code: def test_fake_transpose_quantize_conv_bias_add(const_bias):
      x = relay.var("x", shape=[1, 224, 224, 3], dtype="int8")
      w = relay.var("w", shape=[16, 3, 5, 5], dtype="int8")
      one = relay.const(1.0)
      zero = relay.const(0)
      if const_bias:
          bias = relay.const(np.random.random(16).astype("float32"))
      else:
          bias = relay.qnn.op.dequantize(
              relay.var("bias", shape=[16], dtype="int32"), one, zero
          )
  
      x = relay.qnn.op.dequantize(x, relay.const(2.0), zero)
      x = relay.transpose(x, [0, 3, 1, 2])
      op = relay.op.nn.conv2d(
          x,
          relay.qnn.op.dequantize(w, relay.const(0.5), zero),
          kernel_size=[5, 5],
      )
      op = relay.op.nn.bias_add(op, bias)
      op = relay.qnn.op.quantize(op, one, zero)
  
      x_np = np.random.randint(
          -128, 127, size=[1, 224, 224, 3], dtype="int8"
      )
      w_np = np.random.randint(
          -128, 127, size=[16, 3, 5, 5], dtype="int8"
      )
      bias_np = np.random.randint(
          -32768, 32767, size=[16], dtype="int32"
      )
      args = [x_np, w_np]
  
      if not const_bias:
          args.append(bias_np)
      compare_fq_to_int(op, args)
  
imports: import numpy
  import pytest
  import tvm
  import tvm.testing
  from tvm import relay
  from tvm.relay.transform import fake_quantization_to_integer
label_desc: Use image node_id 10 for calling a global function with example usage: test_fake_transpose_quantize_conv_bias_add(const_bias) without return types