code: def one_hot_segments(start_indices, end_indices, num_samples):
      """Returns a one-hot, float matrix of segments at each timestep.
  
      All integers in the inclusive range of start_indices and end_indices are used.
      This allows start and end timestamps to be mapped to the same index and the
      segment will not be omitted.
  
      Args:
        start_indices: a 1d tensor of integer indices for the start of each
          segement.
        end_indices: a tensor of integer indices for the end of each segment.
          Must be the same shape as start_indices. Values should be >= start_indices
          but not strictly enforced.
        num_samples: the number of rows in the output. Indices should be <
          num_samples, but this is not strictly enforced.
      Returns:
        (segments, indicator)
        segments: A [num_samples, num_elements(start_indices)] tensor where in each
          column the rows with indices >= start_indices[column] and
          <= end_indices[column] are 1.0 and all other values are 0.0.
        indicator: a tensor of 1.0 values with shape [num_samples, 1]. If padded
          with zeros to align sizes, the indicator marks where segments is valid.
      """
      start_indices = tf.convert_to_tensor(start_indices)
      end_indices = tf.convert_to_tensor(end_indices)
      start_indices.shape.assert_is_compatible_with(end_indices.shape)
      start_indices.shape.assert_has_rank(1)
      end_indices.shape.assert_has_rank(1)
      # create a matrix of the index at each row with a column per segment.
      indices = tf.to_int64(
          tf.tile(
              tf.transpose(tf.expand_dims(tf.range(num_samples), 0)),
              [1, tf.shape(start_indices)[0]],
          )
      )
      # switch to one hot encoding of segments (includes start and end indices)
      segments = tf.to_float(
          tf.logical_and(
              tf.greater_equal(indices, start_indices),
              tf.less_equal(indices, end_indices),
          )
      )
      # create a tensors of ones everywhere there's an annotation. If padded with
      # zeros later, element-wise multiplication of the loss will mask out the
      # padding.
      indicator = tf.ones(shape=[num_samples, 1], dtype=tf.float32)
      return segments, indicator
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import contextlib
  import csv
  import os
  import random
  import subprocess
  import sys
  import tempfile
  import zipfile
  from absl import app
  from absl import flags
  from absl import logging
  from six.moves import range
  from six.moves import urllib
  import tensorflow.compat.v1
  from mediapipe.util.sequence import media_sequence
label_desc: Use image node_id 1 for calling a global function with example usage: one_hot_segments(start_indices, end_indices, num_samples) and returns: segments,  indicator