}

// -----

// CHECK-LABEL: testPad
func.func @testPad(tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<3x2xi32>):
  // CHECK: "tfl.pad"(%arg0, %arg1)
  %0 = "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>
}

// -----

// CHECK-LABEL: testPad5D

}

func.func @testPaddedDilatedConv(%arg0 : tensor<2x1920x64xf32>) ->  tensor<2x1920x128xf32> {
  %0 = "tf.Const"() {value = dense<[[0, 0], [2, 0], [0, 0]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
  %1 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
  %2 = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>

// CHECK-LABEL: func @bitcast_smaller_output_width
func.func @bitcast_smaller_output_width(%arg0: tensor<2xf32>) -> tensor<2x2xf16> {
  // CHECK:  mhlo.bitcast_convert %arg0 : (tensor<2xf32>) -> tensor<2x2xf16>
  %0 = "tf.Bitcast"(%arg0) : (tensor<2xf32>) -> tensor<2x2xf16>
  func.return %0 : tensor<2x2xf16>
}

// -----

// CHECK-LABEL: squeeze
func.func @squeeze(%arg0: tensor<1x1x10xf32>) -> tensor<1x10xf32> {

// CHECK-LABEL: @quantize_constant
// CHECK-SAME: %[[ARG0:.*]]: tensor<1x3xf32>
func.func @quantize_constant(%arg0: tensor<1x3xf32>) -> tensor<1x2xf32> {
  // CHECK-DAG: %[[QCST:.*]] = stablehlo.constant() <{value = dense<-78> : tensor<3x2xi8>}> : () -> tensor<3x2x!quant.uniform<i8<-127:127>:f32, 5.000000e-03>>
  // CHECK-DAG: %[[Q1:.*]] = stablehlo.uniform_quantize %[[ARG0]]
  // CHECK-NOT: "quantfork.qcast"
  // CHECK: %[[DOT:.*]] = stablehlo.dot %[[Q1]], %[[QCST]]

func.func @broadcast_to_i16_low_dim(%input: tensor<3xi16>, %shape: tensor<2xi32>) -> tensor<3x3xi16> {
  %0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  func.return %0: tensor<3x3xi16>

// CHECK-LABEL: broadcast_to_i16_low_dim
// CHECK:    %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
// CHECK:    return %0 : tensor<3x3xi16>
}

    func.return %0: tensor<3x4xi32>
  }

  // CHECK-LABEL: @sparse_to_dense
  // CHECK-SAME: (%[[ARG0:.*]]: tensor<3x2xi32>, %[[ARG1:.*]]: tensor<3xf32>, %[[ARG2:.*]]: tensor<f32>)
  func.func @sparse_to_dense(%arg0: tensor<3x2xi32>, %arg1: tensor<3xf32>, %arg2: tensor<f32>) -> tensor<3x3xf32> {

// CHECK-LABEL: QuantizePad
func.func @QuantizePad(tensor<2x1x3x!quant.uniform<u8:f32, 0.1>>, tensor<3x2xi32>) -> tensor<?xf32> {
^bb0(%arg0: tensor<2x1x3x!quant.uniform<u8:f32, 0.1>>, %arg1: tensor<3x2xi32>):
  %0 = "tfl.dequantize"(%arg0) : (tensor<2x1x3x!quant.uniform<u8:f32, 0.1>>) -> tensor<2x1x3xf32>
  %1 = "tfl.pad"(%0, %arg1) : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<?xf32>
  func.return %1 : tensor<?xf32>

func.func @transpose_2d_splat() -> tensor<3x2xi32> {
  %cst = arith.constant dense<0> : tensor<2x3xi32>
  %cst_perm = arith.constant dense<[1, 0]> : tensor<2xi32>

  // CHECK: %[[CST:.*]] = arith.constant dense<0> : tensor<3x2xi32>
  // CHECK: return %[[CST]]
  %0 = "tfl.transpose"(%cst, %cst_perm) : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>
  func.return %0 : tensor<3x2xi32>
}

// CHECK-LABEL: @transpose_2d_identity

  // CHECK:  %cst = arith.constant dense<0> : tensor<1xi32>
  // CHECK:  %4 = "tfl.reduce_min"(%arg0, %cst) <{keep_dims = false}> : (tensor<2xi16>, tensor<1xi32>) -> tensor<i16>
  // CHECK:  %5 = "tfl.arg_min"(%arg0, %cst) : (tensor<2xi16>, tensor<1xi32>) -> tensor<i32>
  // CHECK:  return %4, %5 : tensor<i16>, tensor<i32>
}


// CHECK-LABEL: func @convert_argmin_constant

  %0 = "vhlo.reduce_window_v1"(%arg0, %arg1) <{base_dilations = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>,
        padding = #vhlo.tensor_v1<dense<[[0, 0], [159, 0], [0, 0]]> : tensor<3x2xi64>>,
        window_dilations = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>,
        window_dimensions = #vhlo.tensor_v1<dense<[1, 160, 1]> : tensor<3xi64>>,
        window_strides = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>}> ({