code: def split(input, axis, epsilon, name=None):
      """
      Split the audio by removing the noise smaller than epsilon.
  
      Args:
        input: An audio Tensor.
        axis: The axis to trim.
        epsilon: The max value to be considered as noise.
        name: A name for the operation (optional).
      Returns:
        A tensor of start and stop with shape `[..., 2, ...]`.
      """
      shape = tf.shape(input, out_type=tf.int64)
      length = shape[axis]
  
      nonzero = tf.math.greater(input, epsilon)
  
      rank = tf.cast(tf.rank(input), tf.int64)
      mask = tf.math.equal(tf.range(rank), axis)
  
      fill = tf.zeros(tf.where(mask, 1, shape), tf.int8)
  
      curr = tf.cast(nonzero, tf.int8)
      prev = tf.concat(
          [
              fill,
              tf.slice(
                  curr,
                  tf.where(mask, tf.constant(0, tf.int64), 0),
                  tf.where(mask, length - 1, shape),
              ),
          ],
          axis=axis,
      )
      next = tf.concat(
          [
              tf.slice(
                  curr,
                  tf.where(mask, tf.constant(1, tf.int64), 0),
                  tf.where(mask, length - 1, shape),
              ),
              fill,
          ],
          axis=axis,
      )
  
      # TODO: validate lower == upper except for axis
      lower = tf.where(tf.math.equal(curr - prev, 1))
      upper = tf.where(tf.math.equal(next - curr, -1))
  
      # Fix values with -1 (where indices is not available)
      start = core_ops.io_order_indices(lower, shape, axis)
      start = tf.where(tf.math.greater_equal(start, 0), start, length)
      stop = core_ops.io_order_indices(upper, shape, axis)
      stop = tf.where(tf.math.greater_equal(stop, 0), stop + 1, length)
  
      return tf.stack([start, stop], axis=axis + 1)
  
imports: import sys
  import tensorflow
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 6 for calling a global function with example usage: split(input, axis, epsilon, name) and returns: tf