// CHECK: "tf.ReadVariableOp"(%[[OA1]])
  %0 = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource<tensor<f32>>>) -> tensor<f32>
  %1 = "tf.AddV2"(%0, %arg0) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %2 = "tf.AddV2"(%1, %arg2) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  func.return %2 : tensor<f32>
}

  %input_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
  %bias_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
  %bias_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
  %output_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>

	}
	if u64 != ju64 {
		panic("u64 != ju64")
	}
	if u128 != iu128 {
		panic("u128 != iu128")
	}
	if u128 != ju128 {
		panic("u128 != ju128")
	}
	if f32 != if32 {
		panic("f32 != if32")
	}
	if f32 != jf32 {
		panic("f32 != jf32")
	}
	if f64 != if64 {
		panic("f64 != if64")
	}
	if f64 != jf64 {
		panic("f64 != jf64")
	}
	if c128 != ic128 {
		panic("c128 != ic128")
	}

  %0:2 = "tf.FusedN"(%arg0, %arg1, %arg2) {A=0:index} : (tensor<1x2x3x4x!tf_type.string>, tensor<f32>, tensor<f32>) -> (tensor<1x2x3x4x!tf_type.string>, tensor<f32>)
  func.return %0#1 : tensor<f32>

// CHECK-NEXT: %[[in0:.*]] = "tfr.cast"(%arg0) : (tensor<1x2x3x4x!tf_type.string>) -> !tfr.tensor
// CHECK-NEXT: %[[in1:.*]] = "tfr.cast"(%arg1) : (tensor<f32>) -> !tfr.tensor

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

// CHECK-NEXT: %[[dq2:.*]] = "tfl.dequantize"(%[[q2]]) : (tensor<1x3x!quant.uniform<i8<-127:127>:f32:0, {{.*}}>>) -> tensor<1x3xf32>
// CHECK-NEXT: %[[q3:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<1x3x!quant.uniform<i16:f32, {{.*}}>>}> {volatile} : (tensor<1x3xf32>) -> tensor<1x3x!quant.uniform<i16:f32, {{.*}}>>

    %0 = "tf.Sum"(%output, %cst_0) <{keep_dims = false}> {device = ""} : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<f32>
    %1 = "tf.Greater"(%0, %cst) {device = ""} : (tensor<f32>, tensor<f32>) -> tensor<i1>

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<255.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %rst = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 5, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %rst : tensor<8xf32>

  func.return %2 : tensor<1x32x32x16x!quant.uniform<i8:f32, 1.0>>
}

// -----

// CHECK-LABEL: @test_conv2d_qi16
func.func @test_conv2d_qi16(%arg0: tensor<1x32x32x8x!quant.uniform<i16:f32, 1.0>>) -> tensor<1x32x32x16x!quant.uniform<i16:f32, 1.0>> {

   %0 = "tfl.pseudo_const"() {value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
   %1 = "tfl.mean"(%arg0, %0) {keep_dims = false} : (tensor<?x7x7x2048x!quant.uniform<i8:f32, 0.6:-128>>, tensor<2xi32>) -> tensor<?x2048x!quant.uniform<i8:f32, 0.6:-128>>
  func.return %1 : tensor<?x2048x!quant.uniform<i8:f32, 0.6:-128>>
}