code: def concatenate_segments(segments):
      """Concatenate input segments for a model's input sequence.
  
      `concatenate_segments` combines the tokens of one or more input segments to a
      single sequence of token values and generates matching segment ids.
      `concatenate_segments` can follow a `Trimmer`, who limit segment lengths and
      emit `RaggedTensor` outputs, and can be followed up by `ModelInputPacker`.
  
      `concatenate_segments` first flattens and combines a list of one or more
      segments
      (`RaggedTensor`s of n dimensions) together along the 1st axis, then packages
      any special tokens  into a final n dimensional `RaggedTensor`.
  
      And finally `concatenate_segments` generates another `RaggedTensor` (with the
      same rank as the final combined `RaggedTensor`) that contains a distinct int
      id for each segment.
  
      Example usage:
  
      ```
      segment_a = [[1, 2],
                   [3, 4,],
                   [5, 6, 7, 8, 9]]
  
      segment_b = [[10, 20,],
                   [30, 40, 50, 60,],
                   [70, 80]]
      expected_combined, expected_ids = concatenate_segments([segment_a, segment_b])
  
      # segment_a and segment_b have been concatenated as is.
      expected_combined=[
       [1, 2, 10, 20],
       [3, 4, 30, 40, 50, 60],
       [5, 6, 7, 8, 9, 70, 80],
      ]
  
      # ids describing which items belong to which segment.
      expected_ids=[
       [0, 0, 1, 1],
       [0, 0, 1, 1, 1, 1],
       [0, 0, 0, 0, 0, 1, 1]]
      ```
  
      Args:
        segments: A list of `RaggedTensor`s with the tokens of the input segments.
          All elements must have the same dtype (int32 or int64), same rank, and
          same dimension 0 (namely batch size). Slice `segments[i][j, ...]`
          contains the tokens of the i-th input segment to the j-th example in the
          batch.
  
      Returns:
        a tuple of (combined_segments, segment_ids), where:
  
        combined_segments: A `RaggedTensor` with segments combined and special
          tokens inserted.
        segment_ids:  A `RaggedTensor` w/ the same shape as `combined_segments`
          and containing int ids for each item detailing the segment that they
          correspond to.
      """
  
      # Create special tokens ([CLS] and [SEP]) that will be combined with the
      # segments
      if len(segments) <= 0:
          raise ValueError("`segments` must be a nonempty list.")
      segment_dtype = segments[0].dtype
      if segment_dtype not in (dtypes.int32, dtypes.int64):
          raise ValueError(
              "`segments` must have elements with dtype of int32 or "
              + "int64"
          )
  
      # Combine all segments.
      segments_to_combine = []
      for seg in segments:
          segments_to_combine.append(seg)
      segments_combined = array_ops.concat(segments_to_combine, 1)
  
      # Create the segment ids, making sure to account for special tokens.
      segment_ids_to_combine = []
      for i, item in enumerate(segments):
          # Add segment id
          segment_id = array_ops.ones_like(item) * i
          segment_ids_to_combine.append(segment_id)
  
      segment_ids = array_ops.concat(segment_ids_to_combine, 1)
      return segments_combined, segment_ids
  
imports: from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
label_desc: Use image node_id 2 for calling a global function with example usage: concatenate_segments(segments) and returns: segments_combined,  segment_ids