code: def _clip(
      max_indices: Sequence[TfVal],
      start_indices: Sequence[TfVal],
      slice_sizes: Sequence[TfVal],
  ):
      """Simulates XLA clipping behavior with TF ops.
  
      Various TF ops have different clipping behavior than XLA:
      * If `start_indices` is out-of-bounds, then TF fails but XLA clips the indices
      to
        [0, max_len].
      * If `start_indices + slice_size` is out-of-bounds, then TF fails, but XLA
      adjust
        `start_indices` so that a full slice is returned.
      This function clips the start indices correctly.
      """
      # We cast both arguments to `tf.clip_by_value` to int32. Otherwise, this
      # function may return uint32 which is not always compatible with TF ops, so
      # this may result in type errors.
      max_start = tf.cast(
          tf.subtract(max_indices, slice_sizes), dtype=tf.int32
      )
      return tf.clip_by_value(
          tf.cast(start_indices, dtype=tf.int32), 0, max_start
      )
  
imports: from __future__ import annotations
  import builtins
  from collections.abc import Sequence
  import dataclasses
  from functools import partial, wraps
  import math
  import string
  from typing import Any, Callable, Optional
  from jax._src import core
  from jax import lax
  from jax._src.lax import slicing
  from jax._src import dtypes
  from jax._src import util
  from jax.experimental.jax2tf import jax2tf
  import numpy
  import tensorflow
label_desc: Use image node_id 24 for calling a global function with example usage: _clip(max_indices, start_indices, slice_sizes) and returns: tf