code: def test_MMF_activity_recognition_model_register_and_inference_on_valid_model():
      test_utils.start_torchserve(snapshot_file=snapshot_file_tf)
      test_utils.register_model(
          "MMF_activity_recognition_v2",
          "https://torchserve.pytorch.org/mar_files/MMF_activity_recognition_v2.mar",
      )
      os.system(
          "wget https://mmfartifacts.s3-us-west-2.amazonaws.com/372CC.mp4 -P ../../examples/MMF-activity-recognition"
      )
      input_json = (
          "../../examples/MMF-activity-recognition/372CC.info.json"
      )
      with open(input_json) as jsonfile:
          info = json.load(jsonfile)
  
      files = {
          "data": open(
              "../../examples/MMF-activity-recognition/372CC.mp4", "rb"
          ),
          "script": info["script"],
          "labels": info["action_labels"],
      }
      response = run_inference_using_url_with_data(
          TF_INFERENCE_API
          + "/v1/models/MMF_activity_recognition_v2:predict",
          pfiles=files,
      )
      response = response.content.decode("utf-8")
      response = ast.literal_eval(response)
      response = [n.strip() for n in response]
      assert response == [
          "Sitting at a table",
          "Someone is sneezing",
          "Watching a laptop or something on a laptop",
      ]
      test_utils.unregister_model("MMF_activity_recognition_v2")
  
imports: import ast
  import json
  import logging
  import os
  import numpy
  import pytest
  import requests
  import test_utils
  import torch
label_desc: Use image node_id 17 for calling a global function with example usage: test_MMF_activity_recognition_model_register_and_inference_on_valid_model() without return types