code: def test_fuse_multiple(self) -> None:
      n = 100
      num_multiple = 7
      x = np.hstack(
          [
              np.random.randint(0, 3, [n, 1]),
              np.random.random([n, 2]),
              np.random.randint(0, 11, [n, 1]),
              np.random.random([n, 3]),
          ]
      )
      y = np.random.random([n, 5])
      ps = []
      packed_list = []
      data = cflearn.MLData.init().fit(x, y)
      for i in range(num_multiple):
          m = cflearn.api.fit_ml(
              data,
              config=cflearn.MLConfig(
                  model_name="fcnn",
                  model_config=dict(
                      input_dim=data.num_features,
                      output_dim=data.num_labels,
                  ),
                  loss_name="mae",
                  encoder_settings={
                      "0": cflearn.MLEncoderSettings(3),
                      "3": cflearn.MLEncoderSettings(11),
                  },
              ),
              debug=True,
          )
          loader = data.build_loader(x)
          ps.append(m.predict(loader)[cflearn.PREDICTIONS_KEY])
          packed = f"./m{i}"
          cflearn.DLPipelineSerializer.save(m, packed, compress=True)
          packed_list.append(packed)
      fused = cflearn.api.fuse_inference(packed_list)
      loader = fused.data.build_loader(x)
      pf = fused.predict(loader)[cflearn.PREDICTIONS_KEY]
      assert np.allclose(np.stack(ps).mean(axis=0), pf)
  
file_path: carefree-learn/tests/test_pipeline.py
imports: import os
  import torch
  import cflearn
  import unittest
  import numpy
label_desc: Use image node_id 6 for calling the TestPipeline obj's underlying member method code with example usage: obj.test_fuse_multiple() without return types