code: def test_const_folding():
      data_shape = (2, 4, 2, 4)
      data_dtype = "uint8"
      kernel_shape = (4, 3, 2, 2)
      kernel_dtype = "uint8"
  
      golden_weight = np.random.randint(
          low=0, high=255, size=kernel_shape
      ).astype(kernel_dtype)
      data = relay.var("data", shape=data_shape, dtype=data_dtype)
      kernel = relay.const(golden_weight)
      qnn_func = get_qnn_func(
          data,
          kernel,
          input_zero_point=8,
          kernel_zero_point=3,
          kernel_size=(2, 2),
          input_scale=1.0,
          kernel_scale=1.0,
          padding=(0, 0),
          strides=(1, 1),
          dilation=(1, 1),
          data_layout="NCHW",
          kernel_layout="IOHW",
          out_dtype="int32",
          channels=kernel_shape[1],
          groups=1,
      )
      folded_mod = transform.FoldConstant()(qnn_func)
      folded_func = folded_mod["main"]
      assert "reshape" not in folded_func.astext()
  
imports: import numpy
  import tvm
  from tvm import relay, te
  from tvm.contrib import graph_executor
  from tvm.relay import transform
  from tvm.relay.testing import run_infer_type
  from tvm.relay.testing.temp_op_attr import TempOpAttr
label_desc: Use image node_id 12 for calling a global function with example usage: test_const_folding() without return types