code: def make_ethosu_pooling(
      ifm,
      pooling_type,
      pool_shape,
      ofm_channels,
      ofm_dtype,
      strides,
      padding,
      activation="NONE",
      ifm_layout="NHWC",
      ofm_layout="NHWC",
      rounding_mode="TFL",
      upscale="NONE",
  ):
      pooling = ethosu_ops.ethosu_pooling(
          ifm,
          lut=relay.const([], dtype="int8"),
          pooling_type=pooling_type,
          ifm_scale=1,
          ifm_zero_point=0,
          ofm_scale=1,
          ofm_zero_point=0,
          pool_shape=pool_shape,
          ofm_channels=ofm_channels,
          ofm_dtype=ofm_dtype,
          strides=strides,
          padding=padding,
          activation=activation,
          clip_min=10 if activation == "CLIP" else 0,
          clip_max=100 if activation == "CLIP" else 0,
          rounding_mode=rounding_mode,
          upscale=upscale,
          ifm_layout=ifm_layout,
          ofm_layout=ofm_layout,
      )
      return pooling
  
imports: from typing import List
  import os
  import struct
  import numpy
  import math
  from enum import IntEnum
  import tensorflow
  from ethosu.vela.register_command_stream_generator import CmdMode
  from ethosu.vela.register_command_stream_generator import cmd0
  from ethosu.vela.register_command_stream_generator import cmd1
  import tvm
  from tvm import relay
  import tvm.relay.backend.contrib.ethosu.op
  from tvm.topi.nn.utils import get_pad_tuple
  from tvm.relay.expr_functor import ExprMutator
  from tvm.relay.op.annotation import compiler_begin, compiler_end
  from tvm.relay.backend.contrib.ethosu import preprocess
  import tvm.relay.testing.tf
  from tvm import WorkspaceMemoryPools, WorkspacePoolInfo, PoolInfoProperties
  from tvm.relay.op.contrib.ethosu import partition_for_ethosu
  from tvm.testing.aot import AOTCompiledTestModel, AOTDataLinkage, AOTTestModel, AOTTestRunner, compile_models, run_and_check
label_desc: Use image node_id 21 for calling a global function with example usage: make_ethosu_pooling(ifm, pooling_type, pool_shape, ofm_channels, ofm_dtype, strides, padding, activation, ifm_layout, ofm_layout, rounding_mode, upscale) and returns: pooling