Python tensorflow.python.framework.sparse_tensor.SparseTensor() Examples

def _neg(x, name=None):
  """Computes numerical negative value element-wise.

  I.e., \\(y = -x\\).

  Args:
    x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
  """
  return negative(x, name)


# pylint: enable=g-docstring-has-escape 

def _NextIteration(data, name=None):
  data = ops.internal_convert_to_tensor_or_indexed_slices(data, as_ref=True)
  if isinstance(data, ops.Tensor):
    if data.dtype._is_ref_dtype:   # pylint: disable=protected-access
      return ref_next_iteration(data, name=name)
    else:
      return next_iteration(data, name=name)
  else:
    if not isinstance(data, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
      raise TypeError("Type %s not supported" % type(data))
    values = _NextIteration(data.values, name=name)
    indices = next_iteration(data.indices, name="indices")
    if isinstance(data, ops.IndexedSlices):
      dense_shape = data.dense_shape
      if dense_shape is not None:
        dense_shape = next_iteration(dense_shape, name="dense_shape")
      return ops.IndexedSlices(values, indices, dense_shape)
    else:
      dense_shape = next_iteration(data.dense_shape, name="dense_shape")
      return sparse_tensor.SparseTensor(indices, values, dense_shape) 

def _InitializeValues(self, values):
    """Makes the values known to this context."""
    self._values = set()
    for x in values:
      if isinstance(x, ops.Tensor):
        self._values.add(x.name)
      else:
        self._values.add(x.values.name)
        self._values.add(x.indices.name)
        if isinstance(x, ops.IndexedSlices):
          dense_shape = x.dense_shape
        elif isinstance(x, sparse_tensor.SparseTensor):
          dense_shape = x.dense_shape
        else:
          raise TypeError("Type %s not supported" % type(x))
        if dense_shape is not None:
          self._values.add(dense_shape.name) 

def _maybe_select_class_id(labels, predictions_idx, selected_id=None):
  """If class ID is specified, filter all other classes.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: `int64` `Tensor` of class IDs, with shape [D1, ... DN, k]
      where N >= 1. Commonly, N=1 and `predictions_idx` has shape
      [batch size, k].
    selected_id: Int id to select.

  Returns:
    Tuple of `labels` and `predictions_idx`, possibly with classes removed.
  """
  if selected_id is None:
    return labels, predictions_idx
  return (_select_class_id(labels, selected_id),
          _select_class_id(predictions_idx, selected_id)) 

def set_size(a, validate_indices=True):
  """Compute number of unique elements along last dimension of `a`.

  Args:
    a: `SparseTensor`, with indices sorted in row-major order.
    validate_indices: Whether to validate the order and range of sparse indices
       in `a`.

  Returns:
    `int32` `Tensor` of set sizes. For `a` ranked `n`, this is a `Tensor` with
    rank `n-1`, and the same 1st `n-1` dimensions as `a`. Each value is the
    number of unique elements in the corresponding `[0...n-1]` dimension of `a`.

  Raises:
    TypeError: If `a` is an invalid types.
  """
  a = sparse_tensor.convert_to_tensor_or_sparse_tensor(a, name="a")
  if not isinstance(a, sparse_tensor.SparseTensor):
    raise TypeError("Expected `SparseTensor`, got %s." % a)
  if a.values.dtype.base_dtype not in _VALID_DTYPES:
    raise TypeError("Invalid dtype %s." % a.values.dtype)
  # pylint: disable=protected-access
  return gen_set_ops.set_size(
      a.indices, a.values, a.dense_shape, validate_indices) 

def _convert_to_tensors_or_sparse_tensors(a, b):
  """Convert to tensor types, and flip order if necessary.

  Args:
    a: `Tensor` or `SparseTensor` of the same type as `b`.
    b: `Tensor` or `SparseTensor` of the same type as `a`.

  Returns:
    Tuple of `(a, b, flipped)`, where `a` and `b` have been converted to
    `Tensor` or `SparseTensor`, and `flipped` indicates whether the order has
    been flipped to make it dense,sparse instead of sparse,dense (since the set
    ops do not support the latter).
  """
  a = sparse_tensor.convert_to_tensor_or_sparse_tensor(a, name="a")
  if a.dtype.base_dtype not in _VALID_DTYPES:
    raise TypeError("'a' invalid dtype %s." % a.dtype)
  b = sparse_tensor.convert_to_tensor_or_sparse_tensor(b, name="b")
  if b.dtype.base_dtype != a.dtype.base_dtype:
    raise TypeError("Types don't match, %s vs %s." % (a.dtype, b.dtype))
  if (isinstance(a, sparse_tensor.SparseTensor) and
      not isinstance(b, sparse_tensor.SparseTensor)):
    return b, a, True
  return a, b, False 

def _BuildCondTensor(self, v):
    if isinstance(v, ops.Operation):
      # Use pivot as the proxy for this op.
      return with_dependencies([v], self._pivot)
    elif isinstance(v, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
      values = self._ProcessOutputTensor(v.values)
      indices = self._ProcessOutputTensor(v.indices)
      if isinstance(v, ops.IndexedSlices):
        dense_shape = v.dense_shape
        if dense_shape is not None:
          dense_shape = self._ProcessOutputTensor(dense_shape)
        return ops.IndexedSlices(values, indices, dense_shape)
      else:
        dense_shape = self._ProcessOutputTensor(v.dense_shape)
        return sparse_tensor.SparseTensor(indices, values, dense_shape)
    else:
      v = nest.map_structure(_convert_tensorarray_to_flow, v)
      return self._ProcessOutputTensor(ops.convert_to_tensor(v)) 

def shape(input, name=None, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.

  This operation returns a 1-D integer tensor representing the shape of `input`.

  For example:

  ```python
  # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
  shape(t) ==> [2, 2, 3]
  ```

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to `tf.int32`.

  Returns:
    A `Tensor` of type `out_type`.
  """
  return shape_internal(input, name, optimize=True, out_type=out_type) 

def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the shape as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.

  """
  with ops.name_scope(name, "Shape", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops.cast(input.dense_shape, out_type)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.as_list(), out_type, name=name)
      return gen_array_ops.shape(input, name=name, out_type=out_type) 

def size(input, name=None, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the size of a tensor.

  This operation returns an integer representing the number of elements in
  `input`.

  For example:

  ```python
  # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
  size(t) ==> 12
  ```

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`. Defaults to tf.int32.
  """
  return size_internal(input, name, optimize=True, out_type=out_type) 

def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin,protected-access
  """Returns the size of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the size as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.
  """
  with ops.name_scope(name, "Size", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops._prod(
          gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.num_elements(), out_type, name=name)
      return gen_array_ops.size(input, name=name, out_type=out_type) 

def _convert_to_sparse_tensor(sp_input):
  """Convert `sp_input` to `SparseTensor` and return it.

  Args:
    sp_input: `SparseTensor` or `SparseTensorValue`.

  Returns:
    `sp_input` converted to `SparseTensor`.

  Raises:
    ValueError: if `sp_input` is neither `SparseTensor` nor `SparseTensorValue`.
  """
  if isinstance(sp_input, sparse_tensor.SparseTensorValue):
    return sparse_tensor.SparseTensor.from_value(sp_input)
  if not isinstance(sp_input, sparse_tensor.SparseTensor):
    raise TypeError("Input must be a SparseTensor.")
  return sp_input 

def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None):
  """Converts a dense tensor into a sparse tensor.

  An example use would be to convert dense labels to sparse ones
  so that they can be fed to the ctc_loss.

  Args:
     tensor: An `int` `Tensor` to be converted to a `Sparse`.
     eos_token: An integer. It is part of the target label that signifies the
       end of a sentence.
     outputs_collections: Collection to add the outputs.
     scope: Optional scope for name_scope.
  """
  with variable_scope.variable_scope(scope, 'dense_to_sparse', [tensor]) as sc:
    tensor = ops.convert_to_tensor(tensor)
    indices = array_ops.where(
        math_ops.not_equal(tensor, constant_op.constant(eos_token,
                                                        tensor.dtype)))
    values = array_ops.gather_nd(tensor, indices)
    shape = array_ops.shape(tensor, out_type=dtypes.int64)
    outputs = sparse_tensor.SparseTensor(indices, values, shape)
    return utils.collect_named_outputs(outputs_collections, sc.name, outputs) 

def _add_sparse_to_tensors_map(sp_input, container=None,
                               shared_name=None, name=None):
  """Add a `SparseTensor` to a `SparseTensorsMap` and return its handle.

  Args:
    sp_input: The input `SparseTensor`.
    container: The container for the underlying `SparseTensorsMap` (optional).
    shared_name: The shared name for the underlying `SparseTensorsMap`
      (optional, defaults to the name of the newly created op).
    name: A name prefix for the returned tensors (optional).

  Returns:
    A string 1-vector (1D `Tensor`), with the single element representing the
    a unique handle to a `SparseTensor` stored by the `SparseTensorMap`
    underlying this op.

  Raises:
    TypeError: If `sp_input` is not a `SparseTensor`.
  """
  sp_input = _convert_to_sparse_tensor(sp_input)

  return gen_sparse_ops._add_sparse_to_tensors_map(
      sp_input.indices, sp_input.values, sp_input.dense_shape,
      container=container, shared_name=shared_name, name=name) 

def square(x, name=None):
  r"""Computes square of x element-wise.

  I.e., \\(y = x * x = x^2\\).

  Args:
    x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`. Has the same type as `x`.
  """
  with ops.name_scope(name, "Square", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_square = gen_math_ops.square(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_square, dense_shape=x.dense_shape)
    else:
      return gen_math_ops.square(x, name=name) 

def erf(x, name=None):
  """Computes the Gauss error function of `x` element-wise.

  Args:
    x: A `Tensor` of `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
  """
  with ops.name_scope(name, "Erf", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_erf = gen_math_ops.erf(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_erf, dense_shape=x.dense_shape)
    else:
      return gen_math_ops.erf(x, name=name) 

def serialize_sparse(sp_input, name=None):
  """Serialize a `SparseTensor` into a string 3-vector (1-D `Tensor`) object.

  Args:
    sp_input: The input `SparseTensor`.
    name: A name prefix for the returned tensors (optional).

  Returns:
    A string 3-vector (1D `Tensor`), with each column representing the
    serialized `SparseTensor`'s indices, values, and shape (respectively).

  Raises:
    TypeError: If `sp_input` is not a `SparseTensor`.
  """
  sp_input = _convert_to_sparse_tensor(sp_input)

  return gen_sparse_ops._serialize_sparse(
      sp_input.indices, sp_input.values, sp_input.dense_shape, name=name) 

def sparse_dense_cwise_add(sp_t, dense_t):
  """Adds up a SparseTensor and a dense Tensor, using these special rules:

  (1) Broadcasts the dense side to have the same shape as the sparse side, if
      eligible;
  (2) Then, only the dense values pointed to by the indices of the SparseTensor
      participate in the cwise addition.

  By the rules, the result is a logical SparseTensor with exactly the same
  indices and shape, but possibly with different non-zero values.  The output of
  this Op is the resultant non-zero values.

  Args:
    sp_t: the SparseTensor operand.
    dense_t: the dense Tensor operand; must have the same dtype and a
      broadcast-compatible shape as `sp_t`.

  Returns:
    output: the SparseTensor output.
  """
  result = gen_sparse_ops.sparse_dense_cwise_add(sp_t.indices, sp_t.values,
                                                 sp_t.dense_shape, dense_t)
  return sparse_tensor.SparseTensor(sp_t.indices, result, sp_t.dense_shape) 

def _ReductionDims(x, axis, reduction_indices):
  """Returns range(0, rank(x)) 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), dtype=dtypes.int32)
    if (isinstance(x, sparse_tensor.SparseTensor) and
        x.dense_shape.get_shape().is_fully_defined()):
      rank = x.dense_shape.get_shape()[0].value  # sparse.dense_shape is 1-D.
      return constant_op.constant(np.arange(rank), dtype=dtypes.int32)

    # Otherwise, we rely on Range and Rank to do the right thing at run-time.
    return range(0, array_ops.rank(x)) 

def tanh(x, name=None):
  """Computes hyperbolic tangent of `x` element-wise.

  Args:
    x: A Tensor or SparseTensor with type `float`, `double`, `int32`,
      `complex64`, `int64`, or `qint32`.
    name: A name for the operation (optional).

  Returns:
    A Tensor or SparseTensor respectively with the same type as `x` if
    `x.dtype != qint32` otherwise the return type is `quint8`.
  """
  with ops.name_scope(name, "Tanh", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_tanh = gen_math_ops._tanh(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_tanh, dense_shape=x.dense_shape)
    else:
      return gen_math_ops._tanh(x, name=name) 

def _convert_to_sparse_tensors(sp_inputs):
  """Convert `sp_inputs` to `SparseTensor` objects and return them.

  Args:
    sp_inputs: `list` or `tuple` of `SparseTensor` or `SparseTensorValue`
      objects.

  Returns:
    `sp_inputs` converted to `SparseTensor` objects.

  Raises:
    ValueError: if any item in `sp_inputs` is neither `SparseTensor` nor
      `SparseTensorValue`.
  """
  if isinstance(sp_inputs, list):
    return [_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs]
  if isinstance(sp_inputs, tuple):
    return (_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs)
  raise TypeError("Inputs must be a list or tuple.")


# pylint: disable=protected-access 

def rank_internal(input, name=None, optimize=True):
  # pylint: disable=redefined-builtin
  """Returns the rank of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the rank as a constant when possible.

  Returns:
    A `Tensor` of type `int32`.
  """
  with ops.name_scope(name, "Rank", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_array_ops.size(input.dense_shape, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.ndims is not None:
        return constant(input_shape.ndims, dtypes.int32, name=name)
      return gen_array_ops.rank(input, name=name) 

def to_float(x, name="ToFloat"):
  """Casts a tensor to type `float32`.

  Args:
    x: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` with same shape as `x` with type `float32`.

  Raises:
    TypeError: If `x` cannot be cast to the `float32`.
  """
  return cast(x, dtypes.float32, name=name) 

def to_double(x, name="ToDouble"):
  """Casts a tensor to type `float64`.

  Args:
    x: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` with same shape as `x` with type `float64`.

  Raises:
    TypeError: If `x` cannot be cast to the `float64`.
  """
  return cast(x, dtypes.float64, name=name) 

def lookup(self, keys, name=None):
    """Looks up `keys` in a table, outputs the corresponding values.

    The `default_value` is used for keys not present in the table.

    Args:
      keys: Keys to look up. May be either a `SparseTensor` or dense `Tensor`.
      name: A name for the operation (optional).

    Returns:
      A `SparseTensor` if keys are sparse, otherwise a dense `Tensor`.

    Raises:
      TypeError: when `keys` or `default_value` doesn't match the table data
        types.
    """
    key_tensor = keys
    if isinstance(keys, sparse_tensor.SparseTensor):
      key_tensor = keys.values

    if keys.dtype != self._key_dtype:
      raise TypeError("Signature mismatch. Keys must be dtype %s, got %s." %
                      (self._key_dtype, keys.dtype))

    with ops.name_scope(name, "%s_Lookup" % self._name,
                        (self._table_ref, key_tensor,
                         self._default_value)) as scope:
      # pylint: disable=protected-access
      values = gen_lookup_ops._lookup_table_find_v2(
          self._table_ref, key_tensor, self._default_value, name=scope)
      # pylint: enable=protected-access

    values.set_shape(key_tensor.get_shape())
    if isinstance(keys, sparse_tensor.SparseTensor):
      return sparse_tensor.SparseTensor(keys.indices, values, keys.dense_shape)
    else:
      return values 

def cast(x, dtype, name=None):
  """Casts a tensor to a new type.

  The operation casts `x` (in case of `Tensor`) or `x.values`
  (in case of `SparseTensor`) to `dtype`.

  For example:

  ```python
  # tensor `a` is [1.8, 2.2], dtype=tf.float
  tf.cast(a, tf.int32) ==> [1, 2]  # dtype=tf.int32
  ```

  Args:
    x: A `Tensor` or `SparseTensor`.
    dtype: The destination type.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` with same shape as `x`.

  Raises:
    TypeError: If `x` cannot be cast to the `dtype`.
  """
  base_type = dtypes.as_dtype(dtype).base_dtype
  with ops.name_scope(name, "Cast", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      values_cast = cast(x.values, base_type, name=name)
      return sparse_tensor.SparseTensor(x.indices, values_cast, x.dense_shape)
    else:
      # TODO(touts): Handle what Josh said.
      #
      # Could return ops.convert_to_tensor(x, dtype=dtype, ...)  here, but that
      # allows some conversions that cast() can't do, e.g.  casting numbers to
      # strings.
      x = ops.convert_to_tensor(x, name="x")
      if x.dtype.base_dtype == base_type:
        return x
      return gen_math_ops.cast(x, base_type, name=name) 

def __init__(self, features):
    """Creates a `_LazyBuilder`.

    Args:
      features: A mapping from feature column to objects that are `Tensor` or
        `SparseTensor`, or can be converted to same via
        `sparse_tensor.convert_to_tensor_or_sparse_tensor`. A `string` key
        signifies a base feature (not-transformed). A `_FeatureColumn` key
        means that this `Tensor` is the output of an existing `_FeatureColumn`
        which can be reused.
    """
    self._features = features.copy()
    self._feature_tensors = {} 

def negative(x, name=None):
  """Computes numerical negative value element-wise.

  I.e., \\(y = -x\\).

  Args:
    x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
  """
  with ops.name_scope(name, "Neg", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_neg = gen_math_ops._neg(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_neg, dense_shape=x.dense_shape)
    else:
      return gen_math_ops._neg(x, name=name)


# pylint: enable=g-docstring-has-escape


# pylint: disable=g-docstring-has-escape 

def _inner_flatten(inputs, new_rank, output_collections=None, scope=None):
  """Flattens inner dimensions of `inputs`, returns a Tensor with `new_rank`.

  For example:
  '''
      x = tf.random.uniform(shape=[1, 2, 3, 4, 5, 6])
      y = _inner_flatten(x, 4)
      assert y.get_shape().as_list() == [1, 2, 3, (4 * 5 * 6)]
  '''
  This layer will fail at run time if `new_rank` is greater than the current
  rank of `inputs`.

  Args:
    inputs: A `Tensor` or `SparseTensor`.
    new_rank: The desired rank of the returned `Tensor` or `SparseTensor`.
    output_collections: Collection to which the outputs will be added.
    scope: Optional scope for `name_scope`.

  Returns:
    A `Tensor` or `SparseTensor` containing the same values as `inputs`, but
    with innermost dimensions flattened to obtain rank `new_rank`.

  Raises:
    TypeError: `inputs` is not a `Tensor` or `SparseTensor`.
  """
  with ops.name_scope(scope, 'InnerFlatten', [inputs, new_rank]) as sc:
    if isinstance(inputs, sparse_tensor.SparseTensor):
      flattened = _sparse_inner_flatten(inputs, new_rank)
    else:
      inputs = ops.convert_to_tensor(inputs)
      flattened = _dense_inner_flatten(inputs, new_rank)
  return utils.collect_named_outputs(output_collections, sc, flattened) 

def _mul_dispatch(x, y, name=None):
  """Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse"."""
  is_tensor_y = isinstance(y, ops.Tensor)
  if is_tensor_y:
    return gen_math_ops._mul(x, y, name=name)
  else:
    assert isinstance(y, sparse_tensor.SparseTensor)  # Case: Dense * Sparse.
    new_vals = gen_sparse_ops.sparse_dense_cwise_mul(y.indices, y.values,
                                                     y.dense_shape, x, name)
    return sparse_tensor.SparseTensor(y.indices, new_vals, y.dense_shape)


# NOTE(aselle): When integer division is added for sparse_dense_cwise,
# div, truediv, and floordiv should be delegated appropriately for
# Python sematnics, analogous to dense cwise tensor operations.