code: def merge_strings(a: str, b: str, dil_factor: float) -> str:
      """Merges 2 character sequences in the best way to maximize the alignment of their overlapping characters.
  
      Args:
      ----
          a: first char seq, suffix should be similar to b's prefix.
          b: second char seq, prefix should be similar to a's suffix.
          dil_factor: dilation factor of the boxes to overlap, should be > 1. This parameter is
              only used when the mother sequence is splitted on a character repetition
  
      Returns:
      -------
          A merged character sequence.
  
      Example::
          >>> from doctr.model.recognition.utils import merge_sequences
          >>> merge_sequences('abcd', 'cdefgh', 1.4)
          'abcdefgh'
          >>> merge_sequences('abcdi', 'cdefgh', 1.4)
          'abcdefgh'
      """
      seq_len = min(len(a), len(b))
      if seq_len == 0:  # One sequence is empty, return the other
          return b if len(a) == 0 else a
  
      # Initialize merging index and corresponding score (mean Levenstein)
      min_score, index = 1.0, 0  # No overlap, just concatenate
  
      scores = [
          Levenshtein.distance(a[-i:], b[:i], processor=None) / i
          for i in range(1, seq_len + 1)
      ]
  
      # Edge case (split in the middle of char repetitions): if it starts with 2 or more 0
      if len(scores) > 1 and (scores[0], scores[1]) == (0, 0):
          # Compute n_overlap (number of overlapping chars, geometrically determined)
          n_overlap = round(len(b) * (dil_factor - 1) / dil_factor)
          # Find the number of consecutive zeros in the scores list
          # Impossible to have a zero after a non-zero score in that case
          n_zeros = sum(val == 0 for val in scores)
          # Index is bounded by the geometrical overlap to avoid collapsing repetitions
          min_score, index = 0, min(n_zeros, n_overlap)
  
      else:  # Common case: choose the min score index
          for i, score in enumerate(scores):
              if score < min_score:
                  min_score, index = (
                      score,
                      i + 1,
                  )  # Add one because first index is an overlap of 1 char
  
      # Merge with correct overlap
      if index == 0:
          return a + b
      return a[:-1] + b[index - 1 :]
  
label_desc: Use image node_id 1 for calling a global function with example usage: merge_strings(a, b, dil_factor) and returns: unknown, unknown, unknown