code: def check_encoder_decoder_model_standalone(self, config, inputs_dict):
      model = AutoformerModel(config=config).to(torch_device).eval()
      outputs = model(**inputs_dict)
  
      encoder_last_hidden_state = outputs.encoder_last_hidden_state
      last_hidden_state = outputs.last_hidden_state
  
      with tempfile.TemporaryDirectory() as tmpdirname:
          encoder = model.get_encoder()
          encoder.save_pretrained(tmpdirname)
          encoder = AutoformerEncoder.from_pretrained(tmpdirname).to(
              torch_device
          )
  
      (
          transformer_inputs,
          feature,
          _,
          _,
          _,
      ) = model.create_network_inputs(**inputs_dict)
      seasonal_input, trend_input = model.decomposition_layer(
          transformer_inputs[:, : config.context_length, ...]
      )
  
      enc_input = torch.cat(
          (
              transformer_inputs[:, : config.context_length, ...],
              feature[:, : config.context_length, ...],
          ),
          dim=-1,
      )
      encoder_last_hidden_state_2 = encoder(inputs_embeds=enc_input)[0]
      self.parent.assertTrue(
          (encoder_last_hidden_state_2 - encoder_last_hidden_state)
          .abs()
          .max()
          .item()
          < 1e-3
      )
  
      mean = (
          torch.mean(
              transformer_inputs[:, : config.context_length, ...], dim=1
          )
          .unsqueeze(1)
          .repeat(1, config.prediction_length, 1)
      )
      zeros = torch.zeros(
          [
              transformer_inputs.shape[0],
              config.prediction_length,
              transformer_inputs.shape[2],
          ],
          device=enc_input.device,
      )
  
      dec_input = torch.cat(
          (
              torch.cat(
                  (
                      seasonal_input[:, -config.label_length :, ...],
                      zeros,
                  ),
                  dim=1,
              ),
              feature[
                  :, config.context_length - config.label_length :, ...
              ],
          ),
          dim=-1,
      )
      trend_init = torch.cat(
          (
              torch.cat(
                  (trend_input[:, -config.label_length :, ...], mean),
                  dim=1,
              ),
              feature[
                  :, config.context_length - config.label_length :, ...
              ],
          ),
          dim=-1,
      )
  
      with tempfile.TemporaryDirectory() as tmpdirname:
          decoder = model.get_decoder()
          decoder.save_pretrained(tmpdirname)
          decoder = AutoformerDecoder.from_pretrained(tmpdirname).to(
              torch_device
          )
  
      last_hidden_state_2 = decoder(
          trend=trend_init,
          inputs_embeds=dec_input,
          encoder_hidden_states=encoder_last_hidden_state,
      )[0]
  
      self.parent.assertTrue(
          (last_hidden_state_2 - last_hidden_state).abs().max().item()
          < 1e-3
      )
  
imports: import inspect
  import tempfile
  import unittest
  from huggingface_hub import hf_hub_download
  from transformers import is_torch_available
  from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 6 for calling the AutoformerModelTester obj's underlying member method code with example usage: obj.check_encoder_decoder_model_standalone(config, inputs_dict) without return types