code: def normalize_img_stack(images):
      """Normalize pixel values to (0, 1) for stacked images.
  
      Args:
          images (list): Input data, where each sample in shape (n_phases, dim1, dim2).
  
      Returns:
          list: Normalized images, each sample in the list in shape (n_phases, dim1, dim2).
      """
      n_samples = len(images)
      for i in range(n_samples):
          n_phases = images[i].shape[0]
          for j in range(n_phases):
              img = images[i][j, ...]
              images[i][j, ...] = (img - np.min(img)) / (
                  np.max(img) - np.min(img)
              )
  
      return images
  
file_path: pykale/kale/prepdata/image_transform.py
imports: import logging
  import numpy
  import torchvision.transforms
  from skimage.transform import estimate_transform, rescale, warp
label_desc: Use image node_id 5 for calling a global function with example usage: normalize_img_stack(images) and returns: images