code: def test_pytorch_predictor_serde():
      context_length = 20
      prediction_length = 5
  
      transformation = InstanceSplitter(
          target_field=FieldName.TARGET,
          is_pad_field=FieldName.IS_PAD,
          start_field=FieldName.START,
          forecast_start_field=FieldName.FORECAST_START,
          instance_sampler=TestSplitSampler(),
          past_length=context_length,
          future_length=prediction_length,
      )
  
      pred_net = RandomNetwork(
          prediction_length=prediction_length,
          context_length=context_length,
      )
  
      predictor = PyTorchPredictor(
          prediction_length=prediction_length,
          input_names=["past_target"],
          prediction_net=pred_net,
          batch_size=16,
          input_transform=transformation,
          device="cpu",
      )
  
      with tempfile.TemporaryDirectory() as temp_dir:
          predictor.serialize(Path(temp_dir))
          predictor_exp = Predictor.deserialize(Path(temp_dir))
      assert predictor == predictor_exp
  
file_path: gluonts/test/torch/model/test_torch_predictor.py
imports: import tempfile
  from pathlib import Path
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.dataset.field_names import FieldName
  from gluonts.model.predictor import Predictor
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.transform import InstanceSplitter, TestSplitSampler
label_desc: Use image node_id 1 for calling a global function with example usage: test_pytorch_predictor_serde() without return types