code: def get_transformer_encoder(config: transformer.TransformerConfig, prefix: str) -> 'Encoder':
      
      encoder_seq = EncoderSequence([], dtype=config.dtype)
      cls, encoder_params = _get_positional_embedding_params(config.positional_embedding_type,
                                                             config.model_size,
                                                             config.max_seq_len_source,
                                                             fixed_pos_embed_scale_up_input=True,
                                                             fixed_pos_embed_scale_down_positions=False,
                                                             prefix=prefix + C.SOURCE_POSITIONAL_EMBEDDING_PREFIX)
      encoder_seq.append(cls, **encoder_params)
      if config.conv_config is not None:
          encoder_seq.append(ConvolutionalEmbeddingEncoder, config=config.conv_config,
                             prefix=prefix + C.CHAR_SEQ_ENCODER_PREFIX)
  
      encoder_seq.append(TransformerEncoder, config=config, prefix=prefix + C.TRANSFORMER_ENCODER_PREFIX)
  
      return encoder_seq
docs: Returns a Transformer encoder, consisting of an embedding layer with
      positional encodings and a TransformerEncoder instance.
  
      :param config: Configuration for transformer encoder.
      :param prefix: Prefix for variable names.
      :return: Encoder instance.