code: def _get_elephant(target_size):
      # For models that don't include a Flatten step,
      # the default is to accept variable-size inputs
      # even when loading ImageNet weights (since it is possible).
      # In this case, default to 299x299.
      TEST_IMAGE_PATH = (
          "https://storage.googleapis.com/tensorflow/"
          "keras-applications/tests/elephant.jpg"
      )
  
      if target_size[0] is None:
          target_size = (299, 299)
      test_image = file_utils.get_file("elephant.jpg", TEST_IMAGE_PATH)
      img = image_utils.load_img(
          test_image, target_size=tuple(target_size)
      )
      x = image_utils.img_to_array(img)
      return np.expand_dims(x, axis=0)
  
imports: import os
  import numpy
  import pytest
  from absl.testing import parameterized
  from keras import backend
  from keras import testing
  from keras.applications import convnext
  from keras.applications import densenet
  from keras.applications import efficientnet
  from keras.applications import efficientnet_v2
  from keras.applications import inception_resnet_v2
  from keras.applications import inception_v3
  from keras.applications import mobilenet
  from keras.applications import mobilenet_v2
  from keras.applications import mobilenet_v3
  from keras.applications import nasnet
  from keras.applications import resnet
  from keras.applications import resnet_v2
  from keras.applications import vgg16
  from keras.applications import vgg19
  from keras.applications import xception
  from keras.saving import serialization_lib
  from keras.utils import file_utils
  from keras.utils import image_utils
label_desc: Use image node_id 1 for calling a global function with example usage: _get_elephant(target_size) and returns: np