code: def np_img_to_torch_img(np_img):
      """convert a numpy image to torch image
      numpy use Height x Width x Channels
      torch use Channels x Height x Width
      Arguments:
          np_img {[type]} -- [description]
      """
      assert isinstance(
          np_img, np.ndarray
      ), f"cannot process data type: {type(np_img)}"
      if len(np_img.shape) == 4 and (
          np_img.shape[3] == 3 or np_img.shape[3] == 1
      ):
          return torch.from_numpy(np.transpose(np_img, (0, 3, 1, 2)))
      if len(np_img.shape) == 3 and (
          np_img.shape[2] == 3 or np_img.shape[2] == 1
      ):
          return torch.from_numpy(np.transpose(np_img, (2, 0, 1)))
      elif len(np_img.shape) == 2:
          return torch.from_numpy(np_img)
      else:
          raise ValueError(
              f"cannot process this image with shape: {np_img.shape}"
          )
  
imports: import os
  import random
  import numpy
  import open3d
  import torch
  import pytorch3d
  import matplotlib.pyplot
  import matplotlib
  from scipy import ndimage
  from tqdm import tqdm
  from models.smpl import SMPL, vertices2joints
label_desc: Use image node_id 6 for calling a global function with example usage: np_img_to_torch_img(np_img) and returns: torch, torch, torch