code: def __init__(self, context):
      self.context = context
  
      hparams = self.context.get_hparams()
  
      model = client.get_model(hparams.get("model_name"))
      model_version = model.get_version(hparams.get("model_version"))
      self.context.report_task_using_model_version(model_version)
  
      path = model_version.checkpoint.download()
      training_trial = pytorch.load_trial_from_checkpoint_path(
          path, torch_load_kwargs={"map_location": torch.device("cpu")}
      )
      self.model = context.prepare_model_for_inference(
          training_trial.model
      )
  
      self.device = context.device
      self.rank = self.context.get_distributed_rank()
  
      self.counts = {}
      for rank in range(self.context.get_distributed_size()):
          self.counts[rank] = {"total_correct": 0, "total": 0}
  
file_path: determined/examples/features/inference_mnist_pytorch/inference.py
imports: import os
  from typing import Any
  import filelock
  import torch
  from torchvision import datasets, transforms
  from determined import pytorch
  from determined.experimental import client
  from determined.pytorch import experimental
label_desc: Use image node_id 1 to create a new ExampleInferenceProcessor object from inherited base classes: experimental with example: obj = ExampleInferenceProcessor(context)