code: def pad_nextpow2(batch: Tensor) -> Tensor:
      """Compute required padding from input size and return padded images.
  
      Finds the largest dimension and computes a square image of dimensions that are of the power of 2.
      In case the image dimension is odd, it returns the image with an extra padding on one side.
  
      Args:
          batch (Tensor): Input images
  
      Returns:
          batch: Padded batch
      """
      # find the largest dimension
      l_dim = 2 ** math.ceil(math.log(max(*batch.shape[-2:]), 2))
      padding_w = [
          math.ceil((l_dim - batch.shape[-2]) / 2),
          math.floor((l_dim - batch.shape[-2]) / 2),
      ]
      padding_h = [
          math.ceil((l_dim - batch.shape[-1]) / 2),
          math.floor((l_dim - batch.shape[-1]) / 2),
      ]
      padded_batch = F.pad(batch, pad=[*padding_h, *padding_w])
      return padded_batch
  
file_path: anomalib/src/anomalib/data/utils/image.py
imports: from __future__ import annotations
  import math
  import warnings
  from pathlib import Path
  import cv2
  import numpy
  import tifffile
  import torch.nn.functional
  from torch import Tensor
  from torchvision.datasets.folder import IMG_EXTENSIONS
label_desc: Use image node_id 7 for calling a global function with example usage: pad_nextpow2(batch) and returns: padded_batch