parameters: (data: ragged_tensor.RaggedOrDense,
                     segment_ids: ragged_tensor.RaggedOrDense,
                     num_segments,
                     name=None)
docstring: For docs, see: _RAGGED_SEGMENT_DOCSTRING.
docstring_summary: For docs, see: _RAGGED_SEGMENT_DOCSTRING.
docstring_tokens: For docs see : _RAGGED_SEGMENT_DOCSTRING .
function: def segment_sqrt_n(data: ragged_tensor.RaggedOrDense,
                     segment_ids: ragged_tensor.RaggedOrDense,
                     num_segments,
                     name=None):
    """For docs, see: _RAGGED_SEGMENT_DOCSTRING."""
    with ops.name_scope(name, 'RaggedSegmentSqrtN',
                        [data, segment_ids, num_segments]):
      total = segment_sum(data, segment_ids, num_segments)
      ones = ragged_tensor.RaggedTensor.from_nested_row_splits(
          array_ops.ones_like(data.flat_values),
          data.nested_row_splits,
          validate=False)
      count = segment_sum(ones, segment_ids, num_segments)
      if ragged_tensor.is_ragged(total):
        return total.with_flat_values(total.flat_values /
                                      math_ops.sqrt(count.flat_values))
      else:
        return total / math_ops.sqrt(count)
function_tokens: def segment_sqrt_n ( data : ragged_tensor . RaggedOrDense , segment_ids : ragged_tensor . RaggedOrDense , num_segments , name = None ) : with ops . name_scope ( name , 'RaggedSegmentSqrtN' , [ data , segment_ids , num_segments ] ) : total = segment_sum ( data , segment_ids , num_segments ) ones = ragged_tensor . RaggedTensor . from_nested_row_splits ( array_ops . ones_like ( data . flat_values ) , data . nested_row_splits , validate = False ) count = segment_sum ( ones , segment_ids , num_segments ) if ragged_tensor . is_ragged ( total ) : return total . with_flat_values ( total . flat_values / math_ops . sqrt ( count . flat_values ) ) else : return total / math_ops . sqrt ( count )