min = fakequant_op.getODSOperands(1);
  max = fakequant_op.getODSOperands(2);
  {
    auto target_attr = op->getAttrOfType<IntegerAttr>("num_bits");
    if (!target_attr)
      target_attr = rewriter.getIntegerAttr(rewriter.getIntegerType(64), 8);
    num_bits = target_attr;
  }
  {
    auto target_attr = op->getAttrOfType<BoolAttr>("narrow_range");

    weight = array_ops.fake_quant_with_min_max_args(
        weight, min=-0.1, max=0.2, num_bits=8, narrow_range=False
    )

    # shape: (2, 2)
    output_tensor = math_ops.matmul(matmul_input, weight)
    # Insert fake quant to simulate a QAT model.
    output_tensor = array_ops.fake_quant_with_min_max_args(
        output_tensor, min=-0.2, max=0.2, num_bits=8, narrow_range=False
    )

    %7 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg1, %arg2, %arg3) {device = "", narrow_range = false, num_bits = 8 : i64} : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    "tfl.yield"(%7) : (tensor<*xf32>) -> ()
  }) {device = "", narrow_range = false, num_bits = 8 : i64} : (tensor<3x4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<3x4xf32>

    auto element_ty = input_ty.getElementType();
    auto scalar_ty = tensorflow::GetTypeFromTFTensorShape({}, element_ty);

    auto num_bits = op.getNumBits();
    auto narrow_range = op.getNarrowRange();
    const double bits_min = narrow_range ? 1 : 0;
    const double bits_max = (1 << num_bits) - 1;

    auto float_min = op.getMin();
    auto float_max = op.getMax();