func.return %0#2, %0#3, %0#4 : tensor<i32>, tensor<256x256xf32>, tensor<?x256x256xf32>
}

    %2 = "quantfork.stats"(%1) {layerStats = dense<[5.00000000e-6, 7.00000000e-1]> : tensor<2xf32>} : (tensor<1x3xf32>) -> tensor<1x3xf32>
    return %2 : tensor<1x3xf32>
  }
// CHECK: func.func private @quantize_dot_general_with_bias_same_shape_fn(%[[ARG_0:.+]]: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"}

  %3 = "tf.Transpose"(%arg1, %0) : (tensor<1x4x4x8xf32>, tensor<4xi32>) -> tensor<1x8x4x4xf32>
  %4 = "tf.Transpose"(%3, %0) : (tensor<1x8x4x4xf32>, tensor<4xi32>) -> tensor<1x4x8x4xf32>
  %5 = "tf.AddV2"(%2, %4) : (tensor<1x4x8x4xf32>, tensor<1x4x8x4xf32>) -> tensor<1x4x8x4xf32>

  func.return %5 : tensor<1x4x8x4xf32>
}

// CHECK-LABEL: func @fold_into_max_pool

  %1 = "tf.Invert"(%0) : (tensor<i32>) -> tensor<i32>
  %2 = "tf.Invert"(%1) : (tensor<i32>) -> tensor<i32>
  // CHECK: return [[INVERT]]
  func.return %2: tensor<i32>
}

// CHECK-LABEL: func @testSingleNeg
// CHECK-SAME:  ([[ARG0:%.+]]: tensor<i32>)
func.func @testSingleNeg(%arg0: tensor<i32>) -> tensor<i32> {
  // CHECK: [[NEG:%.+]] = "tf.Neg"([[ARG0]])
  %0 = "tf.Neg"(%arg0) : (tensor<i32>) -> tensor<i32>

^bb0(%arg0: tensor<1x2xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>):
  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>

  %res = "tf.Relu6"(%biasadd) : (tensor<*xf32>) -> tensor<*xf32>
  %q_res = "quantfork.qcast"(%res) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>
  %dq_res = "quantfork.dcast"(%q_res) : (tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>) -> tensor<*xf32>

  func.return %dq_res : tensor<*xf32>
}

^bb0(%arg0: tensor<1x2xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>):
  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>

  %6 = "tfl.quantize"(%5) {qtype = tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>} : (tensor<1x112x112x32xf32>) -> tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>
  func.return %6 : tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>

func.func @decompose_fused_n(%arg0: tensor<1x2x3x4x!tf_type.string>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> {
  %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

  %arg2 :tensor<?x2xi32>, %arg3: tensor<?xi32>, %arg4: tensor<?xi32>, %arg5: tensor<?xi32>,
  %arg6: tensor<!tf_type.string>, %arg7: tensor<!tf_type.string>, %arg8: tensor<i1>) -> () {
  %0 = "tf.Const"() {value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
  %1 = "tf.SelectV2"(%arg8, %arg6, %arg7) : (tensor<i1>, tensor<!tf_type.string>, tensor<!tf_type.string>) -> tensor<!tf_type.string>