%0 = "tfl.pseudo_const" () {value = dense<[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]> : tensor<3x2xf32>} : () -> tensor<3x2xf32>
  %1 = "tfl.sub" (%0, %arg0) {fused_activation_function = "NONE"} : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
  func.return %1 : tensor<3x2xf32>
}
}

// CHECK:      {
// CHECK:        subgraphs: [ {

func.func @bitcastI32ToI16(%arg0: tensor<8xi32>) -> tensor<8x2xi16> {
  %0 = "tf.Bitcast"(%arg0) : (tensor<8xi32>) -> tensor<8x2xi16>
  func.return %0 : tensor<8x2xi16>

// CHECK-LABEL: bitcastI32ToI16
// CHECK: %[[RES0:.*]] = "tfl.bitcast"(%arg0) : (tensor<8xi32>) -> tensor<8x2xi16>
// CHECK:  return %[[RES0]] : tensor<8x2xi16>
}

func.func @bitcastI16ToUI32(%arg0: tensor<8x2xi16>) -> tensor<8xui32> {

// CHECK:         } ]
// CHECK:       }
  %0 = "tfl.pseudo_const" () {value = dense<[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]> : tensor<3x2xf32>} : () -> tensor<3x2xf32>
  %1 = "tfl.add" (%0, %arg0) {fused_activation_function = "NONE"} : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
  func.return %1 : tensor<3x2xf32>
}

  %dq = "quantfork.dcast"(%q) : (tensor<3x2x!quant.uniform<i8:f32, 0.013075299590241675:-64>>) -> tensor<3x2xf32>
  func.return %dq : tensor<3x2xf32>
}

// -----

// CHECK-LABEL: @remove_volatile_qdq_with_requantization
// CHECK-SAME: %[[ARG0:.*]]: tensor<3x2xf32>
func.func @remove_volatile_qdq_with_requantization(%arg0: tensor<3x2xf32>) -> tensor<3x2xf32> {
  // CHECK: %[[Q1:.*]] = stablehlo.uniform_quantize %[[ARG0]]

// CHECK-NEXT:    buffer: 3
// CHECK-NEXT:    } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:  }

  %0 = "tf.AddV2"(%arg0, %arg0) : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
  func.return %0 : tensor<3x2xf32>

  // CHECK-DAG:  %[[CST_1:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf16>}> : () -> tensor<4x4xf16>
  // CHECK-DAG:  %[[DQ_0:.*]] = "tfl.dequantize"(%[[CST_0]]) : (tensor<4x4xf16>) -> tensor<4x4xf32>
  // CHECK-DAG:  %[[DQ_1:.*]] = "tfl.dequantize"(%[[CST_1]]) : (tensor<4x4xf16>) -> tensor<4x4xf32>

// -----

// CHECK-LABEL: transpose_simple_2d
func.func @transpose_simple_2d() -> tensor<3x2xf32> {
  %0 = stablehlo.constant dense<[[0.000000e+0, 1.000000e+0, 2.000000e+0], [3.000000e+0, 4.000000e+0, 5.000000e+0]]> : tensor<2x3xf32>
  %1 = stablehlo.transpose %0, dims = [1, 0] : (tensor<2x3xf32>) -> tensor<3x2xf32>
  return %1 : tensor<3x2xf32>
}

}

func.func @RemoveTrivialAddBf16RHS(%arg0: tensor<2x2xbf16>) -> tensor<2x2xbf16> {
  %cst = arith.constant dense<0.0> : tensor<2x2xbf16>
  %0 = "tf.Add"(%arg0, %cst) : (tensor<2x2xbf16>, tensor<2x2xbf16>) -> tensor<2x2xbf16>
  func.return %0 : tensor<2x2xbf16>

  // CHECK-LABEL: RemoveTrivialAdd
  // CHECK-NEXT: return %arg0 : tensor<2x2xbf16>
}

  module {
    func.func @constant_add() -> (tensor<3x2xf32>) {
      %cst1 = stablehlo.constant dense<2.4> : tensor<3x2xf32>
      %cst2 = stablehlo.constant dense<5.7> : tensor<3x2xf32>
      %add = stablehlo.add %cst1, %cst2 : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
      func.return %add : tensor<3x2xf32>
    }
  }
)mlir";

  // CHECK-NEXT: }
  // CHECK-EMPTY:

  %0 = "tfl.pseudo_const" () {value = dense<[[-5.0, 10000.0], [150.0, 10.0], [5.0, 100.0]]> : tensor<3x2xf32>} : () -> tensor<3x2xf32> loc("Const")
  %1 = "tfl.bucketize"(%0) {boundaries = [0.0 : f32, 10.0 : f32, 100.0 : f32]} : (tensor<3x2xf32>) -> tensor<3x2xi32> loc("bucketize")
  func.return %1 : tensor<3x2xi32>