parameters: (self, transformation_cache, state_manager)
return_statement: return SequenceDenseColumn.TensorSequenceLengthPair(
          dense_tensor=dense_tensor, sequence_length=sequence_length)
docstring: See `SequenceDenseColumn` base class.
docstring_summary: See `SequenceDenseColumn` base class.
docstring_tokens: See SequenceDenseColumn base class .
function: def get_sequence_dense_tensor(self, transformation_cache, state_manager):
      """See `SequenceDenseColumn` base class."""
      if not isinstance(self.categorical_column, SequenceCategoricalColumn):
        raise ValueError(
            'In embedding_column: {}. '
            'categorical_column must be of type SequenceCategoricalColumn '
            'to use SequenceFeatures. '
            'Suggested fix: Use one of sequence_categorical_column_with_*. '
            'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                         self.categorical_column))
      sparse_tensors = self.categorical_column.get_sparse_tensors(
          transformation_cache, state_manager)
      dense_tensor = self._get_dense_tensor_internal(sparse_tensors,
                                                     state_manager)
      sequence_length = fc_utils.sequence_length_from_sparse_tensor(
          sparse_tensors.id_tensor)
      return SequenceDenseColumn.TensorSequenceLengthPair(
          dense_tensor=dense_tensor, sequence_length=sequence_length)
function_tokens: def get_sequence_dense_tensor ( self , transformation_cache , state_manager ) : if not isinstance ( self . categorical_column , SequenceCategoricalColumn ) : raise ValueError ( 'In embedding_column: {}. ' 'categorical_column must be of type SequenceCategoricalColumn ' 'to use SequenceFeatures. ' 'Suggested fix: Use one of sequence_categorical_column_with_*. ' 'Given (type {}): {}' . format ( self . name , type ( self . categorical_column ) , self . categorical_column ) ) sparse_tensors = self . categorical_column . get_sparse_tensors ( transformation_cache , state_manager ) dense_tensor = self . _get_dense_tensor_internal ( sparse_tensors , state_manager ) sequence_length = fc_utils . sequence_length_from_sparse_tensor ( sparse_tensors . id_tensor ) return SequenceDenseColumn . TensorSequenceLengthPair ( dense_tensor = dense_tensor , sequence_length = sequence_length )