code: def test_conv2d_inceptionv3_nchw_3c(
      remote, target, executor_type, dtype
  ):
      input_shape = (1, 3, 299, 299)
      filter_shape = (64, 3, 3, 3)
      bias_shape = (1, 64, 1, 1)
      A = relay.var("data", shape=input_shape, dtype=dtype)
      B = relay.var("weight", shape=filter_shape, dtype=dtype)
      bias = relay.var("bias", shape=bias_shape, dtype=dtype)
  
      # C = relay.nn.relu(A)
      conv = relay.nn.conv2d(
          A,
          B,
          data_layout="NCHW",
          kernel_layout="OIHW",
          padding=[0, 0, 0, 0],
          strides=[2, 2],
          out_dtype=dtype,
          channels=64,
          kernel_size=(3, 3),
      )
      D = relay.op.add(conv, bias)
      D = relay.op.nn.relu(D)
  
      mod = relay.Function([A, B, bias], D)
      np.random.seed(0)
      initializer = relay.testing.init.Xavier()
      filter_data = np.zeros(filter_shape).astype(dtype)
      bias_data = np.zeros(bias_shape).astype(dtype)
      initializer("weight", filter_data)
      initializer("bias", bias_data)
      params1 = {
          "weight": tvm.nd.array(filter_data),
          "bias": tvm.nd.array(bias_data),
      }
  
      if executor_type == "ge":
          build_run_compare(
              remote,
              mod,
              params1,
              {"data": input_shape},
              {"data": dtype},
              target,
          )
      else:
          build_run_compare_vm(
              remote,
              mod,
              params1,
              {"data": input_shape},
              {"data": dtype},
              target,
          )
  
imports: import re
  import tvm
  import numpy
  from tvm import relay
  from tvm.relay import testing
  from tvm.contrib import utils
  from utils.adreno_utils import gpu_preprocess, build_run_compare, build_run_compare_vm
  import pytest
label_desc: Use image node_id 5 for calling a global function with example usage: test_conv2d_inceptionv3_nchw_3c(remote, target, executor_type, dtype) without return types