code: def run(mini_batch, mini_batch_context):
      print(
          f"run method start: {__file__}, run({mini_batch}, {mini_batch_context})"
      )
      # `partition_key_value` is a dict that corresponds to the mini_batch, the keys of the dict are those specified
      # in `partition_keys` in ParallelRunConfig.
      print(
          f"partition_key_value = {mini_batch_context.partition_key_value}"
      )
      # `dataset` is the dataset object that corresponds to the mini_batch, which is a subset of the input dataset
      # filtered by condition specified in `partition_key_value`.
      print(f"dataset = {mini_batch_context.dataset}")
  
      print(f"file_count_of_mini_batch = {len(mini_batch)}")
      file_name_list = []
      file_size_list = []
      total_file_size_of_mini_batch = 0
      for file_path in mini_batch:
          file_name_list.append(os.path.basename(file_path))
          file_size = os.path.getsize(file_path)
          file_size_list.append(file_size)
          total_file_size_of_mini_batch += file_size
      print(
          f"total_file_size_of_mini_batch = {total_file_size_of_mini_batch}"
      )
      file_size_ratio_list = [
          file_size * 1.0 / total_file_size_of_mini_batch
          for file_size in file_size_list
      ]
  
      # If `output_action` is set to `append_row` in ParallelRunConfig for FileDataset input(as is in this sample
      # notebook), the return value of `run` method is expected to be a list/tuple of same length with the
      # input parameter `mini_batch`, and each element in the list/tuple would form a row in the result file by
      # calling the Python builtin `str` function.
      # If you want to specify the output format, please format and return str value as in this example.
      return [
          ",".join([str(x) for x in fields])
          for fields in zip(
              file_name_list,
              file_size_list,
              file_size_ratio_list,
              [mini_batch_context.partition_key_value["user"]]
              * len(mini_batch),
              [mini_batch_context.partition_key_value["genres"]]
              * len(mini_batch),
              [total_file_size_of_mini_batch] * len(mini_batch),
          )
      ]
  
label_desc: Use image node_id 2 for calling a global function with example usage: run(mini_batch, mini_batch_context) and returns: unknown