code: def _approx_top_k_batch_rule(
      batch_operands,
      batch_axes,
      *,
      k,
      reduction_dimension,
      recall_target,
      is_max_k,
      reduction_input_size_override,
      aggregate_to_topk,
  ):
      assert len(batch_operands) == 1
      assert len(batch_axes) == 1
      (operand,) = batch_operands
      (batch_axis,) = batch_axes
      dim_map = [d for d in range(operand.ndim) if d is not batch_axis]
      reduction_dimension = dim_map[reduction_dimension]
      return approx_top_k_p.bind(
          operand,
          k=k,
          reduction_dimension=reduction_dimension,
          recall_target=recall_target,
          is_max_k=is_max_k,
          reduction_input_size_override=reduction_input_size_override,
          aggregate_to_topk=aggregate_to_topk,
      ), (batch_axis, batch_axis)
  
imports: from functools import partial
  import numpy
  from jax._src import ad_util
  from jax._src import core
  from jax._src import dispatch
  from jax._src import dtypes
  from jax._src.interpreters import ad
  from jax._src.interpreters import batching
  from jax._src.interpreters import mlir
  from jax._src.interpreters import xla
  from jax._src.lax import lax
  from jax._src.lib import xla_client
  from jax._src.lib.mlir import ir
  from jax._src.lib.mlir.dialects import func
  from jax._src.lib.mlir.dialects import hlo
  from jax._src.typing import Array
label_desc: Use image node_id 9 for calling a global function with example usage: _approx_top_k_batch_rule(batch_operands, batch_axes) and returns: approx_top_k_p,  batch_axis,  batch_axis