func.return %0: tensor<3x2xf32>
}

func.func @sparse_f16() -> tensor<3x2xf16> {
  // CHECK-LABEL: @sparse_f16

  func.return %0: tensor<3x2xf32>
}

func.func @sparse_f16() -> tensor<3x2xf16> {
  // CHECK-LABEL: @sparse_f16

}

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-SAME: %[[ARG0:.*]]: tensor<3x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG2:.*]]: tensor<2x5xf32>
  func.func private @concatenate_and_composite(%arg0: tensor<3x2xf32>, %arg1: tensor<1x2xf32>, %arg2: tensor<2x5xf32>) -> tensor<4x5xf32> {
    // CHECK: %[[Q1:.*]] = "quantfork.qcast"(%[[ARG0]]) {volatile} : (tensor<3x2xf32>) -> tensor<3x2x!quant.uniform<i8:f32, 5.000000e-03:-1>>

  %output = "tf.StridedSlice"(%input, %begin, %end, %strides)
      : (tensor<4x8xf32>, tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<3x2xf32>
  func.return %output : tensor<3x2xf32>
  // CHECK: return %3 : tensor<3x2xf32>
}

//===----------------------------------------------------------------------===//
// Fused op legalizations.

    func.return %0, %1, %2 : tensor<3xf16>, tensor<3xf32>, tensor<3xf64>
  }

  // CHECK-LABEL: bessel_i1e
  func.func @bessel_i1e(%arg0: tensor<3xf16>, %arg1: tensor<3xf32>, %arg2: tensor<3xf64>) -> (tensor<3xf16>, tensor<3xf32>, tensor<3xf64>) {
    // CHECK-NOT: tf.BesselI1e
    %0 = "tf.BesselI1e"(%arg0) : (tensor<3xf16>) -> (tensor<3xf16>)
    %1 = "tf.BesselI1e"(%arg1) : (tensor<3xf32>) -> (tensor<3xf32>)

  %1 = "quantfork.stats"(%0) {
    layerStats = dense<[-1.0, 1.0]> : tensor<2xf32>,
    axisStats = dense<[
      [-1.0, 1.0],
      [-8.0, 8.0],
      [-0.5, 0.5]
    ]> : tensor<3x2xf32>, axis = 2 : i64
  } : (tensor<8x4x3xf32>) -> tensor<8x4x3xf32>
  func.return %1 : tensor<8x4x3xf32>

// CHECK-LABEL: main
// CHECK: return %arg0 : tensor<5x7xf32>
}

// - transpose
//
func.func @transpose_2d(%arg0: tensor<2x3xf32>) -> tensor<3x2xf32> {
  %0 = "mhlo.transpose"(%arg0) <{permutation = dense<[1, 0]> : tensor<2xi64>}> : (tensor<2x3xf32>) -> tensor<3x2xf32>
  func.return %0 : tensor<3x2xf32>

// CHECK-LABEL:   transpose_2d
// CHECK-NEXT:    %0 = "tfl.pseudo_const"() <{value = dense<[1, 0]> : tensor<2xi64>}> : () -> tensor<2xi64>

  %value0 = "tfl.pseudo_const"() {value = dense<[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]> : tensor<3x2xf32>} : () -> tensor<3x2xf32>
  %value1 = "tfl.pseudo_const"() {value = dense<[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]]> : tensor<2x2x2xf32>} : () -> tensor<2x2x2xf32>
  %lookup0 = "tfl.embedding_lookup"(%index0, %value0) : (tensor<5xi32>, tensor<3x2xf32>) -> tensor<5x2xf32>