code: def patchify(images: torch.Tensor, patch_size: int) -> torch.Tensor:
      """Converts a batch of input images into patches.
  
      Args:
          images:
              Images tensor with shape (batch_size, channels, height, width)
          patch_size:
              Patch size in pixels. Image width and height must be multiples of
              the patch size.
  
      Returns:
          Patches tensor with shape (batch_size, num_patches, channels * patch_size ** 2)
          where num_patches = image_width / patch_size * image_height / patch_size.
  
      """
      # N, C, H, W = (batch_size, channels, height, width)
      N, C, H, W = images.shape
      assert H == W and H % patch_size == 0
  
      patch_h = patch_w = H // patch_size
      num_patches = patch_h * patch_w
      patches = images.reshape(
          shape=(N, C, patch_h, patch_size, patch_w, patch_size)
      )
      patches = torch.einsum("nchpwq->nhwpqc", patches)
      patches = patches.reshape(
          shape=(N, num_patches, patch_size**2 * C)
      )
      return patches
  
imports: import math
  import warnings
  from typing import Iterable, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  from numpy.typing import NDArray
  from torch.nn import Module
  from torch.nn.modules.batchnorm import _BatchNorm
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 17 for calling a global function with example usage: patchify(images, patch_size) and returns: patches