code: def infer(model, img):
      # preprocess image
      aspect_ratio = img.size[0] / img.size[1]
      img = img.resize(
          (
              int(224 * max(aspect_ratio, 1.0)),
              int(224 * max(1.0 / aspect_ratio, 1.0)),
          )
      )
  
      img = np.array(img)
      y0, x0 = (np.asarray(img.shape)[:2] - 224) // 2
      retimg = img = img[y0 : y0 + 224, x0 : x0 + 224]
  
      # if you want to look at the image
      """
    import matplotlib.pyplot as plt
    plt.imshow(img)
    plt.show()
    """
  
      # run the net
      out = _infer(model, Tensor(img.astype("float32"))).numpy()
  
      # if you want to look at the outputs
      """
    import matplotlib.pyplot as plt
    plt.plot(out[0])
    plt.show()
    """
      return out, retimg
  
file_path: tinygrad/examples/efficientnet.py
imports: import sys
  import ast
  import time
  import numpy
  from PIL import Image
  from tinygrad.tensor import Tensor
  from tinygrad.helpers import getenv, fetch, Timing
  from tinygrad.jit import TinyJit
  from extra.models.efficientnet import EfficientNet
label_desc: Use image node_id 2 for calling a global function with example usage: infer(model, img) and returns: out,  retimg