code: def _get_image(input_shape, real_image, device, dtype=None):
      """This routine loads a real or random image based on `real_image` argument.
      Currently, the real image is utilized for the following list of models:
      - `retinanet_resnet50_fpn`,
      - `retinanet_resnet50_fpn_v2`,
      - `keypointrcnn_resnet50_fpn`,
      - `fasterrcnn_resnet50_fpn`,
      - `fasterrcnn_resnet50_fpn_v2`,
      - `fcos_resnet50_fpn`,
      - `maskrcnn_resnet50_fpn`,
      - `maskrcnn_resnet50_fpn_v2`,
      in `test_classification_model` and `test_detection_model`.
      To do so, a keyword argument `real_image` was added to the abovelisted models in `_model_params`
      """
      if real_image:
          # TODO: Maybe unify file discovery logic with test_image.py
          GRACE_HOPPER = os.path.join(
              os.path.dirname(os.path.abspath(__file__)),
              "assets",
              "encode_jpeg",
              "grace_hopper_517x606.jpg",
          )
  
          img = Image.open(GRACE_HOPPER)
  
          original_width, original_height = img.size
  
          # make the image square
          img = img.crop((0, 0, original_width, original_width))
          img = img.resize(input_shape[1:3])
  
          convert_tensor = transforms.ToTensor()
          image = convert_tensor(img)
          assert tuple(image.size()) == input_shape
          return image.to(device=device, dtype=dtype)
  
      # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
      return torch.rand(input_shape).to(device=device, dtype=dtype)
  
imports: import contextlib
  import functools
  import operator
  import os
  import pkgutil
  import platform
  import sys
  import warnings
  from collections import OrderedDict
  from tempfile import TemporaryDirectory
  from typing import Any
  import pytest
  import torch
  import torch.fx
  import torch.nn
  from _utils_internal import get_relative_path
  from common_utils import cpu_and_cuda, freeze_rng_state, map_nested_tensor_object, needs_cuda, set_rng_seed
  from PIL import Image
  from torchvision import models, transforms
  from torchvision.models import get_model_builder, list_models
label_desc: Use image node_id 2 for calling a global function with example usage: _get_image(input_shape, real_image, device, dtype) and returns: torch, image