tensorflow_probability/python/internal/distribution_util.py
def with_dependencies(dependencies, output_tensor, name=None):
  """Produces the content of `output_tensor` only after `dependencies`.

  In some cases, a user may want the output of an operation to be consumed
  externally only after some other dependencies have run first. This function
  returns `output_tensor`, but only after all operations in `dependencies` have
  run. Note that this means that there is no guarantee that `output_tensor` will
  be evaluated after any `dependencies` have run.

  See also `tf.tuple` and `tf.group`.

  Args:
    dependencies: Iterable of operations to run before this op finishes.
    output_tensor: A `Tensor` or `IndexedSlices` that will be returned.
    name: (Optional) A name for this operation.

  Returns:
    output_with_deps: Same as `output_tensor` but with embedded dependencies.

  Raises:
    TypeError: if `output_tensor` is not a `Tensor` or `IndexedSlices`.
  """
  if tf.executing_eagerly():
    return output_tensor
  with tf.name_scope(name or "control_dependency") as name:
    with tf.control_dependencies(d for d in dependencies if d is not None):
      output_tensor = tf.convert_to_tensor(value=output_tensor)
      if isinstance(output_tensor, tf.Tensor):
        return tf.identity(output_tensor, name=name)
      else:
        return tf.IndexedSlices(
            tf.identity(output_tensor.values, name=name),
            output_tensor.indices,
            output_tensor.dense_shape)
def with_dependencies(dependencies, output_tensor, name=None):
  if tf.executing_eagerly():
    return output_tensor
  with tf.name_scope(name or "control_dependency") as name:
    with tf.control_dependencies(d for d in dependencies if d is not None):
      output_tensor = tf.convert_to_tensor(value=output_tensor)
      if isinstance(output_tensor, tf.Tensor):
        return tf.identity(output_tensor, name=name)
      else:
        return tf.IndexedSlices(
            tf.identity(output_tensor.values, name=name),
            output_tensor.indices,
            output_tensor.dense_shape)
def with_dependencies ( dependencies , output_tensor , name = None ) : if tf . executing_eagerly ( ) : return output_tensor with tf . name_scope ( name or "control_dependency" ) as name : with tf . control_dependencies ( d for d in dependencies if d is not None ) : output_tensor = tf . convert_to_tensor ( value = output_tensor ) if isinstance ( output_tensor , tf . Tensor ) : return tf . identity ( output_tensor , name = name ) else : return tf . IndexedSlices ( tf . identity ( output_tensor . values , name = name ) , output_tensor . indices , output_tensor . dense_shape )
Produces the content of `output_tensor` only after `dependencies`.

  In some cases, a user may want the output of an operation to be consumed
  externally only after some other dependencies have run first. This function
  returns `output_tensor`, but only after all operations in `dependencies` have
  run. Note that this means that there is no guarantee that `output_tensor` will
  be evaluated after any `dependencies` have run.

  See also `tf.tuple` and `tf.group`.

  Args:
    dependencies: Iterable of operations to run before this op finishes.
    output_tensor: A `Tensor` or `IndexedSlices` that will be returned.
    name: (Optional) A name for this operation.

  Returns:
    output_with_deps: Same as `output_tensor` but with embedded dependencies.

  Raises:
    TypeError: if `output_tensor` is not a `Tensor` or `IndexedSlices`.
Produces the content of output_tensor only after dependencies .