code: def prepare_config_and_inputs(self):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
      input_ids[:, -1] = self.eos_token_id  # Eos Token
      decoder_input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
  
      # we need to clamp the input ids here to avoid having pad token in between
      # this is because for M2M100 the position_ids are prepared such that
      # all pad tokens have pos id = 2 and rest are between 2..seq_length
      # and the seq_length here is seq_length - num_pad_tokens
      # but when using past, there is no way of knowing if the past input ids had
      # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
      # position_ids being off by num_pad_tokens in past input
      input_ids = input_ids.clamp(self.pad_token_id + 1)
      decoder_input_ids = decoder_input_ids.clamp(self.pad_token_id + 1)
  
      config = self.get_config()
      inputs_dict = prepare_m2m_100_inputs_dict(
          config, input_ids, decoder_input_ids
      )
      return config, inputs_dict
  
imports: import copy
  import tempfile
  import unittest
  from transformers import M2M100Config, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
label_desc: Use image node_id 2 for calling the M2M100ModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  inputs_dict