Python tensorflow.python.ops.math_ops.range() Examples

def _mean(self):
    with ops.control_dependencies(self._assertions):
      distribution_means = [d.mean() for d in self.components]
      cat_probs = self._cat_probs(log_probs=False)
      # This was checked to not be None at construction time.
      static_event_rank = self.event_shape.ndims
      # Expand the rank of x up to static_event_rank times so that
      # broadcasting works correctly.
      def expand(x):
        expanded_x = x
        for _ in range(static_event_rank):
          expanded_x = array_ops.expand_dims(expanded_x, -1)
        return expanded_x
      cat_probs = [expand(c_p) for c_p in cat_probs]
      partial_means = [
          c_p * m for (c_p, m) in zip(cat_probs, distribution_means)
      ]
      # These should all be the same shape by virtue of matching
      # batch_shape and event_shape.
      return math_ops.add_n(partial_means) 

def unstack(self, value, name=None):
    """Unstack the values of a `Tensor` in the TensorArray.

    If input value shapes have rank-`R`, then the output TensorArray will
    contain elements whose shapes are rank-`(R-1)`.

    Args:
      value: (N+1)-D.  Tensor of type `dtype`.  The Tensor to unstack.
      name: A name for the operation (optional).

    Returns:
      A new TensorArray object with flow that ensures the unstack occurs.
      Use this object all for subsequent operations.

    Raises:
      ValueError: if the shape inference fails.
    """
    with ops.name_scope(name, "TensorArrayUnstack", [self._handle, value]):
      num_elements = array_ops.shape(value)[0]
      return self.scatter(
          indices=math_ops.range(0, num_elements), value=value, name=name) 

def _TileGrad(op, grad):
  """Sum reduces grad along the tiled dimensions."""
  assert isinstance(grad, ops.Tensor)
  input_shape = array_ops.shape(op.inputs[0])
  # We interleave multiples and input_shape to get split_shape,
  # reshape grad to split_shape, and reduce along all even
  # dimensions (the tiled dimensions) to get the result
  # with shape input_shape.  For example
  #   input_shape = [20, 30, 40]
  #   multiples = [2, 3, 4]
  #   split_shape = [2, 20, 3, 30, 4, 40]
  #   axes = [0, 2, 4]
  split_shape = array_ops.reshape(
      array_ops.transpose(array_ops.stack([op.inputs[1], input_shape])), [-1])
  axes = math_ops.range(0, array_ops.size(split_shape), 2)
  input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes)
  # Fix shape inference
  input_grad.set_shape(op.inputs[0].get_shape())
  return [input_grad, None] 

def insert(self, keys, values, name=None):
    self._check_keys(keys)
    num_shards = self._num_shards
    if num_shards == 1:
      return self._table_shards[0].insert(keys, values, name=name)

    shard_indices = self._shard_indices(keys)
    # TODO(andreasst): support 'keys' that are not vectors
    key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
                                                 num_shards)
    value_shards = data_flow_ops.dynamic_partition(values, shard_indices,
                                                   num_shards)
    return_values = [
        self._table_shards[i].insert(key_shards[i], value_shards[i], name=name)
        for i in range(num_shards)
    ]

    return control_flow_ops.group(*return_values) 

def _event_dims_tensor(self, sample):
    """Return a 1D `int32` tensor: `range(rank(sample))[-event_ndims:]`."""
    if self.event_ndims is None:
      raise ValueError("Jacobian cannot be computed with unknown event_ndims")
    static_event_ndims = tensor_util.constant_value(self.event_ndims)
    static_rank = sample.get_shape().ndims
    if static_event_ndims is not None and static_rank is not None:
      return ops.convert_to_tensor(
          static_rank + np.arange(-static_event_ndims, 0).astype(np.int32))

    if static_event_ndims is not None:
      event_range = np.arange(-static_event_ndims, 0).astype(np.int32)
    else:
      event_range = math_ops.range(-self.event_ndims, 0, dtype=dtypes.int32)

    if static_rank is not None:
      return event_range + static_rank
    else:
      return event_range + array_ops.rank(sample) 

def _reduce_join_reduction_dims(x, axis, reduction_indices):
  """Returns range(rank(x) - 1, 0, -1) if reduction_indices is None."""
  # TODO(aselle): Remove this after deprecation
  if reduction_indices is not None:
    if axis is not None:
      raise ValueError("Can't specify both 'axis' and 'reduction_indices'.")
    axis = reduction_indices
  if axis is not None:
    return axis
  else:
    # Fast path: avoid creating Rank and Range ops if ndims is known.
    if isinstance(x, ops.Tensor) and x.get_shape().ndims is not None:
      return constant_op.constant(
          np.arange(x.get_shape().ndims - 1, -1, -1), dtype=dtypes.int32)

    # Otherwise, we rely on Range and Rank to do the right thing at run-time.
    return math_ops.range(array_ops.rank(x) - 1, -1, -1) 

def _BiasAddGradV1(unused_bias_op, received_grad):
  """Return the gradients for the 2 inputs of bias_op.

  The first input of unused_bias_op is the tensor t, and its gradient is
  just the gradient the unused_bias_op received.

  The second input of unused_bias_op is the bias vector which has one fewer
  dimension than "received_grad" (the batch dimension.)  Its gradient is the
  received gradient Summed on the batch dimension, which is the first dimension.

  Args:
    unused_bias_op: The BiasOp for which we need to generate gradients.
    received_grad: Tensor.  The gradients passed to the BiasOp.

  Returns:
    Two tensors, the first one for the "tensor" input of the BiasOp,
    the second one for the "bias" input of the BiasOp.
  """
  reduction_dim_tensor = math_ops.range(array_ops.rank(received_grad) - 1)
  return (received_grad, math_ops.reduce_sum(received_grad,
                                             reduction_dim_tensor)) 

def __init__(self,
               key_dtype,
               value_dtype,
               default_value,
               empty_key,
               num_shards=1,
               name='ShardedMutableHashTable'):
    with ops.name_scope(name, 'sharded_mutable_hash_table') as scope:
      super(ShardedMutableDenseHashTable, self).__init__(key_dtype,
                                                         value_dtype, scope)
      table_shards = []
      for i in range(num_shards):
        table_shards.append(
            lookup.MutableDenseHashTable(
                key_dtype=key_dtype,
                value_dtype=value_dtype,
                default_value=default_value,
                empty_key=empty_key,
                name='%s-%d-of-%d' % (name, i + 1, num_shards)))
      self._table_shards = table_shards
      # TODO(andreasst): add a value_shape() method to LookupInterface
      # pylint: disable=protected-access
      self._value_shape = self._table_shards[0]._value_shape
      # pylint: enable=protected-access 

def _lengths_to_masks(lengths, max_length):
  """Creates a binary matrix that can be used to mask away padding.

  Args:
    lengths: A vector of integers representing lengths.
    max_length: An integer indicating the maximum length. All values in
      lengths should be less than max_length.
  Returns:
    masks: Masks that can be used to get rid of padding.
  """
  tiled_ranges = array_ops.tile(
      array_ops.expand_dims(math_ops.range(max_length), 0),
      [array_ops.shape(lengths)[0], 1])
  lengths = array_ops.expand_dims(lengths, 1)
  masks = math_ops.to_float(
      math_ops.to_int64(tiled_ranges) < math_ops.to_int64(lengths))
  return masks 

def _BiasAddGradV1(unused_bias_op, received_grad):
  """Return the gradients for the 2 inputs of bias_op.

  The first input of unused_bias_op is the tensor t, and its gradient is
  just the gradient the unused_bias_op received.

  The second input of unused_bias_op is the bias vector which has one fewer
  dimension than "received_grad" (the batch dimension.)  Its gradient is the
  received gradient Summed on the batch dimension, which is the first dimension.

  Args:
    unused_bias_op: The BiasOp for which we need to generate gradients.
    received_grad: Tensor.  The gradients passed to the BiasOp.

  Returns:
    Two tensors, the first one for the "tensor" input of the BiasOp,
    the second one for the "bias" input of the BiasOp.
  """
  reduction_dim_tensor = math_ops.range(array_ops.rank(received_grad) - 1)
  return (received_grad, math_ops.reduce_sum(received_grad,
                                             reduction_dim_tensor)) 

def _reduce_join_reduction_dims(x, axis, reduction_indices):
  """Returns range(rank(x) - 1, 0, -1) if reduction_indices is None."""
  # TODO(aselle): Remove this after deprecation
  if reduction_indices is not None:
    if axis is not None:
      raise ValueError("Can't specify both 'axis' and 'reduction_indices'.")
    axis = reduction_indices
  if axis is not None:
    return axis
  else:
    # Fast path: avoid creating Rank and Range ops if ndims is known.
    if isinstance(x, ops.Tensor) and x.get_shape().ndims is not None:
      return constant_op.constant(
          np.arange(x.get_shape().ndims - 1, -1, -1), dtype=dtypes.int32)

    # Otherwise, we rely on Range and Rank to do the right thing at run-time.
    return math_ops.range(array_ops.rank(x) - 1, -1, -1) 

def _lengths_to_masks(lengths, max_length):
  """Creates a binary matrix that can be used to mask away padding.

  Args:
    lengths: A vector of integers representing lengths.
    max_length: An integer indicating the maximum length. All values in
      lengths should be less than max_length.
  Returns:
    masks: Masks that can be used to get rid of padding.
  """
  tiled_ranges = array_ops.tile(
      array_ops.expand_dims(math_ops.range(max_length), 0),
      [array_ops.shape(lengths)[0], 1])
  lengths = array_ops.expand_dims(lengths, 1)
  masks = math_ops.to_float(
      math_ops.to_int64(tiled_ranges) < math_ops.to_int64(lengths))
  return masks 

def _TileGrad(op, grad):
  """Sum reduces grad along the tiled dimensions."""
  assert isinstance(grad, ops.Tensor)
  input_shape = array_ops.shape(op.inputs[0])
  # We interleave multiples and input_shape to get split_shape,
  # reshape grad to split_shape, and reduce along all even
  # dimensions (the tiled dimensions) to get the result
  # with shape input_shape.  For example
  #   input_shape = [20, 30, 40]
  #   multiples = [2, 3, 4]
  #   split_shape = [2, 20, 3, 30, 4, 40]
  #   axes = [0, 2, 4]
  split_shape = array_ops.reshape(
      array_ops.transpose(array_ops.stack([op.inputs[1], input_shape])), [-1])
  axes = math_ops.range(0, array_ops.size(split_shape), 2)
  input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes)
  # Fix shape inference
  input_grad.set_shape(op.inputs[0].get_shape())
  return [input_grad, None] 

def count_params(x):
  """Returns the number of scalars in a Keras variable.

  Arguments:
      x: Keras variable.

  Returns:
      Integer, the number of scalars in `x`.

  Example:
  ```python
      >>> kvar = K.zeros((2,3))
      >>> K.count_params(kvar)
      6
      >>> K.eval(kvar)
      array([[ 0.,  0.,  0.],
             [ 0.,  0.,  0.]], dtype=float32)
  ```
  """
  shape = x.get_shape()
  return np.prod([shape[i]._value for i in range(len(shape))]) 

def unstack(self, value, name=None):
    """Unstack the values of a `Tensor` in the TensorArray.

    If input value shapes have rank-`R`, then the output TensorArray will
    contain elements whose shapes are rank-`(R-1)`.
    Args:
      value: (N+1)-D.  Tensor of type `dtype`.  The Tensor to unstack.
      name: A name for the operation (optional).

    Returns:
      A new TensorArray object with flow that ensures the unstack occurs.
      Use this object all for subsequent operations.

    Raises:
      ValueError: if the shape inference fails.
    """
    with ops.name_scope(name, "TensorArrayUnstack", [self._handle, value]):
      num_elements = array_ops.shape(value)[0]
      return self.scatter(
          indices=math_ops.range(0, num_elements), value=value, name=name) 

def __init__(self,
               key_dtype,
               value_dtype,
               default_value,
               empty_key,
               num_shards=1,
               name='ShardedMutableHashTable'):
    with ops.name_scope(name, 'sharded_mutable_hash_table') as scope:
      super(ShardedMutableDenseHashTable, self).__init__(key_dtype,
                                                         value_dtype, scope)
      table_shards = []
      for i in range(num_shards):
        table_shards.append(
            lookup.MutableDenseHashTable(
                key_dtype=key_dtype,
                value_dtype=value_dtype,
                default_value=default_value,
                empty_key=empty_key,
                name='%s-%d-of-%d' % (name, i + 1, num_shards)))
      self._table_shards = table_shards
      # TODO(andreasst): add a value_shape() method to LookupInterface
      # pylint: disable=protected-access
      self._value_shape = self._table_shards[0]._value_shape
      # pylint: enable=protected-access 

def insert(self, keys, values, name=None):
    self._check_keys(keys)
    num_shards = self._num_shards
    if num_shards == 1:
      return self._table_shards[0].insert(keys, values, name=name)

    shard_indices = self._shard_indices(keys)
    # TODO(andreasst): support 'keys' that are not vectors
    key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
                                                 num_shards)
    value_shards = data_flow_ops.dynamic_partition(values, shard_indices,
                                                   num_shards)
    return_values = [
        self._table_shards[i].insert(key_shards[i], value_shards[i], name=name)
        for i in range(num_shards)
    ]

    return control_flow_ops.group(*return_values) 

def _transpose_batch_time(x):
    """Transpose the batch and time dimensions of a Tensor.
    Retains as much of the static shape information as possible.
    Args:
        x: A tensor of rank 2 or higher.
    Returns:
        x transposed along the first two dimensions.
    Raises:
        ValueError: if `x` is rank 1 or lower.
    """
    x_static_shape = x.get_shape()
    if x_static_shape.ndims is not None and x_static_shape.ndims < 2:
        raise ValueError(
            "Expected input tensor %s to have rank at least 2, but saw shape: %s" %
            (x, x_static_shape))
    x_rank = array_ops.rank(x)
    x_t = array_ops.transpose(
        x, array_ops.concat(
            ([1, 0], math_ops.range(2, x_rank)), axis=0))
    x_t.set_shape(
        tensor_shape.TensorShape([
            x_static_shape[1].value, x_static_shape[0].value
        ]).concatenate(x_static_shape[2:]))
    return x_t 

def _transpose_batch_time(x):
  """Transpose the batch and time dimensions of a Tensor.

  Retains as much of the static shape information as possible.

  Args:
    x: A tensor of rank 2 or higher.

  Returns:
    x transposed along the first two dimensions.

  Raises:
    ValueError: if `x` is rank 1 or lower.
  """
  x_static_shape = x.get_shape()
  if x_static_shape.ndims is not None and x_static_shape.ndims < 2:
    raise ValueError(
        "Expected input tensor %s to have rank at least 2, but saw shape: %s" %
        (x, x_static_shape))
  x_rank = array_ops.rank(x)
  x_t = array_ops.transpose(
      x, array_ops.concat(
          ([1, 0], math_ops.range(2, x_rank)), axis=0))
  x_t.set_shape(
      tensor_shape.TensorShape([
          x_static_shape[1].value, x_static_shape[0].value
      ]).concatenate(x_static_shape[2:]))
  return x_t 

def _flip_vector_to_matrix_static(vec, batch_shape):
  """flip_vector_to_matrix with static shapes."""
  # Shapes associated with batch_shape
  batch_rank = batch_shape.ndims

  # Shapes associated with vec.
  vec = ops.convert_to_tensor(vec, name="vec")
  vec_shape = vec.get_shape()
  vec_rank = len(vec_shape)
  vec_batch_rank = vec_rank - 1

  m = vec_batch_rank - batch_rank
  # vec_shape_left = [M1,...,Mm] or [].
  vec_shape_left = vec_shape[:m]
  # If vec_shape_left = [], then condensed_shape = [1] since reduce_prod([]) = 1
  # If vec_shape_left = [M1,...,Mm], condensed_shape = [M1*...*Mm]
  condensed_shape = [np.prod(vec_shape_left)]
  k = vec_shape[-1]
  new_shape = batch_shape.concatenate(k).concatenate(condensed_shape)

  def _flip_front_dims_to_back():
    # Permutation corresponding to [N1,...,Nn] + [k, M1,...,Mm]
    perm = array_ops.concat((math_ops.range(m, vec_rank), math_ops.range(0, m)),
                            0)
    return array_ops.transpose(vec, perm=perm)

  if 0 < m:
    x_flipped = _flip_front_dims_to_back()
  else:
    x_flipped = array_ops.expand_dims(vec, -1)

  return array_ops.reshape(x_flipped, new_shape) 

def _flip_vector_to_matrix_static(vec, batch_shape):
  """flip_vector_to_matrix with static shapes."""
  # Shapes associated with batch_shape
  batch_rank = batch_shape.ndims

  # Shapes associated with vec.
  vec = ops.convert_to_tensor(vec, name="vec")
  vec_shape = vec.get_shape()
  vec_rank = len(vec_shape)
  vec_batch_rank = vec_rank - 1

  m = vec_batch_rank - batch_rank
  # vec_shape_left = [M1,...,Mm] or [].
  vec_shape_left = vec_shape[:m]
  # If vec_shape_left = [], then condensed_shape = [1] since reduce_prod([]) = 1
  # If vec_shape_left = [M1,...,Mm], condensed_shape = [M1*...*Mm]
  condensed_shape = [np.prod(vec_shape_left)]
  k = vec_shape[-1]
  new_shape = batch_shape.concatenate(k).concatenate(condensed_shape)

  def _flip_front_dims_to_back():
    # Permutation corresponding to [N1,...,Nn] + [k, M1,...,Mm]
    perm = array_ops.concat((math_ops.range(m, vec_rank), math_ops.range(0, m)),
                            0)
    return array_ops.transpose(vec, perm=perm)

  if 0 < m:
    x_flipped = _flip_front_dims_to_back()
  else:
    x_flipped = array_ops.expand_dims(vec, -1)

  return array_ops.reshape(x_flipped, new_shape) 

def _flip_matrix_to_vector_static(mat, static_batch_shape):
  """Flip matrix to vector with static shapes."""
  mat_rank = mat.get_shape().ndims
  k = mat.get_shape()[-2]
  final_shape = static_batch_shape.concatenate(k)

  # mat.shape = matrix_batch_shape + [k, M]
  # Permutation corresponding to [M] + matrix_batch_shape + [k]
  perm = [mat_rank - 1] + list(range(0, mat_rank - 1))
  mat_with_end_at_beginning = array_ops.transpose(mat, perm=perm)
  vector = array_ops.reshape(mat_with_end_at_beginning, final_shape)
  return vector 

def to_sparse_tensor(self, input_tensor):
    """Creates a SparseTensor from the bucketized Tensor."""
    dimension = self.source_column.dimension
    batch_size = array_ops.shape(input_tensor, name="shape")[0]

    if dimension > 1:
      i1 = array_ops.reshape(
          array_ops.tile(
              array_ops.expand_dims(
                  math_ops.range(0, batch_size), 1, name="expand_dims"),
              [1, dimension],
              name="tile"), [-1],
          name="reshape")
      i2 = array_ops.tile(
          math_ops.range(0, dimension), [batch_size], name="tile")
      # Flatten the bucket indices and unique them across dimensions
      # E.g. 2nd dimension indices will range from k to 2*k-1 with k buckets
      bucket_indices = array_ops.reshape(
          input_tensor, [-1], name="reshape") + self.length * i2
    else:
      # Simpler indices when dimension=1
      i1 = math_ops.range(0, batch_size)
      i2 = array_ops.zeros([batch_size], dtype=dtypes.int32, name="zeros")
      bucket_indices = array_ops.reshape(input_tensor, [-1], name="reshape")

    indices = math_ops.to_int64(array_ops.transpose(array_ops.stack((i1, i2))))
    shape = math_ops.to_int64(array_ops.stack([batch_size, dimension]))
    sparse_id_values = sparse_tensor_py.SparseTensor(
        indices, bucket_indices, shape)

    return sparse_id_values 

def _flip_vector_to_matrix_dynamic(vec, batch_shape):
  """flip_vector_to_matrix with dynamic shapes."""
  # Shapes associated with batch_shape
  batch_rank = array_ops.size(batch_shape)

  # Shapes associated with vec.
  vec = ops.convert_to_tensor(vec, name="vec")
  vec_shape = array_ops.shape(vec)
  vec_rank = array_ops.rank(vec)
  vec_batch_rank = vec_rank - 1

  m = vec_batch_rank - batch_rank
  # vec_shape_left = [M1,...,Mm] or [].
  vec_shape_left = array_ops.strided_slice(vec_shape, [0], [m])
  # If vec_shape_left = [], then condensed_shape = [1] since reduce_prod([]) = 1
  # If vec_shape_left = [M1,...,Mm], condensed_shape = [M1*...*Mm]
  condensed_shape = [math_ops.reduce_prod(vec_shape_left)]
  k = array_ops.gather(vec_shape, vec_rank - 1)
  new_shape = array_ops.concat((batch_shape, [k], condensed_shape), 0)

  def _flip_front_dims_to_back():
    # Permutation corresponding to [N1,...,Nn] + [k, M1,...,Mm]
    perm = array_ops.concat((math_ops.range(m, vec_rank), math_ops.range(0, m)),
                            0)
    return array_ops.transpose(vec, perm=perm)

  x_flipped = control_flow_ops.cond(
      math_ops.less(0, m),
      _flip_front_dims_to_back,
      lambda: array_ops.expand_dims(vec, -1))

  return array_ops.reshape(x_flipped, new_shape) 

def _DynamicPartitionGrads(op, *grads):
  """Gradients for DynamicPartition."""
  data = op.inputs[0]
  indices = op.inputs[1]
  num_partitions = op.get_attr("num_partitions")

  prefix_shape = array_ops.shape(indices)
  original_indices = array_ops.reshape(
      math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape)
  partitioned_indices = data_flow_ops.dynamic_partition(
      original_indices, indices, num_partitions)
  reconstructed = data_flow_ops.dynamic_stitch(partitioned_indices, grads)
  reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data))
  return [reconstructed, None] 

def _flip_vector_to_matrix_dynamic(vec, batch_shape):
  """flip_vector_to_matrix with dynamic shapes."""
  # Shapes associated with batch_shape
  batch_rank = array_ops.size(batch_shape)

  # Shapes associated with vec.
  vec = ops.convert_to_tensor(vec, name="vec")
  vec_shape = array_ops.shape(vec)
  vec_rank = array_ops.rank(vec)
  vec_batch_rank = vec_rank - 1

  m = vec_batch_rank - batch_rank
  # vec_shape_left = [M1,...,Mm] or [].
  vec_shape_left = array_ops.strided_slice(vec_shape, [0], [m])
  # If vec_shape_left = [], then condensed_shape = [1] since reduce_prod([]) = 1
  # If vec_shape_left = [M1,...,Mm], condensed_shape = [M1*...*Mm]
  condensed_shape = [math_ops.reduce_prod(vec_shape_left)]
  k = array_ops.gather(vec_shape, vec_rank - 1)
  new_shape = array_ops.concat((batch_shape, [k], condensed_shape), 0)

  def _flip_front_dims_to_back():
    # Permutation corresponding to [N1,...,Nn] + [k, M1,...,Mm]
    perm = array_ops.concat((math_ops.range(m, vec_rank), math_ops.range(0, m)),
                            0)
    return array_ops.transpose(vec, perm=perm)

  x_flipped = control_flow_ops.cond(
      math_ops.less(0, m),
      _flip_front_dims_to_back,
      lambda: array_ops.expand_dims(vec, -1))

  return array_ops.reshape(x_flipped, new_shape) 

def clip_by_average_norm(t, clip_norm, name=None):
  """Clips tensor values to a maximum average L2-norm.

  Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
  normalizes `t` so that its average L2-norm is less than or equal to
  `clip_norm`. Specifically, if the average L2-norm is already less than or
  equal to `clip_norm`, then `t` is not modified. If the average L2-norm is
  greater than `clip_norm`, then this operation returns a tensor of the same
  type and shape as `t` with its values set to:

  `t * clip_norm / l2norm_avg(t)`

  In this case, the average L2-norm of the output tensor is `clip_norm`.

  This operation is typically used to clip gradients before applying them with
  an optimizer.

  Args:
    t: A `Tensor`.
    clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
    name: A name for the operation (optional).

  Returns:
    A clipped `Tensor`.
  """
  with ops.name_scope(name, "clip_by_average_norm", [t, clip_norm]) as name:
    t = ops.convert_to_tensor(t, name="t")

    # Calculate L2-norm per element, clip elements by ratio of clip_norm to
    # L2-norm per element
    n_element = math_ops.cast(array_ops.size(t), dtypes.float32)
    l2norm_inv = math_ops.rsqrt(
        math_ops.reduce_sum(t * t, math_ops.range(array_ops.rank(t))))
    tclip = array_ops.identity(
        t * clip_norm * math_ops.minimum(
            l2norm_inv * n_element, constant_op.constant(1.0) / clip_norm),
        name=name)

  return tclip 

def _SparseReorderGrad(op, unused_output_indices_grad, output_values_grad):
  """Gradients for the SparseReorder op.

  Args:
    op: the SparseReorder op
    unused_output_indices_grad: the incoming gradients of the output indices
    output_values_grad: the incoming gradients of the output values

  Returns:
    Gradient for each of the 3 input tensors:
      (input_indices, input_values, input_shape)
    The gradients for input_indices and input_shape is None.
  """
  input_indices = op.inputs[0]
  input_shape = op.inputs[2]

  num_entries = array_ops.shape(input_indices)[0]
  entry_indices = math_ops.range(num_entries)
  sp_unordered = sparse_tensor.SparseTensor(
      input_indices, entry_indices, input_shape)
  sp_ordered = sparse_ops.sparse_reorder(sp_unordered)
  inverted_permutation = array_ops.invert_permutation(sp_ordered.values)

  return (None,
          array_ops.gather(output_values_grad, inverted_permutation),
          None) 

def __new__(cls, source_column, boundaries):
    if not isinstance(source_column, _RealValuedColumn):
      raise TypeError("source_column must be an instance of _RealValuedColumn. "
                      "source_column: {}".format(source_column))

    if source_column.dimension is None:
      raise ValueError("source_column must have a defined dimension. "
                       "source_column: {}".format(source_column))

    if (not isinstance(boundaries, list) and
        not isinstance(boundaries, tuple)) or not boundaries:
      raise ValueError("boundaries must be a non-empty list or tuple. "
                       "boundaries: {}".format(boundaries))

    # We allow bucket boundaries to be monotonically increasing
    # (ie a[i+1] >= a[i]). When two bucket boundaries are the same, we
    # de-duplicate.
    sanitized_boundaries = []
    for i in range(len(boundaries) - 1):
      if boundaries[i] == boundaries[i + 1]:
        continue
      elif boundaries[i] < boundaries[i + 1]:
        sanitized_boundaries.append(boundaries[i])
      else:
        raise ValueError("boundaries must be a sorted list. "
                         "boundaries: {}".format(boundaries))
    sanitized_boundaries.append(boundaries[len(boundaries) - 1])

    return super(_BucketizedColumn, cls).__new__(cls, source_column,
                                                 tuple(sanitized_boundaries)) 

def _flip_matrix_to_vector_dynamic(mat, batch_shape):
  """Flip matrix to vector with dynamic shapes."""
  mat_rank = array_ops.rank(mat)
  k = array_ops.gather(array_ops.shape(mat), mat_rank - 2)
  final_shape = array_ops.concat((batch_shape, [k]), 0)

  # mat.shape = matrix_batch_shape + [k, M]
  # Permutation corresponding to [M] + matrix_batch_shape + [k]
  perm = array_ops.concat(([mat_rank - 1], math_ops.range(0, mat_rank - 1)), 0)
  mat_with_end_at_beginning = array_ops.transpose(mat, perm=perm)
  vector = array_ops.reshape(mat_with_end_at_beginning, final_shape)
  return vector