%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>

    %8 = "mhlo.compare"(%arg1, %arg1) {comparison_direction = #mhlo<comparison_direction NE>} : (tensor<f32>, tensor<f32>) -> tensor<i1>
    %9 = mhlo.or %7, %8 : tensor<i1>
    %10 = "mhlo.select"(%9, %arg1, %arg3) : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>
    %11 = "mhlo.compare"(%arg1, %arg3) {comparison_direction = #mhlo<comparison_direction EQ>} : (tensor<f32>, tensor<f32>) -> tensor<i1>

  %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: %[[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, {{.*}}>>

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

    %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>>
}

// CHECK: %[[id2:.*]] = "tf.RiscSame"(%arg2) : (tensor<f32>) -> tensor<*xf32>
// CHECK: %[[es:.*]] = "tf.EnsureShape"(%[[id2]]) <{shape = #tf_type.shape<>}> : (tensor<*xf32>) -> tensor<f32>
// CHECK: return %[[es]] : tensor<f32>
}

// CHECK-LABEL: decompose_tf_concat_n
func.func @decompose_tf_concat_n(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<3xf32> {
  %0 = "tfr.cast"(%arg0) : (tensor<f32>) -> !tfr.tensor