parameters: (self, transformation_cache, state_manager)
return_statement: return self._get_dense_tensor_internal(transformation_cache, state_manager)
docstring: Returns the embedding lookup result.
docstring_summary: Returns the embedding lookup result.
docstring_tokens: Returns the embedding lookup result .
function: def get_dense_tensor(self, transformation_cache, state_manager):
      """Returns the embedding lookup result."""
      if isinstance(self.categorical_column, SequenceCategoricalColumn):
        raise ValueError(
            'In embedding_column: {}. '
            'categorical_column must not be of type SequenceCategoricalColumn. '
            'Suggested fix A: If you wish to use DenseFeatures, use a '
            'non-sequence categorical_column_with_*. '
            'Suggested fix B: If you wish to create sequence input, use '
            'SequenceFeatures instead of DenseFeatures. '
            'Given (type {}): {}'.format(self.name, type(self.categorical_column),
                                         self.categorical_column))
      return self._get_dense_tensor_internal(transformation_cache, state_manager)
function_tokens: def get_dense_tensor ( self , transformation_cache , state_manager ) : if isinstance ( self . categorical_column , SequenceCategoricalColumn ) : raise ValueError ( 'In embedding_column: {}. ' 'categorical_column must not be of type SequenceCategoricalColumn. ' 'Suggested fix A: If you wish to use DenseFeatures, use a ' 'non-sequence categorical_column_with_*. ' 'Suggested fix B: If you wish to create sequence input, use ' 'SequenceFeatures instead of DenseFeatures. ' 'Given (type {}): {}' . format ( self . name , type ( self . categorical_column ) , self . categorical_column ) ) return self . _get_dense_tensor_internal ( transformation_cache , state_manager )