code: def test_lstm_forecaster_eval_with_onnx_shuffle_loader(self):
      from torch.utils.data import DataLoader, TensorDataset
      from numpy.testing import assert_almost_equal
  
      train_data, val_data, test_data = create_data()
      forecaster = LSTMForecaster(
          past_seq_len=24,
          input_feature_num=2,
          output_feature_num=2,
          loss="mse",
          lr=0.01,
      )
      forecaster.fit(train_data, epochs=2)
      test_loader_shuffle_f = DataLoader(
          TensorDataset(
              torch.from_numpy(test_data[0]),
              torch.from_numpy(test_data[1]),
          ),
          batch_size=32,
          shuffle=False,
      )
      test_loader_shuffle_t = DataLoader(
          TensorDataset(
              torch.from_numpy(test_data[0]),
              torch.from_numpy(test_data[1]),
          ),
          batch_size=32,
          shuffle=True,
      )
      eval_f = forecaster.evaluate_with_onnx(test_loader_shuffle_f)
      eval_t = forecaster.evaluate_with_onnx(test_loader_shuffle_t)
      assert_almost_equal(eval_f, eval_t)
  
imports: import numpy
  import tempfile
  import os
  from bigdl.chronos.utils import LazyImport
  from unittest import TestCase
  import pytest
  from ..None import op_torch, op_distributed, op_inference, op_automl
label_desc: Use image node_id 33 for calling the TestChronosModelLSTMForecaster obj's underlying member method code with example usage: obj.test_lstm_forecaster_eval_with_onnx_shuffle_loader() without return types