code: def test_pad_qconv2d_no_fold():
      def get_expr():
          x = relay.var("x", shape=(1, 1, 2, 2), dtype="int8")
          weight = relay.var("weight", shape=(1, 1, 2, 2), dtype="int8")
          # Pad value and input zp are not equal
          pad_value = 1
          input_zero_point = 0
          pad = relay.nn.pad(
              x, [[0, 0], [0, 0], [1, 1], [1, 1]], pad_value=pad_value
          )
          return relay.qnn.op.conv2d(
              pad,
              weight,
              relay.const(input_zero_point, "int32"),
              relay.const(0, "int32"),
              relay.const(1, "float32"),
              relay.const(1, "float32"),
              channels=1,
              kernel_size=(2, 2),
              padding=(0, 0),
          )
  
      a = run_opt_pass(
          get_expr(), relay.transform.FoldExplicitPadding()
      )
      b = run_opt_pass(get_expr(), transform.InferType())
  
      assert tvm.ir.structural_equal(a, b, map_free_vars=True), (
          "\nActual = \n" + str(a) + "\nExpected = \n" + str(b)
      )
  
file_path: tvm/tests/python/relay/test_pass_fold_explicit_padding.py
imports: import tvm
  from tvm import relay
  from tvm.relay import transform
  from tvm.relay.testing import run_opt_pass
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: test_pad_qconv2d_no_fold() and returns: relay