code: def _get_encode_output_mapping_dict(dec_layer_num):
      encode_output_kernel_pattern = [
          "EncDecAttention {0} k weight&&EncDecAttention {0} v weight".format(
              ele
          )
          for ele in range(dec_layer_num)
      ]
  
      return {
          "encode_output_project_kernel_kv": "&&".join(
              encode_output_kernel_pattern
              + ["expression_.transpose(0, 1)"]
          )
      }
  
file_path: lightseq/examples/inference/python/export/huggingface/hf_mt5_export.py
imports: import os
  from collections import OrderedDict
  import tensorflow
  import h5py
  import numpy
  from operator import attrgetter
  from lightseq.training.ops.pytorch.export import gather_token_embedding, fill_pb_layer
  from export.proto.mt5_pb2 import MT5
  from transformers import MT5ForConditionalGeneration
  from export.util import parse_args
label_desc: Use image node_id 1 for calling a global function with example usage: _get_encode_output_mapping_dict(dec_layer_num) and returns: dict