code: def analyze_diff_and_nearby_variants(
      channels: List[np.ndarray],
  ) -> Tuple[float, int]:
      """Analyzes which differences belong to nearby variants and which do not.
  
      This attempts to identify putative nearby variants from the pileup image
      alone, and then excludes these columns of the pileup to calculate the
      remaining fraction of differences that may be attributed to sequencing errors.
  
      Args:
          channels: A list of channels of a DeepVariant pileup image. This only uses
            channels[5], the 'differs from ref' channel.
  
      Returns:
          Two outputs: diff fraction, number of likely nearby variants.
      """
      diff_channel = remove_ref_band(channels[5])
  
      # Count the number of diff pixels per column.
      column_diffs = np.sum(diff_channel == 254, axis=0)
      # Count number of differences per base position.
      column_read_count = np.sum(diff_channel != 0, axis=0)
      # Divide to get the fraction of reads showing a diff at each base (column).
      # Adding 1 here avoids dividing by zero (exact fraction here is not vital).
      fraction = column_diffs * 1.0 / (column_read_count + 1)
  
      # Columns with more differences could be variants.
      nearby_variant_columns = (fraction > 0.1) * (column_diffs > 4) * 1
      num_potential_nearby_variants = sum(nearby_variant_columns)
  
      # Exclude potential variants when calculating error fraction.
      nearby_variant_mask = np.array(
          [nearby_variant_columns] * diff_channel.shape[0]
      )
      mask_to_remove_nearby_variants = 1 - nearby_variant_mask
      non_variant_diffs = (
          diff_channel == 254
      ) * mask_to_remove_nearby_variants
  
      # Calculate differences as fraction of the total number of read bases.
      total_read_area = np.sum((diff_channel != 0))
      diff_fraction = (
          0
          if total_read_area == 0
          else np.sum(non_variant_diffs) / total_read_area
      )
      return diff_fraction, num_potential_nearby_variants
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import enum
  import math
  from typing import List, NamedTuple, Tuple
  from etils import epath
  from IPython import display
  import numpy
  from PIL import Image
  from PIL import ImageDraw
  from third_party.nucleus.protos import variants_pb2
label_desc: Use image node_id 27 for calling a global function with example usage: analyze_diff_and_nearby_variants(channels) and returns: diff_fraction,  num_potential_nearby_variants