.trainable_variables
  
        non_trainable_variables = (
            trainable_variables + self._trainable_weights +
            non_trainable_variables + self._non_trainable_weights)
      else:
        non_trainable_variables = (
            non_trainable_variables + self._non_trainable_weights)
  
      return self._dedup_weights(non_trainable_variables)
  
    def get_weights(self):
      """Retrieves the weights of the model.
  
      Returns:
          A flat list of Numpy arrays.
      """
      with self.distribute_strategy.scope():
        return super(Model, self).get_weights()
  
    def save(self,
             filepath,
             overwrite=True,
             include_optimizer=True,
             save_format=None,
             signatures=None,
             options=None,
             save_traces=True):
      # pylint: disable=line-too-long
      """Saves the model to Tensorflow SavedModel or a single HDF5 file.
  
      Please see `tf.keras.models.save_model` or the
      [Serialization and Saving guide](https://keras.io/guides/serialization_and_saving/)
      for details.
  
      Args:
          filepath: String, PathLike, path to SavedModel or H5 file to save the
              model.
          overwrite: Whether to silently overwrite any existing file at the
              target location, or provide the user with a manual prompt.
          include_optimizer: If True, save optimizer's state together.
          save_format: Either `'tf'` or `'h5'`, indicating whether to save the
              model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X,
              and 'h5' in TF 1.X.
          signatures: Signatures to save with the SavedModel. Applicable to the
              'tf' format only. Please see the `signatures` argument in
              `tf.saved_model.save` for details.
          options: (only applies to SavedModel format)
              `tf.saved_model.SaveOptions` object that specifies options for
              saving to SavedModel.
          save_traces: (only applies to SavedModel format) When enabled, the
              SavedModel will store the function traces for each layer. This
              can be disabled, so that only the configs of each layer are stored.
              Defaults to `True`. Disabling this will decrease serialization time
              and reduce file size, but it requires that all custom layers/models
              implement a `get_config()` method.
  
      Example:
  
      ```python
      from keras.models import load_model
  
      model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
      del model  # deletes the existing model
  
      # returns a compiled model
      # identical to the previous one
      model = load_model('my_model.h5')
      ```
      """
      # pylint: enable=line-too-long
      save.save_model(self, filepath, overwrite, include_optimizer, save_format,
                      signatures, options, save_traces)
  
    def save_weights(self,
                     filepath,
                     overwrite=True,
                     save_format=None,
                     options=None):
      """Saves all layer weights.
  
      Either saves in HDF5 or in TensorFlow format based on the `save_format`
      argument.
  
      When saving in HDF5 format, the weight file has:
        - `layer_names` (attribute), a list of strings
            (ordered names of model layers).
        - For every layer, a `group` named `layer.name`
            - For every such layer group, a group attribute `weight_names`,
                a list of strings
                (ordered names of weights tensor of the layer).
            - For every weight in the layer, a dataset
                storing the weight value, named after the weight tensor.
  
      When saving in TensorFlow format, all objects referenced by the network are
      saved in the same format as `tf.train.Checkpoint`, including any `Layer`
      instances or `Optimizer` instances assigned to object attributes. For
      networks constructed from inputs and outputs using `tf.keras.Model(inputs,
      outputs)`, `Layer` instances used by the network are tracked/saved
      automatically. For user-defined classes which inherit from `tf.keras.Model`,
      `Layer` instances must be assigned to object attributes, typically in the
      constructor. See the documentation of `tf.train.Checkpoint` and
      `tf.keras.Model` for details.
  
      While the formats are the same, do not mix `save_weights` and
      `tf.train.Checkpoint`. Checkpoints saved by `Model.save_weights` should be
      loaded using `Model.load_weights`. Checkpoints saved using
      `tf.train.Checkpoint.save` should be restored using the corresponding
      `tf.train.Checkpoint.restore`. Prefer `tf.train.Checkpoint` over
      `save_weights` for training checkpoints.
  
      The TensorFlow format matches objects and variables by starting at a root
      object, `self` for `save_weights`, and greedily matching attribute
      names. For `Model.save` this is the `Model`, and for `Checkpoint.save` this
      is the `Checkpoint` even if the `Checkpoint` has a model attached. This
      means saving a `tf.keras.Model` using `save_weights` and loading into a
      `tf.train.Checkpoint` with a `Model` attached (or vice versa) will not match
      the `Model`'s variables. See the [guide to training
      checkpoints](https://www.tensorflow.org/guide/checkpoint) for details
      on the TensorFlow format.
  
      Args:
          filepath: String or PathLike, path to the file to save the weights to.
              When saving in TensorFlow format, this is the prefix used for
              checkpoint files (multiple files are generated). Note that the '.h5'
              suffix causes weights to be saved in HDF5 format.
          overwrite: Whether to silently overwrite any existing file at the
              target location, or provide the user with a manual prompt.
          save_format: Either 'tf' or 'h5'. A `filepath` ending in '.h5' or
              '.keras' will default to HDF5 if `save_format` is `None`. Otherwise
              `None` defaults to 'tf'.
          options: Optional `tf.train.CheckpointOptions` object that specifies
              options for saving weights.
  
      Raises:
          ImportError: If h5py is not available when attempting to save in HDF5
              format.
          ValueError: For invalid/unknown format arguments.
      """
      self._assert_weights_created()
      filepath = path_to_string(filepath)
      filepath_is_h5 = saving_utils.is_hdf5_filepath(filepath)
      if save_format is None:
        if filepath_is_h5:
          save_format = 'h5'
        else:
          save_format = 'tf'
      else:
        user_format = save_format.lower().strip()
        if user_format in ('tensorflow', 'tf'):
          save_format = 'tf'
        elif user_format in ('hdf5', 'h5', 'keras'):
          save_format = 'h5'
        else:
          raise ValueError(
              'Unknown format "%s". Was expecting one of {"tf", "h5"}.' % (
                  save_format,))
      if save_format == 'tf' and filepath_is_h5:
        raise ValueError(
            ('save_weights got save_format="tf"/"tensorflow", but the '
             'filepath ("%s") looks like an HDF5 file. Omit the ".h5"/".keras" '
             'when saving in TensorFlow format.')
            % filepath)
  
      if save_format == 'h5' and h5py is None:
        raise ImportError(
            '`save_weights` requires h5py when saving in hdf5.')
      if save_format == 'tf':
        check_filepath = filepath + '.index'
      else:
        check_filepath = filepath
      # If file exists and should not be overwritten:
      if not overwrite and os.path.isfile(check_filepath):
        proceed = ask_to_proceed_with_overwrite(check_filepath)
        if not proceed:
          return
      if save_format == 'h5':
        with h5py.File(filepath, 'w') as f:
          hdf5_format.save_weights_to_hdf5_group(f, self.layers)
      else:
        if tf.executing_eagerly():
          session = None
        else:
          session = backend.get_session()
        optimizer = getattr(self, 'optimizer', None)
        if (optimizer
            and not isinstance(optimizer, tf.__internal__.tracking.Trackable)):
          logging.warning(
              ('This model was compiled with a Keras optimizer (%s) but is being '
               'saved in TensorFlow format with `save_weights`. The model\'s '
               'weights will be saved, but unlike with TensorFlow optimizers in '
               'the TensorFlow format the optimizer\'s state will not be '
               'saved.\n\nConsider using a TensorFlow optimizer from `tf.train`.')
              % (optimizer,))
        self._trackable_saver.save(filepath, session=session, options=options)
        # Record this checkpoint so it's visible from tf.train.latest_checkpoint.
        checkpoint_management.update_checkpoint_state_internal(
            save_dir=os.path.dirname(filepath),
            model_checkpoint_path=filepath,
            save_relative_paths=True,
            all_model_checkpoint_paths=[filepath])
  
    def load_weights(self,
                     filepath,
                     by_name=False,
                     skip_mismatch=False,
                     options=None):
      """Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
  
      If `by_name` is False weights are loaded based on the network's
      topology. This means the architecture should be the same as when the weights
      were saved.  Note that layers that don't have weights are not taken into
      account in the topological ordering, so adding or removing layers is fine as
      long as they don't have weights.
  
      If `by_name` is True, weights are loaded into layers only if they share the
      same name. This is useful for fine-tuning or transfer-learning models where
      some of the layers have changed.
  
      Only topological loading (`by_name=False`) is supported when loading weights
      from the TensorFlow format. Note that topological loading differs slightly
      between TensorFlow and HDF5 formats for user-defined classes inheriting from
      `tf.keras.Model`: HDF5 loads based on a flattened list of weights, while the
      TensorFlow format loads based on the object-local names of attributes to
      which layers are assigned in the `Model`'s constructor.
  
      Args:
          filepath: String, path to the weights file to load. For weight files in
              TensorFlow format, this is the file prefix (the same as was passed
              to `save_weights`). This can also be a path to a SavedModel
              saved from `model.save`.
          by_name: Boolean, whether to load weights by name or by topological
              order. Only topological loading is supported for weight files in
              TensorFlow format.
          skip_mismatch: Boolean, whether to skip loading of layers where there is
              a mismatch in the number of weights, or a mismatch in the shape of
              the weight (only valid when `by_name=True`).
          options: Optional `tf.train.CheckpointOptions` object that specifies
              options for loading weights.
  
      Returns:
          When loading a weight file in TensorFlow format, returns the same status
          object as `tf.train.Checkpoint.restore`. When graph building, restore
          ops are run automatically as soon as the network is built (on first call
          for user-defined classes inheriting from `Model`, immediately if it is
          already built).
  
          When loading weights in HDF5 format, returns `None`.
  
      Raises:
          ImportError: If h5py is not available and the weight file is in HDF5
              format.
          ValueError: If `skip_mismatch` is set to `True` when `by_name` is
            `False`.
      """
      if dist_utils.is_tpu_strategy(self._distribution_strategy):
        if (self._distribution_strategy.extended.steps_per_run > 1 and
            (not saving_utils.is_hdf5_filepath(filepath))):
          raise ValueError('Load weights is not yet supported with TPUStrategy '
                           'with steps_per_run greater than 1.')
      if skip_mismatch and not by_name:
        raise ValueError(
            'When calling model.load_weights, skip_mismatch can only be set to '
            'True when by_name is True.')
  
      filepath, save_format = _detect_save_format(filepath)
      if save_format == 'tf':
        status = self._trackable_saver.restore(filepath, options)
        if by_name:
          raise NotImplementedError(
              'Weights may only be loaded based on topology into Models when '
              'loading TensorFlow-formatted weights (got by_name=True to '
              'load_weights).')
        if not tf.executing_eagerly():
          session = backend.get_session()
          # Restore existing variables (if any) immediately, and set up a
          # streaming restore for any variables created in the future.
          trackable_utils.streaming_restore(status=status, session=session)
        status.assert_nontrivial_match()
        return status
      if h5py is None:
        raise ImportError(
            '`load_weights` requires h5py when loading weights from HDF5.')
      if not self._is_graph_network and not self.built:
        raise ValueError(
            'Unable to load weights saved in HDF5 format into a subclassed '
            'Model which has not created its variables yet. Call the Model '
            'first, then load the weights.')
      self._assert_weights_created()
      with h5py.File(filepath, 'r') as f:
        if 'layer_names' not in f.attrs and 'model_weights' in f:
          f = f['model_weights']
        if by_name:
          hdf5_format.load_weights_from_hdf5_group_by_name(
              f, self.layers, skip_mismatch=skip_mismatch)
        else:
          hdf5_format.load_weights_from_hdf5_group(f, self.layers)
  
    def _updated_config(self):
      """Util shared between different serialization methods.
  
      Returns:
          Model config with Keras version information added.
      """
      from keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top
  
      config = self.get_config()
      model_config = {
          'class_name': self.__class__.__name__,
          'config': config,
          'keras_version': keras_version,
          'backend': backend.backend()
      }
      return model_config
  
    def get_config(self):
      raise NotImplementedError
  
    @classmethod
    def from_config(cls, config, custom_objects=None):
      # Since only FunctionalModel produces config, the model can only
      # be constructed for FunctionalModel
      from keras.engine import functional  # pylint: disable=g-import-not-at-top
      return functional.Functional.from_config(
          config, custom_objects=custom_objects)
  
    def to_json(self, **kwargs):
      """Returns a JSON string containing the network configuration.
  
      To load a network from a JSON save file, use
      `keras.models.model_from_json(json_string, custom_objects={})`.
  
      Args:
          **kwargs: Additional keyword arguments
              to be passed to `json.dumps()`.
  
      Returns:
          A JSON string.
      """
      model_config = self._updated_config()
      return json.dumps(
          model_config, default=json_utils.get_json_type, **kwargs)
  
    def to_yaml(self, **kwargs):
      """Returns a yaml string containing the network configuration.
  
      To load a network from a yaml save file, use
      `keras.models.model_from_yaml(yaml_string, custom_objects={})`.
  
      `custom_objects` should be a dictionary mapping
      the names of custom losses / layers / etc to the corresponding
      functions / classes.
  
      Args:
          **kwargs: Additional keyword arguments
              to be passed to `yaml.dump()`.
  
      Returns:
          A YAML string.
  
      Raises:
          ImportError: if yaml module is not found.
      """
      if yaml is None:
        raise ImportError(
            'Requires yaml module installed (`pip install pyyaml`).')
      return yaml.dump(self._updated_config(), **kwargs)
  
    def reset_states(self):
      for layer in self.layers:
        if hasattr(layer, 'reset_states') and getattr(layer, 'stateful', False):
          layer.reset_states()
  
    @property
    @doc_controls.do_not_generate_docs
    def state_updates(self):
      """Deprecated, do NOT use!
  
      Returns the `updates` from all layers that are stateful.
  
      This is useful for separating training updates and
      state updates, e.g. when we need to update a layer's internal state
      during prediction.
  
      Returns:
          A list of update ops.
      """
      warnings.warn('`Model.state_updates` will be removed in a future version. '
                    'This property should not be used in TensorFlow 2.0, '
                    'as `updates` are applied automatically.')
      state_updates = []
      for layer in self.layers:
        if getattr(layer, 'stateful', False):
          if hasattr(layer, 'updates'):
            state_updates += layer.updates
      return state_updates
  
    @property
    def weights(self):
      """Returns the list of all layer variables/weights.
  
      Note: This will not track the weights of nested `tf.Modules` that are not
      themselves Keras layers.
  
      Returns:
        A list of variables.
      """
      return self._dedup_weights(self._undeduplicated_weights)
  
    @property
    def _undeduplicated_weights(self):
      """Returns the undeduplicated list of all layer variables/weights."""
      self._assert_weights_created()
      weights = []
      for layer in self._self_tracked_trackables:
        weights += layer.variables
      weights += (self._trainable_weights + self._non_trainable_weights)
      return weights
  
    def summary(self, line_length=None, positions=None, print_fn=None):
      """Prints a string summary of the network.
  
      Args:
          line_length: Total length of printed lines
              (e.g. set this to adapt the display to different
              terminal window sizes).
          positions: Relative or absolute positions of log elements
              in each line. If not provided,
              defaults to `[.33, .55, .67, 1.]`.
          print_fn: Print function to use. Defaults to `print`.
              It will be called on each line of the summary.
              You can set it to a custom function
              in order to capture the string summary.
  
      Raises:
          ValueError: if `summary()` is called before the model is built.
      """
      if not self.built:
        raise ValueError('This model has not yet been built. '
                         'Build the model first by calling `build()` or calling '
                         '`fit()` with some data, or specify '
                         'an `input_shape` argument in the first layer(s) for '
                         'automatic build.')
      layer_utils.print_summary(self,
                                line_length=line_length,
                                positions=positions,
                                print_fn=print_fn)
  
    @property
    def layers(self):
      return list(self._flatten_layers(include_self=False, recursive=False))
  
    def get_layer(self, name=None, index=None):
      """Retrieves a layer based on either its name (unique) or index.
  
      If `name` and `index` are both provided, `index` will take precedence.
      Indices are based on order of horizontal graph traversal (bottom-up).
  
      Args:
          name: String, name of layer.
          index: Integer, index of layer.
  
      Returns:
          A layer instance.
  
      Raises:
          ValueError: In case of invalid layer name or index.
      """
      # TODO(fchollet): We could build a dictionary based on layer names
      # since they are constant, but we have not done that yet.
      if index is not None and name is not None:
        raise ValueError('Provide only a layer name or a layer index.')
  
      if index is not None:
        if len(self.layers) <= index:
          raise ValueError('Was asked to retrieve layer at index ' + str(index) +
                           ' but model only has ' + str(len(self.layers)) +
                           ' layers.')
        else:
          return self.layers[index]
  
      if name is not None:
        for layer in self.layers:
          if layer.name == name:
            return layer
        raise ValueError('No such layer: ' + name + '.')
      raise ValueError('Provide either a layer name or layer index.')
  
    @trackable.no_automatic_dependency_tracking
    def _set_save_spec(self, inputs):
      if self._saved_model_inputs_spec is not None:
        return  # Already set.
  
      input_names = self.input_names
      if not input_names:
        input_names = compile_utils.create_pseudo_input_names(inputs)
  
      flat_inputs = tf.nest.flatten(inputs)
      specs = []
      for name, tensor in zip(input_names, flat_inputs):
        specs.append(
            tf_utils.get_tensor_spec(tensor, dynamic_batch=False, name=name))
      specs = tf.nest.pack_sequence_as(inputs, specs)
  
      self._saved_model_inputs_spec = specs
  
      # Store the input shapes
      if (self.__class__.__name__ == 'Sequential' and
          self._build_input_shape is None):
        self._build_input_shape = tf.nest.map_structure(
            lambda x: None if x is None else x.shape, specs)
  
    def _assert_weights_created(self):
      """Asserts that all the weights for the model have been created.
  
      For a non-dynamic model, the weights must already be created after the
      layer has been called. For a dynamic model, the exact list of weights can
      never be known for certain since it may change at any time during execution.
  
      We run this check right before accessing weights or getting the Numpy value
      for the current weights. Otherwise, if the layer has never been called,
      the user would just get an empty list, which is misleading.
  
      Raises:
        ValueError: if the weights of the network has not yet been created.
      """
      if self.dynamic:
        return
  
      if ('build' in self.__class__.__dict__ and
          self.__class__ != Model and
          not self.built):
        # For any model that has customized build() method but hasn't
        # been invoked yet, this will cover both sequential and subclass model.
        # Also make sure to exclude Model class itself which has build() defined.
        raise ValueError('Weights for model %s have not yet been created. '
                         'Weights are created when the Model is first called on '
                         'inputs or `build()` is called with an `input_shape`.' %
                         self.name)
  
    def _check_call_args(self, method_name):
      """Check that `call` has only one positional arg."""
      # Always allow first arg, regardless of arg name.
      fullargspec = self._call_full_argspec
      if fullargspec.defaults:
        positional_args = fullargspec.args[:-len(fullargspec.defaults)]
      else:
        positional_args = fullargspec.args
      if 'training' in positional_args:
        positional_args.remove('training')
  
      # self and first arg can be positional.
      if len(positional_args) > 2:
        extra_args = positional_args[2:]
        raise ValueError(
            'Models passed to `' + method_name + '` can only have `training` '
            'and the first argument in `call` as positional arguments, '
            'found: ' + str(extra_args) + '.')
  
    def _validate_compile(self, optimizer, metrics, **kwargs):
      """Performs validation checks for the default `compile`."""
      if any(
          isinstance(opt, optimizer_v1.Optimizer)
          for opt in tf.nest.flatten(optimizer)):
        raise ValueError(
            '`tf.compat.v1.keras` Optimizer (', optimizer, ') is '
            'not supported when eager execution is enabled. Use a '
            '`tf.keras` Optimizer instead, or disable eager '
            'execution.')
  
      kwargs.pop('cloning', None)  # Legacy DistStrat argument, never used.
      kwargs.pop('experimental_run_tf_function', None)  # Always `True`.
      if kwargs.pop('distribute', None) is not None:
        raise ValueError(
            'Distribute argument in compile is not available in TF 2.0 please '
            'create the model under the distribution strategy scope.')
      if kwargs.pop('target_tensors', None) is not None:
        raise ValueError(
            'target_tensors argument is not supported when executing eagerly.')
      invalid_kwargs = set(kwargs) - {'sample_weight_mode'}
      if invalid_kwargs:
        raise TypeError('Invalid keyword argument(s) in `compile`: %s' %
                        (invalid_kwargs,))
  
      # Model must be created and compiled with the same DistStrat.
      if self.built and tf.distribute.has_strategy():
        strategy = tf.distribute.get_strategy()
        for v in self.variables:
          if not strategy.extended.variable_created_in_scope(v):
            raise ValueError(
                'Variable (%s) was not created in the distribution strategy '
                'scope of (%s). It is most likely due to not all layers or '
                'the model or optimizer being created outside the distribution '
                'strategy scope. Try to make sure your code looks similar '
                'to the following.\n'
                'with strategy.scope():\n'
                '  model=_create_model()\n'
                '  model.compile(...)' % (v, strategy))
  
      # Model metrics must be created in the same distribution strategy scope
      # as the model.
      strategy = self.distribute_strategy
      for metric in tf.nest.flatten(metrics):
        for v in getattr(metric, 'variables', []):
          if not strategy.extended.variable_created_in_scope(v):
            raise ValueError(
                'Metric (%s) passed to model.compile was created inside of a '
                'different distribution strategy scope than the model. All '
                'metrics must be created in the same distribution strategy '
                'scope as the model (in this case %s). If you pass in a string '
                'identifier for a metric to compile the metric will '
                'automatically be created in the correct distribution '
                'strategy scope.' % (metric, strategy)
            )
  
      # Model metrics must be created in the same distribution strategy scope
      # as the model.
      for opt in tf.nest.flatten(optimizer):
        for v in getattr(opt, '_weights', []):
          if not strategy.extended.variable_created_in_scope(v):
            raise ValueError(
                'Optimizer (%s) passed to model.compile was created inside of a '
                'different distribution strategy scope than the model. All '
                'optimizers must be created in the same distribution strategy '
                'scope as the model (in this case %s). If you pass in a string '
                'identifier for an optimizer to compile the optimizer will '
                'automatically be created in the correct distribution '
                'strategy scope.' % (opt, strategy))
  
    def _maybe_load_initial_epoch_from_ckpt(self, initial_epoch):
      """Maybe load initial epoch from ckpt considering possible worker recovery.
  
      Refer to tensorflow/python/keras/distribute/worker_training_state.py
      for more information.
  
      Args:
        initial_epoch: The original initial_epoch user passes in in `fit()`.
  
      Returns:
        If the training is recovering from previous failure under multi-worker
        training setting, return the epoch the training is supposed to continue
        at. Otherwise, return the `initial_epoch` the user passes in.
      """
      if self._training_state is not None:
        return self._training_state.maybe_load_initial_epoch_from_ckpt(
            initial_epoch, mode=ModeKeys.TRAIN)
      return initial_epoch
  
    def _assert_compile_was_called(self):
      # Checks whether `compile` has been called. If it has been called,
      # then the optimizer is set. This is different from whether the
      # model is compiled
      # (i.e. whether the model is built and its inputs/outputs are set).
      if not self._is_compiled:
        raise RuntimeError('You must compile your model before '
                           'training/testing. '
                           'Use `model.compile(optimizer, loss)`.')
  
    def _set_inputs(self, inputs, outputs=None, training=None):
      """This method is for compat with Modelv1. Only inputs are needed here."""
      self._set_save_spec(inputs)
  
    @property
    def _trackable_saved_model_saver(self):
      return model_serialization.ModelSavedModelSaver(self)
  
    def _list_functions_for_serialization(self, serialization_cache):
      # SavedModel needs to ignore the execution functions.
      train_function = self.train_function
      test_function = self.test_function
      predict_function = self.predict_function
      self.train_function = None
      self.test_function = None
      self.predict_function = None
      functions = super(
          Model, self)._list_functions_for_serialization(serialization_cache)
      self.train_function = train_function
      self.test_function = test_function
      self.predict_function = predict_function
      return functions
  
    def _should_eval(self, epoch, validation_freq):
      epoch = epoch + 1  # one-index the user-facing epoch.
      if isinstance(validation_freq, int):
        return epoch % validation_freq == 0
      elif isinstance(validation_freq, list):
        return epoch in validation_freq
      else:
        raise ValueError('Expected `validation_freq` to be a list or int.')
  
    ######################################################################
    # Functions below exist only as v1 / v2 compatibility shims.
    ######################################################################
  
    def _get_compile_args(self, user_metrics=True):
      """Used for saving or cloning a Model.
  
      Args:
        user_metrics: Whether to return user-supplied metrics or `Metric` objects.
          Defaults to returning the user-supplied metrics.
  
      Returns:
        Dictionary of arguments that were used when compiling the model.
      """
      self._assert_compile_was_called()
      # pylint: disable=protected-access
  
      saved_metrics = self.compiled_metrics._user_metrics
      saved_weighted_metrics = self.compiled_metrics._user_weighted_metrics
  
      if not user_metrics:
        if saved_metrics is not None:
          saved_metrics = self.compiled_metrics._metrics
        if saved_weighted_metrics is not None:
          saved_weighted_metrics = self.compiled_metrics._weighted_metrics
  
      compile_args = {
          'optimizer': self.optimizer,
          'loss': self.compiled_loss._user_losses,
          'metrics': saved_metrics,
          'weighted_metrics': saved_weighted_metrics,
          'loss_weights': self.compiled_loss._user_loss_weights,
      }
      # pylint: enable=protected-access
      return compile_args
  
    def _get_callback_model(self):
      return self
  
    def _in_multi_worker_mode(self):
      return self.distribute_strategy.extended._in_multi_worker_mode()  # pylint: disable=protected-access
  
    @property
    def _compile_was_called(self):
      return self._is_compiled
  
  
  def reduce_per_replica(values, strategy, reduction='first'):
    """Reduce PerReplica objects.
  
    Args:
      values: Structure of `PerReplica` objects or `Tensor`s. `Tensor`s are
        returned as-is.
      strategy: `tf.distribute.Strategy` object.
      reduction: One of 'first', 'concat'.
  
    Returns:
      Structure of `Tensor`s.
    """
  
    def _reduce(v):
      """Reduce a single `PerReplica` object."""
      if reduction == 'concat' and _collective_all_reduce_multi_worker(strategy):
        return _multi_worker_concat(v, strategy)
      if not isinstance(v, ds_values.PerReplica):
        return v
      elif reduction == 'first':
        return strategy.unwrap(v)[0]
      elif reduction == 'concat':
        if _is_tpu_multi_host(strategy):
          return _tpu_multi_host_concat(v, strategy)
        else:
          return concat(strategy.unwrap(v))
      else:
        raise ValueError('`reduction` must be "first" or "concat".')
  
    return tf.nest.map_structure(_reduce, values)
  
  
  def concat(tensors, axis=0):
    """Concats `tensor`s along `axis`."""
    if isinstance(tensors[0], tf.SparseTensor):
      return tf.sparse.concat(axis=axis, sp_inputs=tensors)
    return tf.concat(tensors, axis=axis)
  
  
  def _is_tpu_multi_host(strategy):
    return (dist_utils.is_tpu_strategy(strategy) and
            strategy.extended.num_hosts > 1)
  
  
  def _tpu_multi_host_concat(v, strategy):
    """Correctly order TPU PerReplica objects."""
    replicas = strategy.unwrap(v)
    # When distributed datasets are created from Tensors / NumPy,
    # TPUStrategy.experimental_distribute_dataset shards data in
    # (Replica, Host) order, and TPUStrategy.unwrap returns it in
    # (Host, Replica) order.
    # TODO(b/150317897): Figure out long-term plan here.
    num_replicas_per_host = strategy.extended.num_replicas_per_host
    ordered_replicas = []
    for replica_id in range(num_replicas_per_host):
      ordered_replicas += replicas[replica_id::num_replicas_per_host]
    return concat(ordered_replicas)
  
  
  def _collective_all_reduce_multi_worker(strategy):
    return (isinstance(strategy,
                       tf.distribute.MultiWorkerMirroredStrategy)
           ) and strategy.extended._in_multi_worker_mode()  # pylint: disable=protected-access
  
  
  # TODO(wxinyi): merge this with _tpu_multi_host_concat once we have all_gather
  # for all strategies
  def _multi_worker_concat(v, strategy):
    """Order PerReplica objects for CollectiveAllReduceStrategy and concat."""
    replicas = strategy.gather(v, axis=0)  # pylint: disable=protected-access
    # TODO(b/170435030): We now need to make sure these run after the iterator
    # GetNext, so that we don't trigger aborting collective ops in the case of
    # EOF. Remove after the issue is fixed.
    with tf.control_dependencies([replicas]):
      # v might not have the same shape on different replicas
      if isinstance(v, ds_values.PerReplica):
        shapes = tf.concat([
            tf.expand_dims(tf.compat.v1.shape(single_value)[0], axis=0)
            for single_value in v.values
        ],
                                  axis=0)
        all_shapes = strategy.gather(shapes, axis=0)
      else:
        # v is a tensor. This may happen when, say, we have 2x1 multi-worker.
        all_shapes = strategy.gather(
            tf.expand_dims(tf.compat.v1.shape(v)[0], axis=0),
            axis=0)
  
    replicas = tf.split(
        replicas,
        num_or_size_splits=all_shapes,
        num=strategy.num_replicas_in_sync)
    ordered_replicas = []
    num_replicas_per_worker = len(strategy.extended.worker_devices)
    for replica_id in range(num_replicas_per_worker):
      ordered_replicas += replicas[replica_id::num_replicas_per_worker]
    return concat(ordered_replicas)
  
  
  def _is_scalar(x):
    return isinstance(x, (tf.Tensor, tf.Variable)) and x.shape.rank == 0
  
  
  def write_scalar_summaries(logs, step):
    for name, value in logs.items():
      if _is_scalar(value):
        summary_ops_v2.scalar('batch_' + name, value, step=step)
  
  
  def _minimum_control_deps(outputs):
    """Returns the minimum control dependencies to ensure step succeeded."""
    if tf.executing_eagerly():
      return []  # Control dependencies not needed.
    outputs = tf.nest.flatten(outputs, expand_composites=True)
    for out in outputs:
      # Variables can't be control dependencies.
      if not isinstance(out, tf.Variable):
        return [out]  # Return first Tensor or Op from outputs.
    return []  # No viable Tensor or Op to use for control deps.
  
  
  def _disallow_inside_tf_function(method_name):
    if tf.inside_function():
      error_msg = (
          'Detected a call to `Model.{method_name}` inside a `tf.function`. '
          '`Model.{method_name} is a high-level endpoint that manages its own '
          '`tf.function`. Please move the call to `Model.{method_name}` outside '
          'of all enclosing `tf.function`s. Note that you can call a `Model` '
          'directly on `Tensor`s inside a `tf.function` like: `model(x)`.'
      ).format(method_name=method_name)
      raise RuntimeError(error_msg)
  
  
  def _detect_save_format(filepath):
    """Returns path to weights file and save format."""
  
    filepath = path_to_string(filepath)
    if saving_utils.is_hdf5_filepath(filepath):
      return filepath, 'h5'
  
    # Filepath could be a TensorFlow checkpoint file prefix or SavedModel
    # directory. It's possible for filepath to be both a prefix and directory.
    # Prioritize checkpoint over SavedModel.
    if _is_readable_tf_checkpoint(filepath):
      save_format = 'tf'
    elif tf.saved_model.contains_saved_model(filepath):
      ckpt_path = os.path.join(filepath, tf.saved_model.VARIABLES_DIRECTORY,
                               tf.saved_model.VARIABLES_FILENAME)
      if _is_readable_tf_checkpoint(ckpt_path):
        filepath = ckpt_path
        save_format = 'tf'
      else:
        raise ValueError('Unable to load weights. filepath {} appears to be a '
                         'SavedModel directory, but checkpoint either doesn\'t '
                         'exist, or is incorrectly formatted.'.format(filepath))
    else:
      # Not a TensorFlow checkpoint. This filepath is likely an H5 file that
      # doesn't have the hdf5/keras extensions.
      save_format = 'h5'
    return filepath, save_format
  
  
  def _is_readable_tf_checkpoint(filepath):
    try:
      tf.compat.v1.train.NewCheckpointReader(filepath)
      return True
    except tf.errors.DataLossError:
      # The checkpoint is not readable in TensorFlow format.
      return False
  