def piecewise_constant(x, boundaries, values, name=None):
  """ Piecewise constant from boundaries and interval values.

  Example: use a learning rate that's 1.0 for the first 100000 steps, 0.5
    for steps 100001 to 110000, and 0.1 for any additional steps.

  ```python
  global_step = tf.Variable(0, trainable=False)
  boundaries = [100000, 110000]
  values = [1.0, 0.5, 0.1]
  learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)

  # Later, whenever we perform an optimization step, we increment global_step.
  ```

  Args:
    x: A 0-D scalar `Tensor`. Must be one of the following types: `float32`,
      `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`.
    boundaries: A list of `Tensor`s or `int`s or `float`s with strictly
      increasing entries, and with all elements having the same type as `x`.
    values: A list of `Tensor`s or float`s or `int`s that specifies the values
      for the intervals defined by `boundaries`. It should have one more element
      than `boundaries`, and all elements should have the same type.
    name: A string. Optional name of the operation. Defaults to
      'PiecewiseConstant'.

  Returns:
    A 0-D Tensor. Its value is `values[0]` when `x <= boundaries[0]`,
    `values[1]` when `x > boundaries[0]` and `x <= boundaries[1]`, ...,
    and values[-1] when `x > boundaries[-1]`.
  """

  with ops.name_scope(name, 'PiecewiseConstant',
                      [x, boundaries, values, name]) as name:
    x = ops.convert_to_tensor(x)
    # Avoid explicit conversion to x's dtype. This could result in faulty
    # comparisons, for example if floats are converted to integers.
    boundaries = ops.convert_n_to_tensor(boundaries)
    if not all(b.dtype == x.dtype for b in boundaries):
      raise ValueError('boundaries must have the same dtype as x.')
    # TODO(rdipietro): Ensure that boundaries' elements are strictly increasing.
    values = ops.convert_n_to_tensor(values)
    if not all(v.dtype == values[0].dtype for v in values):
      raise ValueError('values must have elements all with the same dtype.')

    pred_fn_pairs = {}
    pred_fn_pairs[x <= boundaries[0]] = lambda: values[0]
    pred_fn_pairs[x > boundaries[-1]] = lambda: values[-1]
    for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]):
      # Need to bind v here; can do this with lambda v=v: ...
      pred = (x > low) & (x <= high)
      pred_fn_pairs[pred] = lambda v=v: v

    # The default isn't needed here because our conditions are mutually
    # exclusive and exhaustive, but tf.case requires it.
    default = lambda: values[0]
    return control_flow_ops.case(pred_fn_pairs, default, exclusive=True)

 def testInt(self):
   np.random.seed(54321)
   x = [np.random.randint(-128, 128, (5, 4, 3, 2, 1)) for _ in range(6)]
   tf_x = ops.convert_n_to_tensor(x)
   with self.test_session(use_gpu=True):
     self.assertAllEqual(sum(x), math_ops.accumulate_n(tf_x).eval())
     self.assertAllEqual(x[0] * 6, math_ops.accumulate_n([tf_x[0]] * 6).eval())

 def testFloat(self):
   np.random.seed(12345)
   x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
   tf_x = ops.convert_n_to_tensor(x)
   with self.test_session(use_gpu=True):
     self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).eval())
     self.assertAllClose(x[0] * 5, math_ops.accumulate_n([tf_x[0]] * 5).eval())

  def test_mean(self):
    m = metrics.Mean(name='my_mean')

    # check config
    self.assertEqual(m.name, 'my_mean')
    self.assertTrue(m.stateful)
    self.assertEqual(m.dtype, dtypes.float32)
    self.assertEqual(len(m.variables), 2)
    self.evaluate(variables.global_variables_initializer())

    # check initial state
    self.assertEqual(self.evaluate(m.total), 0)
    self.assertEqual(self.evaluate(m.count), 0)

    # check __call__()
    self.assertEqual(self.evaluate(m(100)), 100)
    self.assertEqual(self.evaluate(m.total), 100)
    self.assertEqual(self.evaluate(m.count), 1)

    # check update_state() and result() + state accumulation + tensor input
    update_op = m.update_state(ops.convert_n_to_tensor([1, 5]))
    self.evaluate(update_op)
    self.assertAlmostEqual(self.evaluate(m.result()), 106 / 3, 2)
    self.assertEqual(self.evaluate(m.total), 106)  # 100 + 1 + 5
    self.assertEqual(self.evaluate(m.count), 3)

    # check reset_states()
    m.reset_states()
    self.assertEqual(self.evaluate(m.total), 0)
    self.assertEqual(self.evaluate(m.count), 0)

def xla_launch_eager_fallback(constants, args, resources, Tresults, function, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function xla_launch
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(resources, (list, tuple)):
    raise TypeError(
        "Expected list for 'resources' argument to "
        "'xla_launch' Op, not %r." % resources)
  _attr_Nresources = len(resources)
  if not isinstance(Tresults, (list, tuple)):
    raise TypeError(
        "Expected list for 'Tresults' argument to "
        "'xla_launch' Op, not %r." % Tresults)
  Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
  _attr_Tconstants, constants = _execute.convert_to_mixed_eager_tensors(constants, _ctx)
  _attr_Targs, args = _execute.convert_to_mixed_eager_tensors(args, _ctx)
  resources = _ops.convert_n_to_tensor(resources, _dtypes.resource)
  _inputs_flat = list(constants) + list(args) + list(resources)
  _attrs = ("Tconstants", _attr_Tconstants, "Targs", _attr_Targs,
  "Nresources", _attr_Nresources, "Tresults", Tresults, "function", function)
  _result = _execute.execute(b"XlaLaunch", len(Tresults), inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "XlaLaunch", _inputs_flat, _attrs, _result, name)
  return _result

 def testFloat(self):
   np.random.seed(12345)
   x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
   tf_x = ops.convert_n_to_tensor(x)
   self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x))
   self.assertAllClose(x[0] * 5,
                       math_ops.accumulate_n([tf_x[0]] * 5))

  def testBuild(self):
    graph = graph_pb2.GraphDef()
    node = graph.node.add()
    node.name = "a"
    node.op = "op0"
    node = graph.node.add()
    node.name = "b"
    node.op = "op1"
    inputs = [ops.convert_n_to_tensor([1], dtypes.int64)]
    output_types = [np.int64, np.int64]
    graph_input_node_names = ["a"]
    graph_output_node_names = ["a", "b"]
    executor_name = ""
    serialized_executor_parameters = b""
    default_graph_input_tensor_type_shapes = [[dtypes.int64, [1]]]
    default_graph_output_tensor_type_shapes = [[dtypes.int64, [1]],
                                               [dtypes.int64, [1]]]

    output_nodes = remote_fused_graph_ops.remote_fused_graph_execute(
        inputs, output_types, graph, graph_input_node_names,
        graph_output_node_names, executor_name, serialized_executor_parameters,
        default_graph_input_tensor_type_shapes,
        default_graph_output_tensor_type_shapes)
    self.assertEqual(2, len(output_nodes))
    for output_node in output_nodes:
      with self.test_session(use_gpu=False):
        output_node.eval()

def grow_tree_ensemble_eager_fallback(tree_ensemble_handle, stamp_token, next_stamp_token, learning_rate, dropout_seed, max_tree_depth, weak_learner_type, partition_ids, gains, splits, learner_config, center_bias, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function grow_tree_ensemble
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(partition_ids, (list, tuple)):
    raise TypeError(
        "Expected list for 'partition_ids' argument to "
        "'grow_tree_ensemble' Op, not %r." % partition_ids)
  _attr_num_handlers = len(partition_ids)
  if not isinstance(gains, (list, tuple)):
    raise TypeError(
        "Expected list for 'gains' argument to "
        "'grow_tree_ensemble' Op, not %r." % gains)
  if len(gains) != _attr_num_handlers:
    raise ValueError(
        "List argument 'gains' to 'grow_tree_ensemble' Op with length %d "
        "must match length %d of argument 'partition_ids'." %
        (len(gains), _attr_num_handlers))
  if not isinstance(splits, (list, tuple)):
    raise TypeError(
        "Expected list for 'splits' argument to "
        "'grow_tree_ensemble' Op, not %r." % splits)
  if len(splits) != _attr_num_handlers:
    raise ValueError(
        "List argument 'splits' to 'grow_tree_ensemble' Op with length %d "
        "must match length %d of argument 'partition_ids'." %
        (len(splits), _attr_num_handlers))
  learner_config = _execute.make_str(learner_config, "learner_config")
  center_bias = _execute.make_bool(center_bias, "center_bias")
  tree_ensemble_handle = _ops.convert_to_tensor(tree_ensemble_handle, _dtypes.resource)
  stamp_token = _ops.convert_to_tensor(stamp_token, _dtypes.int64)
  next_stamp_token = _ops.convert_to_tensor(next_stamp_token, _dtypes.int64)
  learning_rate = _ops.convert_to_tensor(learning_rate, _dtypes.float32)
  dropout_seed = _ops.convert_to_tensor(dropout_seed, _dtypes.int64)
  max_tree_depth = _ops.convert_to_tensor(max_tree_depth, _dtypes.int32)
  weak_learner_type = _ops.convert_to_tensor(weak_learner_type, _dtypes.int32)
  partition_ids = _ops.convert_n_to_tensor(partition_ids, _dtypes.int32)
  gains = _ops.convert_n_to_tensor(gains, _dtypes.float32)
  splits = _ops.convert_n_to_tensor(splits, _dtypes.string)
  _inputs_flat = [tree_ensemble_handle, stamp_token, next_stamp_token, learning_rate, dropout_seed, max_tree_depth, weak_learner_type] + list(partition_ids) + list(gains) + list(splits)
  _attrs = ("learner_config", learner_config, "num_handlers",
  _attr_num_handlers, "center_bias", center_bias)
  _result = _execute.execute(b"GrowTreeEnsemble", 0, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _result = None
  return _result

 def testFloat(self):
   np.random.seed(12345)
   x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
   tf_x = ops.convert_n_to_tensor(x)
   for u in tf_x:
     print("shape=%s" % u.get_shape())
   with self.test_session():
     self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).eval())
     self.assertAllClose(x[0] * 5, math_ops.accumulate_n([tf_x[0]] * 5).eval())

  def decayed_lr(x, boundaries, values, name):
    """Helper to recompute learning rate; most helpful in eager-mode."""
    with ops.name_scope(name, "PiecewiseConstant",
                        [x, boundaries, values, name]) as name:
      boundaries = ops.convert_n_to_tensor(boundaries)
      values = ops.convert_n_to_tensor(values)
      x_recomp = ops.convert_to_tensor(x)
      # Avoid explicit conversion to x's dtype. This could result in faulty
      # comparisons, for example if floats are converted to integers.
      for i, b in enumerate(boundaries):
        if b.dtype.base_dtype != x_recomp.dtype.base_dtype:
          # We can promote int32 boundaries to int64 without loss of precision.
          # This covers the most common case where the user passes in boundaries
          # as an array of Python integers.
          if (b.dtype.base_dtype == dtypes.int32 and
              x_recomp.dtype.base_dtype == dtypes.int64):
            b = math_ops.cast(b, x_recomp.dtype.base_dtype)
            boundaries[i] = b
          else:
            raise ValueError(
                "Boundaries (%s) must have the same dtype as x (%s)." %
                (b.dtype.base_dtype, x_recomp.dtype.base_dtype))
      # TODO(rdipietro): Ensure that boundaries' elements strictly increases.
      for v in values[1:]:
        if v.dtype.base_dtype != values[0].dtype.base_dtype:
          raise ValueError(
              "Values must have elements all with the same dtype (%s vs %s)." %
              (values[0].dtype.base_dtype, v.dtype.base_dtype))
      pred_fn_pairs = []
      pred_fn_pairs.append((x_recomp <= boundaries[0], lambda: values[0]))
      pred_fn_pairs.append((x_recomp > boundaries[-1], lambda: values[-1]))
      for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]):
        # Need to bind v here; can do this with lambda v=v: ...
        pred = (x_recomp > low) & (x_recomp <= high)
        pred_fn_pairs.append((pred, lambda v=v: v))

      # The default isn't needed here because our conditions are mutually
      # exclusive and exhaustive, but tf.case requires it.
      default = lambda: values[0]
      return control_flow_ops.case(pred_fn_pairs, default, exclusive=True)

def sparse_feature_cross_v2_eager_fallback(indices, values, shapes, dense, hashed_output, num_buckets, hash_key, out_type, internal_type, name=None, ctx=None):
  r"""This is the slowpath function for Eager mode.
  This is for function sparse_feature_cross_v2
  """
  _ctx = ctx if ctx else _context.context()
  if not isinstance(indices, (list, tuple)):
    raise TypeError(
        "Expected list for 'indices' argument to "
        "'sparse_feature_cross_v2' Op, not %r." % indices)
  _attr_N = len(indices)
  if not isinstance(shapes, (list, tuple)):
    raise TypeError(
        "Expected list for 'shapes' argument to "
        "'sparse_feature_cross_v2' Op, not %r." % shapes)
  if len(shapes) != _attr_N:
    raise ValueError(
        "List argument 'shapes' to 'sparse_feature_cross_v2' Op with length %d "
        "must match length %d of argument 'indices'." %
        (len(shapes), _attr_N))
  hashed_output = _execute.make_bool(hashed_output, "hashed_output")
  num_buckets = _execute.make_int(num_buckets, "num_buckets")
  hash_key = _execute.make_int(hash_key, "hash_key")
  out_type = _execute.make_type(out_type, "out_type")
  internal_type = _execute.make_type(internal_type, "internal_type")
  _attr_sparse_types, values = _execute.convert_to_mixed_eager_tensors(values, _ctx)
  _attr_dense_types, dense = _execute.convert_to_mixed_eager_tensors(dense, _ctx)
  indices = _ops.convert_n_to_tensor(indices, _dtypes.int64)
  shapes = _ops.convert_n_to_tensor(shapes, _dtypes.int64)
  _inputs_flat = list(indices) + list(values) + list(shapes) + list(dense)
  _attrs = ("N", _attr_N, "hashed_output", hashed_output, "num_buckets",
  num_buckets, "hash_key", hash_key, "sparse_types", _attr_sparse_types,
  "dense_types", _attr_dense_types, "out_type", out_type, "internal_type",
  internal_type)
  _result = _execute.execute(b"SparseFeatureCrossV2", 3, inputs=_inputs_flat,
                             attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "SparseFeatureCrossV2", _inputs_flat, _attrs, _result, name)
  _result = _SparseFeatureCrossV2Output._make(_result)
  return _result

  def apply_op(self, op_type_name, name=None, **keywords):
    # pylint: disable=g-doc-args
    """Add a node invoking a registered Op to a graph.

    Example usage:
       # input1 and input2 can be Tensors or anything ops.convert_to_tensor()
       # will convert to a Tensor.
       op_def_library.apply_op("op", input1=input1, input2=input2)
       # Can specify a node name.
       op_def_library.apply_op("op", input1=input1, name="node_name")
       # Must use keyword arguments, with the names specified in the OpDef.
       op_def_library.apply_op("op", input_name=input, attr_name=attr)

    All attrs must either be inferred from an input or specified.
    (If inferred, the attr must not be specified.)  If an attr has a default
    value specified in the Op's OpDef, then you may pass None as the value
    of that attr to get the default.

    Args:
      op_type_name: string. Must match the name field of a registered Op.
      name: string. Optional name of the created op.
      **keywords: input Tensor and attr arguments specified by name,
        and optional parameters to pass when constructing the Operation.

    Returns:
      The Tensor(s) representing the output of the operation, or the Operation
      itself if there are no outputs.

    Raises:
      RuntimeError: On some errors.
      TypeError: On some errors.
      ValueError: On some errors.
    """
    output_structure, is_stateful, op = self._apply_op_helper(
        op_type_name, name, **keywords)
    if output_structure:
      outputs = op.outputs
      res = _Restructure(ops.convert_n_to_tensor(outputs), output_structure)
      if isinstance(res, list) and not res and is_stateful:
        return op
      else:
        return res
    else:
      return op

#.........这里部分代码省略.........
        #   the type indicated by the attrs (if they have already been
        #   inferred via an earlier input).
        # * If the input_arg has an explicit type, make sure the input
        #   conforms.

        if _IsListParameter(input_arg):
          if not _IsListValue(values):
            raise TypeError(
                "Expected list for '%s' argument to '%s' Op, not %s." %
                (input_name, op_type_name, values))
          # In cases where we expect all elements of the list to have the
          # same dtype, try to cast non-Tensor elements to that type.
          dtype = None
          default_dtype = None
          if input_arg.type != types_pb2.DT_INVALID:
            dtype = input_arg.type
          elif input_arg.number_attr:
            if input_arg.type_attr in attrs:
              dtype = attrs[input_arg.type_attr]
            else:
              for t in values:
                if isinstance(t, ops.Tensor):
                  dtype = t.dtype
                  break

            # dtype still not found, prefer using the default dtype
            # from the attr.
            if dtype is None and input_arg.type_attr in default_type_attr_map:
              default_dtype = default_type_attr_map[input_arg.type_attr]

          try:
            if not input_arg.is_ref and dtype:
              dtype = dtypes.as_dtype(dtype).base_dtype
            values = ops.convert_n_to_tensor(
                values,
                name=input_arg.name,
                dtype=dtype if dtype else None,
                preferred_dtype=default_dtype,
                as_ref=input_arg.is_ref)
            if input_arg.number_attr and len(
                set(v.dtype.base_dtype for v in values)) > 1:
              raise TypeError()  # All types should match.
          except (TypeError, ValueError):
            # What types does the conversion function think values have?
            observed_types = []
            for value in values:
              try:
                converted_value = ops.convert_to_tensor(
                    value, as_ref=input_arg.is_ref)
                observed_types.append(converted_value.dtype.base_dtype.name)
              except (TypeError, ValueError):
                observed_types.append("<NOT CONVERTIBLE TO TENSOR>")
            observed = ", ".join(observed_types)

            prefix = (
                "Tensors in list passed to '%s' of '%s' Op have types [%s]" %
                (input_name, op_type_name, observed))
            if input_arg.number_attr:
              if input_arg.type != types_pb2.DT_INVALID:
                raise TypeError("%s that do not match expected type %s." %
                                (prefix, dtype.name))
              elif input_arg.type_attr in attrs:
                raise TypeError("%s that do not match type %s inferred from "
                                "earlier arguments." %
                                (prefix, dtype.name))
              else:

  def __init__(self,
               filenames,
               record_defaults,
               buffer_size=None,
               header=False,
               field_delim=",",
               use_quote_delim=True,
               na_value="",
               select_cols=None):
    """Creates a `CsvDataset` by reading and decoding CSV files.

    The elements of this dataset correspond to records from the file(s).
    RFC 4180 format is expected for CSV files
    (https://tools.ietf.org/html/rfc4180)
    Note that we allow leading and trailing spaces with int or float field.


    For example, suppose we have a file 'my_file0.csv' with four CSV columns of
    different data types:
    ```
    abcdefg,4.28E10,5.55E6,12
    hijklmn,-5.3E14,,2
    ```

    We can construct a CsvDataset from it as follows:
    ```python
    dataset = tf.contrib.data.CsvDataset(
      "my_file*.csv",
      [tf.float32,  # Required field, use dtype or empty tensor
       tf.constant([0.0], dtype=tf.float32),  # Optional field, default to 0.0
       tf.int32,  # Required field, use dtype or empty tensor
       ],
      select_cols=[1,2,3]  # Only parse last three columns
    )
    ```

    The expected output of its iterations is:
    ```python
    next = dataset.make_one_shot_iterator().get_next()
    with tf.Session() as sess:
      while True:
        try:
          print(sess.run(nxt))
        except tf.errors.OutOfRangeError:
          break

    >> (4.28e10, 5.55e6, 12)
    >> (-5.3e14, 0.0, 2)
    ```

    Args:
      filenames: A `tf.string` tensor containing one or more filenames.
      record_defaults: A list of default values for the CSV fields. Each item in
        the list is either a valid CSV `DType` (float32, float64, int32, int64,
        string), or a `Tensor` object with one of the above types. One per
        column of CSV data, with either a scalar `Tensor` default value for the
        column if it is optional, or `DType` or empty `Tensor` if required. If
        both this and `select_columns` are specified, these must have the same
        lengths, and `column_defaults` is assumed to be sorted in order of
        increasing column index.
      buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
        to buffer while reading files. Defaults to 4MB.
      header: (Optional.) A `tf.bool` scalar indicating whether the CSV file(s)
        have header line(s) that should be skipped when parsing. Defaults to
        `False`.
      field_delim: (Optional.) A `tf.string` scalar containing the delimiter
        character that separates fields in a record. Defaults to `","`.
      use_quote_delim: (Optional.) A `tf.bool` scalar. If `False`, treats
        double quotation marks as regular characters inside of string fields
        (ignoring RFC 4180, Section 2, Bullet 5). Defaults to `True`.
      na_value: (Optional.) A `tf.string` scalar indicating a value that will
        be treated as NA/NaN.
      select_cols: (Optional.) A sorted list of column indices to select from
        the input data. If specified, only this subset of columns will be
        parsed. Defaults to parsing all columns.
    """
    super(CsvDataset, self).__init__()
    self._filenames = ops.convert_to_tensor(
        filenames, dtype=dtypes.string, name="filenames")
    record_defaults = [
        constant_op.constant([], dtype=x) if x in _ACCEPTABLE_CSV_TYPES else x
        for x in record_defaults
    ]
    self._record_defaults = ops.convert_n_to_tensor(
        record_defaults, name="record_defaults")
    self._buffer_size = convert.optional_param_to_tensor(
        "buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)
    self._header = ops.convert_to_tensor(
        header, dtype=dtypes.bool, name="header")
    self._field_delim = ops.convert_to_tensor(
        field_delim, dtype=dtypes.string, name="field_delim")
    self._use_quote_delim = ops.convert_to_tensor(
        use_quote_delim, dtype=dtypes.bool, name="use_quote_delim")
    self._na_value = ops.convert_to_tensor(
        na_value, dtype=dtypes.string, name="na_value")
    self._select_cols = convert.optional_param_to_tensor(
        "select_cols",
        select_cols,
        argument_default=[],
        argument_dtype=dtypes.int64,
#.........这里部分代码省略.........

def piecewise_constant(x, boundaries, values, name=None):
  """Piecewise constant from boundaries and interval values.

  Example: use a learning rate that's 1.0 for the first 100000 steps, 0.5
    for steps 100001 to 110000, and 0.1 for any additional steps.

  ```python
  global_step = tf.Variable(0, trainable=False)
  boundaries = [100000, 110000]
  values = [1.0, 0.5, 0.1]
  learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)

  # Later, whenever we perform an optimization step, we increment global_step.
  ```

  Args:
    x: A 0-D scalar `Tensor`. Must be one of the following types: `float32`,
      `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`.
    boundaries: A list of `Tensor`s or `int`s or `float`s with strictly
      increasing entries, and with all elements having the same type as `x`.
    values: A list of `Tensor`s or float`s or `int`s that specifies the values
      for the intervals defined by `boundaries`. It should have one more element
      than `boundaries`, and all elements should have the same type.
    name: A string. Optional name of the operation. Defaults to
      'PiecewiseConstant'.

  Returns:
    A 0-D Tensor. Its value is `values[0]` when `x <= boundaries[0]`,
    `values[1]` when `x > boundaries[0]` and `x <= boundaries[1]`, ...,
    and values[-1] when `x > boundaries[-1]`.

  Raises:
    ValueError: if types of `x` and `boundaries` do not match, or types of all
        `values` do not match or
        the number of elements in the lists does not match.
  """
  if len(boundaries) != len(values) - 1:
    raise ValueError(
        "The length of boundaries should be 1 less than the length of values")
  with ops.name_scope(name, "PiecewiseConstant",
                      [x, boundaries, values, name]) as name:
    x = ops.convert_to_tensor(x)
    # Avoid explicit conversion to x's dtype. This could result in faulty
    # comparisons, for example if floats are converted to integers.
    boundaries = ops.convert_n_to_tensor(boundaries)
    for i, b in enumerate(boundaries):
      if b.dtype.base_dtype != x.dtype.base_dtype:
        # We can promote int32 boundaries to int64 without loss of precision.
        # This covers the most common case where the user passes in boundaries
        # as an array of Python integers.
        if (b.dtype.base_dtype == dtypes.int32 and
            x.dtype.base_dtype == dtypes.int64):
          b = math_ops.cast(b, x.dtype.base_dtype)
          boundaries[i] = b
        else:
          raise ValueError(
              "Boundaries (%s) must have the same dtype as x (%s)." % (
                  b.dtype.base_dtype, x.dtype.base_dtype))
    # TODO(rdipietro): Ensure that boundaries' elements are strictly increasing.
    values = ops.convert_n_to_tensor(values)
    for v in values[1:]:
      if v.dtype.base_dtype != values[0].dtype.base_dtype:
        raise ValueError(
            "Values must have elements all with the same dtype (%s vs %s)." % (
                values[0].dtype.base_dtype, v.dtype.base_dtype))
    pred_fn_pairs = []
    pred_fn_pairs.append((x <= boundaries[0], lambda: values[0]))
    pred_fn_pairs.append((x > boundaries[-1], lambda: values[-1]))
    for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]):
      # Need to bind v here; can do this with lambda v=v: ...
      pred = (x > low) & (x <= high)
      pred_fn_pairs.append((pred, lambda v=v: v))

    # The default isn't needed here because our conditions are mutually
    # exclusive and exhaustive, but tf.case requires it.
    default = lambda: values[0]
    return control_flow_ops.case(pred_fn_pairs, default, exclusive=True)