code: def model_inputs(self):
      return {
          "input_ids": torch.LongTensor(
              [
                  [
                      28768,
                      248,
                      6399,
                      9,
                      65972,
                      452,
                      1925,
                      629,
                      123543,
                      248075,
                      2,
                      256047,
                  ],
                  [
                      117,
                      7027,
                      7195,
                      202,
                      44778,
                      248075,
                      2,
                      256047,
                      1,
                      1,
                      1,
                      1,
                  ],
              ]
          ),
          "attention_mask": torch.Tensor(
              [
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                  [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
              ]
          ),
          "decoder_input_ids": torch.LongTensor(
              [[2, 256057], [2, 256057]]
          ),
      }
  
imports: import copy
  import tempfile
  import unittest
  from transformers import NllbMoeConfig, is_torch_available, set_seed
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the NllbMoeModelIntegrationTests obj's underlying member method code with example usage: obj.model_inputs() and returns: dict