%1 = "tfl.quantize"(%arg1) {qtype = tensor<2x1x!quant.uniform<u8:f32, 1.0:128>>}: (tensor<2x1xf32>) -> tensor<2x1x!quant.uniform<u8:f32, 1.0:128>>
  %2 = "tfl.dequantize"(%0) : (tensor<2x2x!quant.uniform<u8:f32, 1.0:128>>) -> tensor<2x2xf32>
  %3 = "tfl.dequantize"(%1) : (tensor<2x1x!quant.uniform<u8:f32, 1.0:128>>) -> tensor<2x1xf32>

// CHECK-NEXT: [[res:%.+]] = "quantfork.dcast"([[conv]]) : (tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>) -> tensor<*xf32>

)

var (
	// Black is an opaque black uniform image.
	Black = NewUniform(color.Black)
	// White is an opaque white uniform image.
	White = NewUniform(color.White)
	// Transparent is a fully transparent uniform image.
	Transparent = NewUniform(color.Transparent)
	// Opaque is a fully opaque uniform image.
	Opaque = NewUniform(color.Opaque)
)

// Uniform is an infinite-sized [Image] of uniform color.

// CHECK:           %[[VAL_3:.*]] = "tfl.reshape"(%[[VAL_2]], %[[VAL_1]]) : (tensor<?x1x1x2048x!quant.uniform<i8:f32, 6.000000e-01:-128>>, tensor<2xi32>) -> tensor<?x2048x!quant.uniform<i8:f32, 6.000000e-01:-128>>
// CHECK:           return %[[VAL_3]] : tensor<?x2048x!quant.uniform<i8:f32, 6.000000e-01:-128>>
// CHECK:         }

  %q2 = "quantfork.qcast"(%q1) {volatile} : (tensor<3x2x!quant.uniform<i8:f32, 6.000000e-03:-128>>) -> tensor<3x2x!quant.uniform<i8:f32, 0.013075299590241675:-64>>
  %dq1 = "quantfork.dcast"(%q2) : (tensor<3x2x!quant.uniform<i8:f32, 0.013075299590241675:-64>>) -> tensor<3x2xf32>

  %4 = "tfl.reshape"(%3, %cst) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>, tensor<2xi32>) -> tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>
  %5 = "tfl.softmax"(%4) {beta = 1.000000e+00 : f32} : (tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>) -> tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>>

// CHECK-SAME: {_quantization_method = "static_range_ptq { }"}
// CHECK-SAME: : (tensor<1x4x!quant.uniform<i8:f32, 6.000000e-03:-128>>, tensor<4x3x!quant.uniform<i8<-127:127>:f32:1, {5.000000e-03,5.000000e-03,5.000000e-03}>>) -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>>
// CHECK: %[[DCAST_0:.+]] = "quantfork.dcast"(%[[CALL_0]]) :  (tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>>) -> tensor<1x3xf32>
// CHECK: return

  %requant = stablehlo.uniform_quantize %conv : (tensor<1x3x4x2x!quant.uniform<i32:f32, 5.8949912267181218E-5>>) -> tensor<1x3x4x2x!quant.uniform<i8:f32, 0.045673100153605144:-62>>

    : QuantizedType<"Uniform",
                        [8, zero_pt, smantissa, sexp, -128, 127], 1>;

// General uniform quantized types. The definitions can be used to specify
// operand's tensor types.
def QI4 : QuantizedType<"Uniform", [4], 1>;
def QUI8 : QuantizedType<"Uniform", [8], 0>;
def QI8 : QuantizedType<"Uniform", [8], 1>;
def QUI16 : QuantizedType<"Uniform", [16], 0>;

func.func @remove_quantized_io(
  %arg0: tensor<1x10x!quant.uniform<i8:f32, 0.1:-128>>,
  %arg1: tensor<1x5xf32>) -> (tensor<1x10x!quant.uniform<i8:f32, 0.2:42>>, tensor<1x5xf32>) {
  %0 = "tf.MyRequantize"(%arg0) : (tensor<1x10x!quant.uniform<i8:f32, 0.1:-128>>) -> tensor<1x10x!quant.uniform<i8:f32, 0.2:42>>
  %1 = "tf.Intermediate"(%arg1) : (tensor<1x5xf32>) -> tensor<1x5xf32>
  func.return %0, %1 : tensor<1x10x!quant.uniform<i8:f32, 0.2:42>>, tensor<1x5xf32>