code: def _approx_top_k_tpu_translation(
      ctx,
      avals_in,
      avals_out,
      operand,
      *,
      k,
      reduction_dimension,
      recall_target,
      is_max_k,
      reduction_input_size_override,
      aggregate_to_topk,
  ):
      c = ctx.builder
      op_shape = c.get_shape(operand)
      if not op_shape.is_array():
          raise ValueError(
              f"operand must be an array, but was {op_shape}"
          )
      op_dims = op_shape.dimensions()
      op_type = op_shape.element_type()
      if reduction_dimension < 0:
          reduction_dimension = len(op_dims) + reduction_dimension
      comparator = _comparator_builder(op_type, is_max_k)
      init_val_literal = _get_init_val_literal(op_type, is_max_k)
      iota = xc.ops.Iota(
          c,
          xc.Shape.array_shape(np.dtype(np.int32), op_dims),
          reduction_dimension,
      )
      init_val = xc.ops.Constant(c, init_val_literal)
      init_arg = xc.ops.Constant(c, np.int32(-1))
      out = xc.ops.ApproxTopK(
          c,
          [operand, iota],
          [init_val, init_arg],
          k,
          reduction_dimension,
          comparator,
          recall_target,
          aggregate_to_topk,
          reduction_input_size_override,
      )
      return xla.xla_destructure(c, out)
  
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 6 for calling a global function with example usage: _approx_top_k_tpu_translation(ctx, avals_in, avals_out, operand) and returns: xla