code: def _pad_image(img, crop_size=480):
      b, c, h, w = img.shape
      assert c == 3
      padh = crop_size - h if h < crop_size else 0
      padw = crop_size - w if w < crop_size else 0
      mean = [0.485, 0.456, 0.406]
      std = [0.229, 0.224, 0.225]
      pad_values = -np.array(mean) / np.array(std)
      img_pad = mx.nd.zeros((b, c, h + padh, w + padw)).as_in_context(
          img.context
      )
      for i in range(c):
          img_pad[:, i, :, :] = mx.nd.squeeze(
              mx.nd.pad(
                  img[:, i, :, :].expand_dims(1),
                  "constant",
                  pad_width=(0, 0, 0, 0, 0, padh, 0, padw),
                  constant_value=pad_values[i],
              )
          )
      assert (
          img_pad.shape[2] >= crop_size
          and img_pad.shape[3] >= crop_size
      )
      return img_pad
  
imports: import math
  import warnings
  import numpy
  import mxnet
  from mxnet.ndarray import NDArray
  from mxnet.gluon.nn import HybridBlock
  from ..utils.parallel import parallel_apply
  from .resnetv1b import resnet18_v1b, resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
  from .resnest import resnest50, resnest101, resnest200, resnest269
  from ..utils.parallel import tuple_map
label_desc: Use image node_id 4 for calling a global function with example usage: _pad_image(img, crop_size) and returns: img_pad