code: def _pre_gather_with_batch_dim(args: GatherArgs):
      """Returns True if this call to gather has non-empty batch dimensions.
  
      This is for instance triggered when doing jax.vmap(lax.dynamic_slice).
      """
      # We assume exactly one batch (and one or more non-batch dimensions).
      if len(args.batch_dims) != 1:
          raise ValueError(
              f"batch_dims is {len(args.batch_dims)} but should be 1"
          )
  
      # `start_index_map` maps indices in `start_indices` to indices in `operand`.
      # For simplicity, we currently only consider the case where this mapping is
      # the identity function, i.e., [2, 3] in `start_indices` maps to
      # `operand[2, 3]`.
      if args.dnums.start_index_map != tuple(
          range(args.start_indices_shape[-1])
      ):
          raise ValueError("unsupported start_index_map")
  
      # The batch dims in `start_indices` and `operand` should agree.
      if not core.definitely_equal(
          args.op_shape[0], args.start_indices_shape[0]
      ):
          raise ValueError(
              "Batch dimensions in operand and start_indices don't "
              "agree"
          )
  
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 30 for calling a global function with example usage: _pre_gather_with_batch_dim(args) without return types