parameters: (input: ragged_tensor.Ragged, multiples, name=None)
docstring: Constructs a `RaggedTensor` by tiling a given `RaggedTensor`.
  
    The values of `input` are replicated `multiples[i]` times along the
    `i`th dimension (for each dimension `i`).  For every dimension `axis` in
    `input`, the length of each output element in that dimension is the
    length of corresponding input element multiplied by `multiples[axis]`.
  
    Args:
      input: A `RaggedTensor`.
      multiples: A 1-D integer `Tensor`.  Length must be the same as the number of
        dimensions in `input`.
      name: A name for the operation (optional).
  
    Returns:
      A `RaggedTensor` with the same type, rank, and ragged_rank as `input`.
  
    #### Example:
  
    >>> rt = tf.ragged.constant([[1, 2], [3]])
    >>> tf.tile(rt, [3, 2]).to_list()
    [[1, 2, 1, 2], [3, 3], [1, 2, 1, 2], [3, 3], [1, 2, 1, 2], [3, 3]]
docstring_summary: Constructs a `RaggedTensor` by tiling a given `RaggedTensor`.
docstring_tokens: Constructs a RaggedTensor by tiling a given RaggedTensor .
function: def tile(input: ragged_tensor.Ragged, multiples, name=None):  # pylint: disable=redefined-builtin
    """Constructs a `RaggedTensor` by tiling a given `RaggedTensor`.
  
    The values of `input` are replicated `multiples[i]` times along the
    `i`th dimension (for each dimension `i`).  For every dimension `axis` in
    `input`, the length of each output element in that dimension is the
    length of corresponding input element multiplied by `multiples[axis]`.
  
    Args:
      input: A `RaggedTensor`.
      multiples: A 1-D integer `Tensor`.  Length must be the same as the number of
        dimensions in `input`.
      name: A name for the operation (optional).
  
    Returns:
      A `RaggedTensor` with the same type, rank, and ragged_rank as `input`.
  
    #### Example:
  
    >>> rt = tf.ragged.constant([[1, 2], [3]])
    >>> tf.tile(rt, [3, 2]).to_list()
    [[1, 2, 1, 2], [3, 3], [1, 2, 1, 2], [3, 3], [1, 2, 1, 2], [3, 3]]
    """
    with ops.name_scope(name, 'RaggedTile', [input, multiples]):
      input = ragged_tensor.convert_to_tensor_or_ragged_tensor(
          input, name='input')
      if not ragged_tensor.is_ragged(input):
        return array_ops.tile(input, multiples, name)
      multiples = ragged_util.convert_to_int_tensor(
          multiples, name='multiples', dtype=input.row_splits.dtype)
      multiples.shape.assert_has_rank(1)
  
      # If the constant value of `multiples` is available, then we can use it
      # to skip tiling dimensions where `multiples=1`.
      const_multiples = tensor_util.constant_value(multiples)
  
      return ragged_tensor.RaggedTensor.from_nested_row_splits(
          _tile_ragged_values(input, multiples, const_multiples),
          _tile_ragged_splits(input, multiples, const_multiples),
          validate=False)
function_tokens: def tile ( input : ragged_tensor . Ragged , multiples , name = None ) : # pylint: disable=redefined-builtin with ops . name_scope ( name , 'RaggedTile' , [ input , multiples ] ) : input = ragged_tensor . convert_to_tensor_or_ragged_tensor ( input , name = 'input' ) if not ragged_tensor . is_ragged ( input ) : return array_ops . tile ( input , multiples , name ) multiples = ragged_util . convert_to_int_tensor ( multiples , name = 'multiples' , dtype = input . row_splits . dtype ) multiples . shape . assert_has_rank ( 1 ) # If the constant value of `multiples` is available, then we can use it # to skip tiling dimensions where `multiples=1`. const_multiples = tensor_util . constant_value ( multiples ) return ragged_tensor . RaggedTensor . from_nested_row_splits ( _tile_ragged_values ( input , multiples , const_multiples ) , _tile_ragged_splits ( input , multiples , const_multiples ) , validate = False )