code: def test_fq_qat_positive_nothing_to_do():
      # All operations are converted by the non-QAT pass.
      shape_x = [1, 4, 2]
      shape_w = [1, 4, 2]
      a = relay.var("a", shape=shape_x, dtype="int8")
      b = relay.var("b", shape=shape_w, dtype="int8")
  
      op0 = relay.qnn.op.dequantize(a, relay.const(2.0), relay.const(0))
      op1 = relay.qnn.op.dequantize(b, relay.const(6.0), relay.const(0))
      op2 = relay.op.nn.batch_matmul(op0, op1)
      op3 = relay.op.add(op2, relay.const(1.0))
      expr = relay.qnn.op.quantize(
          op3, relay.const(1.0), relay.const(0), out_dtype="int8"
      )
  
      op0 = relay.qnn.op.batch_matmul(
          a,
          b,
          relay.const(0),
          relay.const(0),
          relay.const(2.0),
          relay.const(6.0),
      )
      op1 = relay.qnn.op.quantize(
          relay.const(1.0),
          relay.const(12.0),
          relay.const(0),
          out_dtype="int32",
      )
      op2 = relay.op.add(
          op0,
          op1,
      )
      expected_expr = relay.qnn.op.requantize(
          op2,
          relay.const(12.0),
          relay.const(0),
          relay.const(1.0),
          relay.const(0),
          out_dtype="int8",
      )
  
      x_np = np.random.randint(-128, 127, size=shape_x, dtype="int8")
      w_np = np.random.randint(-128, 127, size=shape_w, dtype="int8")
      compare_expected_fq_qat_to_int(expr, expected_expr, [x_np, w_np])
  
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 47 for calling a global function with example usage: test_fq_qat_positive_nothing_to_do() without return types