parameters: (input: ragged_tensor.Ragged, out_type=dtypes.int32, name=None)
docstring: Returns the size of a potentially ragged tensor.
  
    The size of a ragged tensor is the size of its inner values.
  
    #### Example:
  
    >>> tf.size(tf.ragged.constant([[1, 2], [3]])).numpy()
    3
  
    Args:
      input: A potentially ragged `Tensor`.
      out_type: The numeric output type for the operation.
      name: A name for the operation (optional).
  
    Returns:
      A Tensor of type `out_type`.
docstring_summary: Returns the size of a potentially ragged tensor.
docstring_tokens: Returns the size of a potentially ragged tensor .
function: def size(input: ragged_tensor.Ragged, out_type=dtypes.int32, name=None):  # pylint: disable=redefined-builtin
    """Returns the size of a potentially ragged tensor.
  
    The size of a ragged tensor is the size of its inner values.
  
    #### Example:
  
    >>> tf.size(tf.ragged.constant([[1, 2], [3]])).numpy()
    3
  
    Args:
      input: A potentially ragged `Tensor`.
      out_type: The numeric output type for the operation.
      name: A name for the operation (optional).
  
    Returns:
      A Tensor of type `out_type`.
    """
    if ragged_tensor.is_ragged(input):
      return array_ops.size(input.flat_values, out_type=out_type, name=name)
    else:
      return array_ops.size(input, out_type=out_type, name=name)
function_tokens: def size ( input : ragged_tensor . Ragged , out_type = dtypes . int32 , name = None ) : # pylint: disable=redefined-builtin if ragged_tensor . is_ragged ( input ) : return array_ops . size ( input . flat_values , out_type = out_type , name = name ) else : return array_ops . size ( input , out_type = out_type , name = name )