parameters: (self, edge_list, feature_types)
return_statement: return [
              t.as_array() for t in self._graph_data.get_edge_feature(
                  Tensor(edge_list),
                  feature_types)]
docstring: Get `feature_types` feature of the edges in `edge_list`.
  
          Args:
              edge_list (Union[list, numpy.ndarray]): The given list of edges.
              feature_types (Union[list, numpy.ndarray]): The given list of feature types.
  
          Returns:
              numpy.ndarray, array of features.
  
          Examples:
              >>> edges = graph_dataset.get_all_edges(edge_type=0)
              >>> features = graph_dataset.get_edge_feature(edge_list=edges, feature_types=[1])
  
          Raises:
              TypeError: If `edge_list` is not list or ndarray.
              TypeError: If `feature_types` is not list or ndarray.
docstring_summary: Get `feature_types` feature of the edges in `edge_list`.
docstring_tokens: Get feature_types feature of the edges in edge_list .
function: def get_edge_feature(self, edge_list, feature_types):
          """
          Get `feature_types` feature of the edges in `edge_list`.
  
          Args:
              edge_list (Union[list, numpy.ndarray]): The given list of edges.
              feature_types (Union[list, numpy.ndarray]): The given list of feature types.
  
          Returns:
              numpy.ndarray, array of features.
  
          Examples:
              >>> edges = graph_dataset.get_all_edges(edge_type=0)
              >>> features = graph_dataset.get_edge_feature(edge_list=edges, feature_types=[1])
  
          Raises:
              TypeError: If `edge_list` is not list or ndarray.
              TypeError: If `feature_types` is not list or ndarray.
          """
          if self._working_mode == 'server':
              raise Exception("This method is not supported when working mode is server.")
          if isinstance(edge_list, list):
              edge_list = np.array(edge_list, dtype=np.int32)
          return [
              t.as_array() for t in self._graph_data.get_edge_feature(
                  Tensor(edge_list),
                  feature_types)]
function_tokens: def get_edge_feature ( self , edge_list , feature_types ) : if self . _working_mode == 'server' : raise Exception ( "This method is not supported when working mode is server." ) if isinstance ( edge_list , list ) : edge_list = np . array ( edge_list , dtype = np . int32 ) return [ t . as_array ( ) for t in self . _graph_data . get_edge_feature ( Tensor ( edge_list ) , feature_types ) ]