code: def process_batch(self, batch, batch_idx) -> None:
      output_list = []
      model_input = batch[0]
      model_input = self.context.to_device(model_input)
      file_name = f"prediction_output_{batch_idx}"
      with torch.no_grad():
          pred = self.model(model_input)
          output = {"predictions": pred, "input": batch}
          output_list.append(output)
  
      # Automatic upload to output to the same storage used by determined checkpoints
      with self.context.upload_path() as path:
          file_path = pathlib.Path(path, file_name)
          torch.save(output_list, file_path)
  
file_path: determined/examples/features/torch_batch_process_core_api_comparison/torch_batch_process_inference.py
imports: import os
  import pathlib
  import constants
  import filelock
  import model
  import torch
  import torchvision
  import torchvision.transforms
  from determined.pytorch import experimental
label_desc: Use image node_id 2 for calling the MyProcessor obj's underlying member method code with example usage: obj.process_batch(batch, batch_idx) without return types