code: def test_max_pool2d(
      *,
      x_k_s_p,
      ceil_mode,
      test_flags,
      backend_fw,
      fn_name,
      on_device,
  ):
      dtype, x, kernel, stride, pad, dilation, data_format = x_k_s_p
      assume(
          not (
              backend_fw == "tensorflow"
              and all(
                  stride[i] > kernel[i]
                  or (stride[i] > 1 and dilation[i] > 1)
                  for i in range(2)
              )
          )
      )
      data_format = "NCHW" if data_format == "channel_first" else "NHWC"
      assume(
          not (
              isinstance(pad, str)
              and (pad.upper() == "VALID")
              and ceil_mode
          )
      )
      # TODO: Remove this once the paddle backend supports dilation
      assume(backend_fw != "paddle" or max(list(dilation)) <= 1)
  
      helpers.test_function(
          input_dtypes=dtype,
          test_flags=test_flags,
          backend_to_test=backend_fw,
          fn_name=fn_name,
          on_device=on_device,
          rtol_=1e-2,
          atol_=1e-2,
          x=x[0],
          kernel=kernel,
          strides=stride,
          padding=pad,
          dilation=dilation,
          ceil_mode=ceil_mode,
          data_format=data_format,
      )
  
imports: import numpy
  import torch
  from hypothesis import strategies, assume
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test, BackendHandler
label_desc: Use image node_id 33 for calling a global function with example usage: test_max_pool2d() without return types