class_docstr: Convert a torch.FloatTensor of shape (NUM_IMAGES x CHANNELS x HEIGHT x WIDTH) to
  a torch.FloatTensor of shape (CHANNELS x NUM_IMAGES x HEIGHT x WIDTH).
code: def forward(self, tensor):
      return tensor.permute(1, 0, 2, 3).contiguous()
  
file_path: pykale/kale/prepdata/video_transform.py
imports: import torch
  from torchvision import transforms
label_desc: Use image node_id 1 for calling the TensorPermute obj's underlying member method code with example usage: obj.forward(tensor) and returns: tensor