code: def test_fake_quantize_dense_bias():
      out_dtype = "int8"
      x = relay.var("x", shape=[128, 64], dtype="int8")
      w = relay.var("w", shape=[256, 64], dtype="int8")
      bias = relay.var("bias", shape=[256], dtype="int32")
      one = relay.const(1.0)
      zero = relay.const(0)
      w_scale = np.random.random([256]).astype("float32")
  
      op = relay.op.nn.dense(
          relay.qnn.op.dequantize(x, relay.const(2.0), zero),
          relay.qnn.op.dequantize(
              w,
              relay.const(w_scale),
              zero,
              axis=0,
          ),
          units=256,
      )
  
      op += relay.qnn.op.dequantize(
          bias,
          relay.const(2.0 * w_scale),
          zero,
      )
  
      op = relay.qnn.op.quantize(op, one, zero, out_dtype=out_dtype)
  
      x_np = np.random.randint(-128, 127, size=[128, 64], dtype="int8")
      w_np = np.random.randint(-128, 127, size=[256, 64], dtype="int8")
      bias_np = np.random.randint(-128, 127, size=[256], dtype="int32")
  
      compare_fq_to_int(
          op, [x_np, w_np, bias_np], allow_rounding_error=True
      )
  
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 7 for calling a global function with example usage: test_fake_quantize_dense_bias() without return types