parameters: (
          self, q_weight_list: List[Tuple[Tensor, Optional[Tensor]]]
      )
docstring: Assigns self.split_embedding_weights() with values from the input list of weights and scale_shifts.
docstring_summary: Assigns self.split_embedding_weights() with values from the input list of weights and scale_shifts.
docstring_tokens: Assigns self . split_embedding_weights () with values from the input list of weights and scale_shifts .
function: def assign_embedding_weights(
          self, q_weight_list: List[Tuple[Tensor, Optional[Tensor]]]
      ) -> None:
          """
          Assigns self.split_embedding_weights() with values from the input list of weights and scale_shifts.
          """
          weights = self.split_embedding_weights()
          assert len(q_weight_list) == len(weights)
  
          for (dest_weight, input_weight) in zip(weights, q_weight_list):
              dest_weight[0].copy_(input_weight[0])
              if input_weight[1] is not None:
                  assert dest_weight[1] is not None
                  dest_weight[1].copy_(input_weight[1])
              else:
                  assert dest_weight[1] is None
function_tokens: def assign_embedding_weights ( self , q_weight_list : List [ Tuple [ Tensor , Optional [ Tensor ] ] ] ) -> None : weights = self . split_embedding_weights ( ) assert len ( q_weight_list ) == len ( weights ) for ( dest_weight , input_weight ) in zip ( weights , q_weight_list ) : dest_weight [ 0 ] . copy_ ( input_weight [ 0 ] ) if input_weight [ 1 ] is not None : assert dest_weight [ 1 ] is not None dest_weight [ 1 ] . copy_ ( input_weight [ 1 ] ) else : assert dest_weight [ 1 ] is None