code: def image_list_to_tensor(images: List[Any]) -> Tensor:
      """Converts a list of numpy images to a PyTorch 4d tensor image.
  
      Args:
          images: list of images, each of the form :math:`(H, W, C)`.
          Image shapes must be consistent
  
      Returns:
          tensor of the form :math:`(B, C, H, W)`.
  
      Example:
          >>> imgs = [np.ones((4, 4, 1)), np.zeros((4, 4, 1))]
          >>> image_list_to_tensor(imgs).shape
          torch.Size([2, 1, 4, 4])
      """
      if not images:
          raise ValueError("Input list of numpy images is empty")
      if len(images[0].shape) != 3:
          raise ValueError(
              "Input images must be three dimensional arrays"
          )
  
      list_of_tensors: List[Tensor] = []
      for image in images:
          list_of_tensors.append(image_to_tensor(image))
      tensor: Tensor = torch.stack(list_of_tensors)
      return tensor
  
file_path: kornia/kornia/utils/image.py
imports: from functools import wraps
  from typing import Any, Callable, List
  import torch
  from torch import nn
  from kornia.core import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: image_list_to_tensor(images) and returns: tensor