code: def test_conv_nhwc_convert_layout():
      def before():
          x = relay.var("x", shape=(1, 64, 56, 56))
          weight = relay.var("weight", shape=(64, 64, 3, 3))
          y = relay.nn.conv2d(
              x,
              weight,
              channels=64,
              kernel_size=(3, 3),
              padding=(1, 1),
              data_layout="NCHW",
              kernel_layout="OIHW",
          )
          y = relay.nn.relu(y)
          y = relay.Function([x, weight], y)
          return y
  
      def expected():
          x = relay.var("x", shape=(1, 64, 56, 56))
          weight = relay.var("weight", shape=(64, 64, 3, 3))
          x = relay.layout_transform(x, "NCHW", "NHWC")
          weight = relay.layout_transform(weight, "OIHW", "HWIO")
          y = relay.nn.conv2d(
              x,
              weight,
              channels=64,
              kernel_size=(3, 3),
              padding=(1, 1),
              data_layout="NHWC",
              kernel_layout="HWIO",
          )
          y = relay.nn.relu(y)
          y = relay.layout_transform(y, "NHWC", "NCHW")
          y = relay.Function(relay.analysis.free_vars(y), y)
          return y
  
      a = before()
      a = run_opt_pass(
          a, transform.ConvertLayout({"nn.conv2d": ["NHWC", "default"]})
      )
      b = run_opt_pass(expected(), transform.InferType())
  
      assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
  
imports: import pytest
  import tvm
  from tvm import relay, te
  from tvm.relay import analysis, transform
  from tvm.relay.op import op
  from tvm.relay.op import register_alter_op_layout
  from tvm.relay.quantize._annotate import attach_simulated_quantize, QAnnotateKind
  from tvm.relay.transform.infer_layout_utils import InferCorrectLayoutOutput
label_desc: Use image node_id 5 for calling a global function with example usage: test_conv_nhwc_convert_layout() and returns: y, y