def test_decorator(self):
    compatibility_date = self._compatibility_date()
    one_day_after = self._n_days_after(1)
    with compat.forward_compatibility_horizon(*one_day_after):
      self.assertTrue(compat.forward_compatible(*compatibility_date))
      self.assertFalse(compat.forward_compatible(*one_day_after))

    # After exiting context manager, value should be reset.
    self.assertFalse(compat.forward_compatible(*compatibility_date))

  def initialize(self, table):
    """Initializes the given `table` with `keys` and `values` tensors.

    Args:
      table: The table to initialize.

    Returns:
      The operation that initializes the table.

    Raises:
      TypeError: when the keys and values data types do not match the table
      key and value data types.
    """
    _check_table_dtypes(table, self._keys.dtype, self._values.dtype)
    with ops.name_scope(
        self._name, values=(table.resource_handle, self._keys,
                            self._values)) as scope:
      if context.executing_eagerly():
        # Ensure a unique name when eager execution is enabled to avoid spurious
        # sharing issues.
        scope += str(ops.uid())
      if fwd_compat.forward_compatible(2018, 9, 19):
        init_op = gen_lookup_ops.lookup_table_import_v2(
            table.resource_handle, self._keys, self._values, name=scope)
      else:
        # To maintain forward compatibiltiy, use the old implementation.
        init_op = gen_lookup_ops.initialize_table_v2(
            table.resource_handle, self._keys, self._values, name=scope)
    ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op)
    return init_op

def _safe_div(numerator, denominator, name="value"):
  """Computes a safe divide which returns 0 if the denominator is zero.

  Note that the function contains an additional conditional check that is
  necessary for avoiding situations where the loss is zero causing NaNs to
  creep into the gradient computation.

  Args:
    numerator: An arbitrary `Tensor`.
    denominator: A `Tensor` whose shape matches `numerator` and whose values are
      assumed to be non-negative.
    name: An optional name for the returned op.

  Returns:
    The element-wise value of the numerator divided by the denominator.
  """
  if compat.forward_compatible(2018, 11, 1):
    return math_ops.div_no_nan(numerator, denominator, name=name)
  return array_ops.where(
      math_ops.greater(denominator, 0),
      math_ops.div(numerator,
                   array_ops.where(
                       math_ops.equal(denominator, 0),
                       array_ops.ones_like(denominator), denominator)),
      array_ops.zeros_like(numerator),
      name=name)

  def testSecondGradient(self):
    with self.cached_session() as sess:
      l = constant_op.constant(
          [
              0.0, 0.0, 1.0 / 3, 0.0, 1.0 / 3, 0.0, 0.0, 0.0, 0.0, 0.5 / 3, 0.0,
              0.5 / 3
          ],
          shape=[12],
          dtype=dtypes.float64,
          name="l")
      f = constant_op.constant(
          [0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4],
          shape=[12],
          dtype=dtypes.float64,
          name="f")
      x = nn_ops.softmax_cross_entropy_with_logits(
          labels=l, logits=f, name="xent")
      loss = math_ops.reduce_sum(x)

      gradients = gradients_impl.gradients(loss, [f])[0]

      err = gradient_checker.compute_gradient_error(f, [12], gradients, [12])

      # Check that second derivative is calculated.
      # (it is equivalent to being `BatchMatMul` op in the graph because of implementation of xentropy grad)
      op_names = [
          op.op_def.name for op in sess.graph.get_operations() if op.op_def
      ]
      if compat.forward_compatible(2019, 4, 25):
        self.assertIn("BatchMatMulV2", op_names)
      else:
        self.assertIn("BatchMatMul", op_names)

    print("cross entropy hessian err = ", err)
    self.assertLess(err, 5e-8)

 def _as_variant_tensor(self):
   if (self._compression_type is not None or
       compat.forward_compatible(2018, 11, 30)):
     return gen_dataset_ops.fixed_length_record_dataset_v2(
         self._filenames, self._header_bytes, self._record_bytes,
         self._footer_bytes, self._buffer_size, self._compression_type)
   else:
     return gen_dataset_ops.fixed_length_record_dataset(
         self._filenames, self._header_bytes, self._record_bytes,
         self._footer_bytes, self._buffer_size)

  def testGradientAtSingularity(self):
    if not compat.forward_compatible(2019, 6, 14):
      self.skipTest("Skipping test for future functionality.")

    ops_and_singularity = [
        (gen_math_ops.reciprocal, (0.,)),
        (gen_math_ops.rsqrt, (0.,)),
        (gen_math_ops.sqrt, (0.,)),
        (gen_math_ops.sqrt_grad, (
            0.,
            0.,
        )),
        (gen_math_ops.reciprocal_grad, (
            1.,
            0.,
        )),
        (gen_math_ops.tan, (np.pi / 2,)),
        (gen_math_ops.log, (0.,)),
        (gen_math_ops.log1p, (-1.,)),
        (gen_math_ops.acosh, (0.,)),
        (gen_math_ops.asin, (1.,)),
        (gen_math_ops.acos, (1.,)),
        (gen_math_ops.atan2, (0., 0.)),
        (gen_math_ops.div, (1., 0.)),
        (gen_math_ops.div_no_nan, (1., 0.)),
        (gen_math_ops.real_div, (1., 0.)),
        (math_ops.pow, (0., -1.)),
    ]
    for op, singularity in ops_and_singularity:
      for dtype in (dtypes_lib.half, dtypes_lib.float32, dtypes_lib.float64,
                    dtypes_lib.complex64, dtypes_lib.complex128):
        if dtype.is_complex and op in [
            gen_math_ops.asin, gen_math_ops.acos, gen_math_ops.atan2
        ]:
          continue
        if dtype == dtypes_lib.half and op in [
            gen_math_ops.acosh, gen_math_ops.asin, gen_math_ops.acos,
            gen_math_ops.atan2
        ]:
          continue
        with self.cached_session():
          print("op = ", op, ", singularity = ", singularity, ", type = ",
                dtype)
          args = [constant_op.constant(s, dtype=dtype) for s in singularity]
          grad_y = constant_op.constant(0, dtype=dtype)
          y = op(*args)
          g = gradients_impl.gradients(y, args, grad_ys=grad_y)
          g_val = self.evaluate(g)
          self.assertAllEqual(g_val, np.zeros(len(singularity)))

  def test_decorator_with_failure(self):
    compatibility_date = self._compatibility_date()
    one_day_after = self._n_days_after(1)

    class DummyError(Exception):
      pass

    try:
      with compat.forward_compatibility_horizon(*one_day_after):
        raise DummyError()
    except DummyError:
      pass  # silence DummyError

    # After exiting context manager, value should be reset.
    self.assertFalse(compat.forward_compatible(*compatibility_date))

  def __init__(self,
               filenames,
               record_bytes,
               header_bytes=None,
               footer_bytes=None,
               buffer_size=None,
               compression_type=None):
    """Creates a `FixedLengthRecordDataset`.

    Args:
      filenames: A `tf.string` tensor containing one or more filenames.
      record_bytes: A `tf.int64` scalar representing the number of bytes in
        each record.
      header_bytes: (Optional.) A `tf.int64` scalar representing the number of
        bytes to skip at the start of a file.
      footer_bytes: (Optional.) A `tf.int64` scalar representing the number of
        bytes to ignore at the end of a file.
      buffer_size: (Optional.) A `tf.int64` scalar representing the number of
        bytes to buffer when reading.
      compression_type: (Optional.) A `tf.string` scalar evaluating to one of
        `""` (no compression), `"ZLIB"`, or `"GZIP"`.
    """
    self._filenames = ops.convert_to_tensor(
        filenames, dtype=dtypes.string, name="filenames")
    self._record_bytes = ops.convert_to_tensor(
        record_bytes, dtype=dtypes.int64, name="record_bytes")

    self._header_bytes = convert.optional_param_to_tensor(
        "header_bytes", header_bytes)
    self._footer_bytes = convert.optional_param_to_tensor(
        "footer_bytes", footer_bytes)
    self._buffer_size = convert.optional_param_to_tensor(
        "buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)
    self._compression_type = convert.optional_param_to_tensor(
        "compression_type",
        compression_type,
        argument_default="",
        argument_dtype=dtypes.string)
    if (self._compression_type is not None or
        compat.forward_compatible(2018, 11, 30)):
      variant_tensor = gen_dataset_ops.fixed_length_record_dataset_v2(
          self._filenames, self._header_bytes, self._record_bytes,
          self._footer_bytes, self._buffer_size, self._compression_type)
    else:
      variant_tensor = gen_dataset_ops.fixed_length_record_dataset(
          self._filenames, self._header_bytes, self._record_bytes,
          self._footer_bytes, self._buffer_size)
    super(FixedLengthRecordDatasetV2, self).__init__(variant_tensor)

  def test_batch_matmul_broadcast(self):
    if not compat.forward_compatible(2019, 4, 25):
      self.skipTest("Skipping test for future functionality.")
    for broadcast_a in (True, False):
      for broadcast_b in (True, False):
        for stack_a in (True, False):
          for stack_b in (True, False):
            shape_a = (2, 3, 5) if broadcast_a else (4, 2, 3, 5)
            shape_b = (2, 5, 7) if broadcast_b else (4, 2, 5, 7)
            shape_a = (2,) + shape_a if stack_a else shape_a
            shape_b = (2,) + shape_b if stack_b else shape_b
            x = random_ops.random_uniform(shape_a)
            y = random_ops.random_uniform(shape_b)

            # pylint: disable=cell-var-from-loop
            def loop_fn(i):
              a = array_ops.gather(x, i) if stack_a else x
              b = array_ops.gather(y, i) if stack_b else y
              return math_ops.matmul(a, b)

            # pylint: enable=cell-var-from-loop
            self._test_loop_fn(loop_fn, 2)

def regex_full_match(input, pattern, name=None):
  r"""Match elements of `input` with regex `pattern`.

  Args:
    input: string `Tensor`, the source strings to process.
    pattern: string or scalar string `Tensor`, regular expression to use,
      see more details at https://github.com/google/re2/wiki/Syntax
    name: Name of the op.

  Returns:
    bool `Tensor` of the same shape as `input` with match results.
  """
  # TODO(b/112455102): Remove compat.forward_compatible once past the horizon.
  if not compat.forward_compatible(2018, 11, 10):
    return gen_string_ops.regex_full_match(
        input=input, pattern=pattern, name=name)
  if isinstance(pattern, util_compat.bytes_or_text_types):
    # When `pattern` is static through the life of the op we can
    # use a version which performs the expensive regex compilation once at
    # creation time.
    return gen_string_ops.static_regex_full_match(
        input=input, pattern=pattern, name=name)
  return gen_string_ops.regex_full_match(
      input=input, pattern=pattern, name=name)

  def from_string_handle(string_handle,
                         output_types,
                         output_shapes=None,
                         output_classes=None):
    """Creates a new, uninitialized `Iterator` based on the given handle.

    This method allows you to define a "feedable" iterator where you can choose
    between concrete iterators by feeding a value in a `tf.Session.run` call.
    In that case, `string_handle` would be a `tf.placeholder`, and you would
    feed it with the value of `tf.data.Iterator.string_handle` in each step.

    For example, if you had two iterators that marked the current position in
    a training dataset and a test dataset, you could choose which to use in
    each step as follows:

    ```python
    train_iterator = tf.data.Dataset(...).make_one_shot_iterator()
    train_iterator_handle = sess.run(train_iterator.string_handle())

    test_iterator = tf.data.Dataset(...).make_one_shot_iterator()
    test_iterator_handle = sess.run(test_iterator.string_handle())

    handle = tf.placeholder(tf.string, shape=[])
    iterator = tf.data.Iterator.from_string_handle(
        handle, train_iterator.output_types)

    next_element = iterator.get_next()
    loss = f(next_element)

    train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
    test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})
    ```

    Args:
      string_handle: A scalar `tf.Tensor` of type `tf.string` that evaluates
        to a handle produced by the `Iterator.string_handle()` method.
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element of this dataset.
      output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this dataset. If
        omitted, each component will have an unconstrainted shape.
      output_classes: (Optional.) A nested structure of Python `type` objects
        corresponding to each component of an element of this iterator. If
        omitted, each component is assumed to be of type `tf.Tensor`.

    Returns:
      An `Iterator`.
    """
    output_types = nest.map_structure(dtypes.as_dtype, output_types)
    if output_shapes is None:
      output_shapes = nest.map_structure(
          lambda _: tensor_shape.TensorShape(None), output_types)
    else:
      output_shapes = nest.map_structure_up_to(
          output_types, tensor_shape.as_shape, output_shapes)
    if output_classes is None:
      output_classes = nest.map_structure(lambda _: ops.Tensor, output_types)
    nest.assert_same_structure(output_types, output_shapes)
    output_structure = structure_lib.convert_legacy_structure(
        output_types, output_shapes, output_classes)
    string_handle = ops.convert_to_tensor(string_handle, dtype=dtypes.string)
    # pylint: disable=protected-access
    if compat.forward_compatible(2018, 8, 3):
      if _device_stack_is_empty():
        with ops.device("/cpu:0"):
          iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2(
              string_handle,
              output_types=output_structure._flat_types,
              output_shapes=output_structure._flat_shapes)
      else:
        iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2(
            string_handle,
            output_types=output_structure._flat_types,
            output_shapes=output_structure._flat_shapes)
    else:
      iterator_resource = gen_dataset_ops.iterator_from_string_handle(
          string_handle,
          output_types=output_structure._flat_types,
          output_shapes=output_structure._flat_shapes)
    # pylint: enable=protected-access
    return Iterator(iterator_resource, None, output_types, output_shapes,
                    output_classes)

  def from_structure(output_types,
                     output_shapes=None,
                     shared_name=None,
                     output_classes=None):
    """Creates a new, uninitialized `Iterator` with the given structure.

    This iterator-constructing method can be used to create an iterator that
    is reusable with many different datasets.

    The returned iterator is not bound to a particular dataset, and it has
    no `initializer`. To initialize the iterator, run the operation returned by
    `Iterator.make_initializer(dataset)`.

    The following is an example

    ```python
    iterator = Iterator.from_structure(tf.int64, tf.TensorShape([]))

    dataset_range = Dataset.range(10)
    range_initializer = iterator.make_initializer(dataset_range)

    dataset_evens = dataset_range.filter(lambda x: x % 2 == 0)
    evens_initializer = iterator.make_initializer(dataset_evens)

    # Define a model based on the iterator; in this example, the model_fn
    # is expected to take scalar tf.int64 Tensors as input (see
    # the definition of 'iterator' above).
    prediction, loss = model_fn(iterator.get_next())

    # Train for `num_epochs`, where for each epoch, we first iterate over
    # dataset_range, and then iterate over dataset_evens.
    for _ in range(num_epochs):
      # Initialize the iterator to `dataset_range`
      sess.run(range_initializer)
      while True:
        try:
          pred, loss_val = sess.run([prediction, loss])
        except tf.errors.OutOfRangeError:
          break

      # Initialize the iterator to `dataset_evens`
      sess.run(evens_initializer)
      while True:
        try:
          pred, loss_val = sess.run([prediction, loss])
        except tf.errors.OutOfRangeError:
          break
    ```

    Args:
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element of this dataset.
      output_shapes: (Optional.) A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this dataset. If
        omitted, each component will have an unconstrainted shape.
      shared_name: (Optional.) If non-empty, this iterator will be shared under
        the given name across multiple sessions that share the same devices
        (e.g. when using a remote server).
      output_classes: (Optional.) A nested structure of Python `type` objects
        corresponding to each component of an element of this iterator. If
        omitted, each component is assumed to be of type `tf.Tensor`.

    Returns:
      An `Iterator`.

    Raises:
      TypeError: If the structures of `output_shapes` and `output_types` are
        not the same.
    """
    output_types = nest.map_structure(dtypes.as_dtype, output_types)
    if output_shapes is None:
      output_shapes = nest.map_structure(
          lambda _: tensor_shape.TensorShape(None), output_types)
    else:
      output_shapes = nest.map_structure_up_to(
          output_types, tensor_shape.as_shape, output_shapes)
    if output_classes is None:
      output_classes = nest.map_structure(lambda _: ops.Tensor, output_types)
    nest.assert_same_structure(output_types, output_shapes)
    output_structure = structure_lib.convert_legacy_structure(
        output_types, output_shapes, output_classes)
    if shared_name is None:
      shared_name = ""
    # pylint: disable=protected-access
    if compat.forward_compatible(2018, 8, 3):
      if _device_stack_is_empty():
        with ops.device("/cpu:0"):
          iterator_resource = gen_dataset_ops.iterator_v2(
              container="",
              shared_name=shared_name,
              output_types=output_structure._flat_types,
              output_shapes=output_structure._flat_shapes)
      else:
        iterator_resource = gen_dataset_ops.iterator_v2(
            container="",
            shared_name=shared_name,
            output_types=output_structure._flat_types,
            output_shapes=output_structure._flat_shapes)
    else:
      iterator_resource = gen_dataset_ops.iterator(
#.........这里部分代码省略.........

#.........这里部分代码省略.........
          # There really should not be more than 2^32 partitions.
          p_assignments = math_ops.cast(p_assignments, dtypes.int32)
          # Partition list of ids based on assignments into num_partitions
          # separate lists.
          gather_ids = data_flow_ops.dynamic_partition(new_ids,
                                                       p_assignments,
                                                       num_partitions)
          # Add these into the dictionaries for use in the later update.
          num_partitions_by_var[v_num] = num_partitions
          p_assignments_by_var[v_num] = p_assignments
          gather_ids_by_var[v_num] = gather_ids

          # Gather the weights from each partition.
          partition_gathered_weights = []
          for p in range(num_partitions):
            with ops.colocate_with(w[p]):
              partition_gathered_weights.append(
                  array_ops.gather(w[p], gather_ids[p]))

          # Stitch the weights back together in the same order they were before
          # we dynamic_partitioned them.
          condition_indices = data_flow_ops.dynamic_partition(
              math_ops.range(array_ops.shape(new_ids)[0]),
              p_assignments, num_partitions)
          batch_gathered_weights = data_flow_ops.dynamic_stitch(
              condition_indices, partition_gathered_weights)
        else:
          w_as_tensor = internal_convert_to_tensor(w)
          with ops.device(w_as_tensor.device):
            batch_gathered_weights = array_ops.gather(
                w_as_tensor, sparse_idx)
        sparse_weights.append(batch_gathered_weights)

      # pylint: disable=protected-access
      if compat.forward_compatible(year=2018, month=10, day=30):
        esu, sfw, dfw = gen_sdca_ops.sdca_optimizer_v2(
            sparse_example_indices,
            sparse_feature_indices,
            sparse_features_values,
            self._convert_n_to_tensor(self._examples['dense_features']),
            internal_convert_to_tensor(self._examples['example_weights']),
            internal_convert_to_tensor(self._examples['example_labels']),
            sparse_indices,
            sparse_weights,
            self._convert_n_to_tensor(self._slots[
                'unshrinked_dense_features_weights']),
            example_state_data,
            loss_type=self._options['loss_type'],
            l1=self._options['symmetric_l1_regularization'],
            l2=self._symmetric_l2_regularization(),
            num_loss_partitions=self._num_loss_partitions(),
            num_inner_iterations=1,
            adaptive=self._adaptive())
      else:
        esu, sfw, dfw = gen_sdca_ops.sdca_optimizer(
            sparse_example_indices,
            sparse_feature_indices,
            sparse_features_values,
            self._convert_n_to_tensor(self._examples['dense_features']),
            internal_convert_to_tensor(self._examples['example_weights']),
            internal_convert_to_tensor(self._examples['example_labels']),
            sparse_indices,
            sparse_weights,
            self._convert_n_to_tensor(self._slots[
                'unshrinked_dense_features_weights']),
            example_state_data,
            loss_type=self._options['loss_type'],
            l1=self._options['symmetric_l1_regularization'],
            l2=self._symmetric_l2_regularization(),
            num_loss_partitions=self._num_loss_partitions(),
            num_inner_iterations=1,
            adaptative=self._adaptive())
      # pylint: enable=protected-access

      with ops.control_dependencies([esu]):
        update_ops = [self._hashtable.insert(example_ids_hashed, esu)]
        # Update the weights before the proximal step.
        for v_num, (w, i, u) in enumerate(
            zip(self._slots['unshrinked_sparse_features_weights'],
                sparse_indices, sfw)):
          if (isinstance(w, var_ops.PartitionedVariable) or
              isinstance(w, list)):
            update_ops += self._get_partitioned_update_ops(
                v_num, num_partitions_by_var, p_assignments_by_var,
                gather_ids_by_var, w, u, p_assignments, num_partitions)
          else:
            update_ops.append(state_ops.scatter_add(w, i, u))
        for w, u in zip(self._slots['unshrinked_dense_features_weights'], dfw):
          if (isinstance(w, var_ops.PartitionedVariable) or
              isinstance(w, list)):
            split_updates = array_ops.split(
                u, num_or_size_splits=[v.shape.as_list()[0] for v in w])
            for v, split_update in zip(w, split_updates):
              update_ops.append(state_ops.assign_add(v, split_update))
          else:
            update_ops.append(state_ops.assign_add(w, u))
      if not global_step:
        return control_flow_ops.group(*update_ops)
      with ops.control_dependencies(update_ops):
        return state_ops.assign_add(global_step, 1, name=name).op

def split_compile_and_replicate(computation,
                                inputs=None,
                                infeed_queue=None,
                                device_assignment=None,
                                name=None,
                                use_tpu=True):
  """Builds graph operators that runs compilation and replicated computation.

  This is a lower level interface than replicate that returns a separate compile
  and execute output tensor. In the generated graph the compile op feeds into
  the execute op and no additional compilation is incurred when running the
  compile op before the execute op. The compile op returns additional
  information about the compilation but does not return the compiled program.

  Args:
    computation: A Python function that builds the computation to replicate.
    inputs: A list of lists of input tensors or `None` (equivalent to
      `[[]]`), indexed by `[replica_num][input_num]`. All replicas must
      have the same number of inputs.
    infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple
      of arguments as inputs to computation.
    device_assignment: If not `None`, a `DeviceAssignment` describing the
      mapping between logical cores in the computation with physical cores in
      the TPU topology. Uses a default device assignment if `None`. The
      `DeviceAssignment` may be omitted if each replica of the computation uses
      only one core, and there is either only one replica, or the number of
      replicas is equal to the number of cores in the TPU system.
    name: (Deprecated) Does nothing.
    use_tpu: When false, the input `computation` is executed on the XLA CPU/GPU
      backends. Currently, only supports a default placement (computation is
      placed on GPU if one is available, and on CPU if not).
  Returns:
    A list of lists with the first list corresponding to the compile op and the
    second a list of output tensors, indexed by `[replica_num][output_num]`.
  Raises:
    ValueError: If all replicas do not have equal numbers of input tensors.
    ValueError: If the number of inputs per replica does not match
      the number of formal parameters to `computation`.
  """
  del name
  inputs = [[]] if inputs is None else inputs

  metadata_kwargs = {}
  if device_assignment is not None:
    # Turn the Numpy array into a flattened list so we can pass it as an
    # operator attribute.
    metadata_kwargs = {
        "topology":
            device_assignment.topology.serialized(),
        "device_assignment":
            device_assignment.core_assignment.flatten().tolist()
    }
    # TODO(phawkins): remove this case after the forward compatibility window
    # expires on 2018-10-5.
    if api_compat.forward_compatible(2018, 10, 5):
      metadata_kwargs["num_cores_per_replica"] = (
          device_assignment.num_cores_per_replica)
    else:
      metadata_kwargs["computation_shape"] = [
          device_assignment.num_cores_per_replica
      ]

  if ((not isinstance(inputs, list)) or
      any(not isinstance(inp, (list, tuple)) for inp in inputs)):
    raise TypeError("tpu.replicate() inputs must be a list of lists/tuples")

  num_replicas = len(inputs)

  # No replicas? Nothing to do.
  if num_replicas == 0:
    return []

  # Converts inputs to Tensors.
  inputs = [[ops.convert_to_tensor(x) for x in inp] for inp in inputs]

  # Verifies that all replicas have matching numbers and types of inputs
  input_types = [x.dtype for x in inputs[0]]
  input_arity = len(input_types)
  for i in range(num_replicas):
    if len(inputs[i]) != input_arity:
      raise ValueError("Replicas must have the same number of inputs. "
                       "Replica 0 had {} inputs, replica {} had {} "
                       "inputs.".format(input_arity, i, len(inputs[i])))

    types = [x.dtype for x in inputs[i]]
    if types != input_types:
      raise ValueError(
          "Replicas must have matching input types. Replica 0 had "
          "input types {}, replica {} had input types {}".format(
              input_types, i, types))

  arg_error = xla.check_function_argument_count(
      computation, input_arity, infeed_queue)
  if arg_error is not None:
    if infeed_queue is None:
      raise TypeError(
          "Supplied computation cannot be called with the specified inputs. "
          "You specified %d inputs: %s, but the computation needs %s" % (
              input_arity, str([i.name for i in inputs[0]]), arg_error))
    else:
#.........这里部分代码省略.........

 def test_basic(self):
   compatibility_date = self._compatibility_date()
   one_day_before = self._n_days_after(-1)
   self.assertTrue(compat.forward_compatible(*one_day_before))
   self.assertFalse(compat.forward_compatible(*compatibility_date))

#.........这里部分代码省略.........
      tf_logging.info: "log(info)",
      tf_logging.WARN: "log(warning)",
      tf_logging.warning: "log(warning)",
      tf_logging.warn: "log(warning)",
      tf_logging.ERROR: "log(error)",
      tf_logging.error: "log(error)",
  }

  if _is_filepath(output_stream):
    output_stream_string = output_stream
  else:
    output_stream_string = output_stream_to_constant.get(output_stream)
    if not output_stream_string:
      raise ValueError("Unsupported output stream, logging level, or file." +
                       str(output_stream) +
                       ". Supported streams are sys.stdout, "
                       "sys.stderr, tf.logging.info, "
                       "tf.logging.warning, tf.logging.error. " +
                       "File needs to be in the form of 'file://<filepath>'.")

  # If we are only printing a single string scalar, there is no need to format
  if (len(inputs) == 1 and tensor_util.is_tensor(inputs[0]) and
      (not isinstance(inputs[0], sparse_tensor.SparseTensor)) and
      (inputs[0].shape.ndims == 0) and (inputs[0].dtype == dtypes.string)):
    formatted_string = inputs[0]
  # Otherwise, we construct an appropriate template for the tensors we are
  # printing, and format the template using those tensors.
  else:
    # For each input to this print function, we extract any nested tensors,
    # and construct an appropriate template to format representing the
    # printed input.
    templates = []
    tensors = []
    tensor_free_structure = nest.map_structure(
        lambda x: "" if tensor_util.is_tensor(x) else x, inputs)
    tensor_free_template = " ".join(
        pprint.pformat(x) for x in tensor_free_structure)
    placeholder = _generate_placeholder_string(tensor_free_template)

    for input_ in inputs:
      placeholders = []
      # Use the nest utilities to flatten & process any nested elements in this
      # input. The placeholder for a tensor in the template should be the
      # placeholder string, and the placeholder for a non-tensor can just be
      # the printed value of the non-tensor itself.
      for x in nest.flatten(input_):
        # support sparse tensors
        if isinstance(x, sparse_tensor.SparseTensor):
          tensors.extend([x.indices, x.values, x.dense_shape])
          placeholders.append(
              "SparseTensor(indices={}, values={}, shape={})".format(
                  placeholder, placeholder, placeholder))
        elif tensor_util.is_tensor(x):
          tensors.append(x)
          placeholders.append(placeholder)
        else:
          placeholders.append(x)

      if isinstance(input_, six.string_types):
        # If the current input to format/print is a normal string, that string
        # can act as the template.
        cur_template = input_
      else:
        # We pack the placeholders into a data structure that matches the
        # input data structure format, then format that data structure
        # into a string template.
        #
        # NOTE: We must use pprint.pformat here for building the template for
        # unordered data structures such as `dict`, because `str` doesn't
        # guarantee orderings, while pprint prints in sorted order. pprint
        # will match the ordering of `nest.flatten`.
        # This even works when nest.flatten reorders OrderedDicts, because
        # pprint is printing *after* the OrderedDicts have been reordered.
        cur_template = pprint.pformat(
            nest.pack_sequence_as(input_, placeholders))
      templates.append(cur_template)

    # We join the templates for the various inputs into a single larger
    # template. We also remove all quotes surrounding the placeholders, so that
    # the formatted/printed output will not contain quotes around tensors.
    # (example of where these quotes might appear: if we have added a
    # placeholder string into a list, then pretty-formatted that list)
    template = sep.join(templates)
    template = template.replace("'" + placeholder + "'", placeholder)
    formatted_string = string_ops.string_format(
        inputs=tensors,
        template=template,
        placeholder=placeholder,
        summarize=summarize,
        name=format_name)

  if compat.forward_compatible(2019, 5, 27):
    return gen_logging_ops.print_v2(
        formatted_string, output_stream=output_stream_string, name=name,
        end=end)
  else:
    if end == os.linesep:
      end = ""
    return gen_logging_ops.print_v2(
        formatted_string + end, output_stream=output_stream_string, name=name)