code: def _represent_alt_aligned_pileups(
      representation, ref_image, alt_images
  ):
      """Combines ref and alt-aligned pileup images according to the representation.
  
      Args:
        representation: string, one of "rows", "base_channels", "diff_channels".
        ref_image: 3D numpy array. The original pileup image.
        alt_images: list of either one or two 3D numpy arrays, both of the same
          dimensions as ref_image. Pileup image(s) of the same reads aligned to the
          alternate haplotype(s).
  
      Returns:
        One 3D numpy array containing a selection of data from the input arrays.
      """
  
      # If there is only one alt, duplicate it to make all pileups the same size.
      if len(alt_images) == 1:
          alt_images = alt_images + alt_images
      if len(alt_images) != 2:
          raise ValueError(
              "alt_images must contain exactly one or two arrays."
          )
  
      # Ensure that all three pileups have the same shape.
      if (
          not ref_image.shape
          == alt_images[0].shape
          == alt_images[1].shape
      ):
          raise ValueError(
              "Pileup images must be the same shape to be combined. "
              "ref_image.shape is {}. alt_images[0].shape is {}. "
              "alt_images[1].shape is {}.".format(
                  ref_image.shape,
                  alt_images[0].shape,
                  alt_images[1].shape,
              )
          )
  
      if representation == "rows":
          # Combine all images: [ref, alt1, alt2].
          return np.concatenate([ref_image] + alt_images, axis=0)
      elif representation == "base_channels":
          channels = [
              ref_image[:, :, c] for c in range(ref_image.shape[2])
          ]
          # Add channel 0 (bases ATCG) of both alts as channels.
          channels.append(alt_images[0][:, :, 0])
          channels.append(alt_images[1][:, :, 0])
          return np.stack(channels, axis=2)
      elif representation == "diff_channels":
          channels = [
              ref_image[:, :, c] for c in range(ref_image.shape[2])
          ]
          # Add channel 5 (base differs from ref) of both alts as channels.
          channels.append(alt_images[0][:, :, 5])
          channels.append(alt_images[1][:, :, 5])
          return np.stack(channels, axis=2)
      else:
          raise ValueError(
              "_represent_alt_aligned_pileups received invalid value for "
              'representation: "{}". Must be one of '
              "rows, base_channels, or diff_channels.".format(
                  representation
              )
          )
  
imports: import itertools
  from typing import Iterable, List, Optional, Tuple
  from absl import logging
  import numpy
  from deepvariant import dv_constants
  from deepvariant import sample
  from deepvariant.protos import deepvariant_pb2
  from deepvariant.python import pileup_image_native
  from third_party.nucleus.io import fasta
  from third_party.nucleus.io import sam
  from third_party.nucleus.protos import reads_pb2
  from third_party.nucleus.protos import variants_pb2
  from third_party.nucleus.util import ranges
label_desc: Use image node_id 3 for calling a global function with example usage: _represent_alt_aligned_pileups(representation, ref_image, alt_images) and returns: np, np, np