code: def setUp(self):
      self.image_size = (3, 32, 128)
      self.tmpdirname = tempfile.mkdtemp()
  
      vocab = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']  # fmt: skip
      vocab_tokens = dict(zip(vocab, range(len(vocab))))
  
      self.vocab_file = os.path.join(
          self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]
      )
      with open(self.vocab_file, "w", encoding="utf-8") as fp:
          fp.write(json.dumps(vocab_tokens) + "\n")
  
      image_processor_map = {
          "do_normalize": False,
          "do_resize": True,
          "image_processor_type": "ViTImageProcessor",
          "resample": 3,
          "size": {"height": 32, "width": 128},
      }
      self.image_processor_file = os.path.join(
          self.tmpdirname, IMAGE_PROCESSOR_NAME
      )
      with open(self.image_processor_file, "w", encoding="utf-8") as fp:
          json.dump(image_processor_map, fp)
  
imports: import json
  import os
  import shutil
  import tempfile
  import unittest
  import numpy
  import pytest
  from transformers import MgpstrTokenizer
  from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
label_desc: Use image node_id 2 for calling the MgpstrProcessorTest obj's underlying member method code with example usage: obj.setUp() without return types