// CHECK-DAG: %[[cst:.*]] = arith.constant dense<[0, 2, 1, 3]> : tensor<4xi32>
  // CHECK-DAG: %[[cst_1:.*]] = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>
  // CHECK: %[[v0:.*]] = "tf.Transpose"(%arg0, %[[cst]]) : (tensor<2x5x7x3xf32>, tensor<4xi32>) -> tensor<2x7x5x3xf32>
  // CHECK: %[[v1:.*]] = "tf.Transpose"(%arg1, %[[cst_1]]) : (tensor<2x4x7x3xf32>, tensor<4xi32>) -> tensor<2x7x3x4xf32>

  %1 = "tf.Transpose"(%arg0, %0) : (tensor<1x4x4x8xf32>, tensor<4xi32>) -> tensor<1x8x4x4xf32>
  %2 = "tf.Transpose"(%1, %0) : (tensor<1x8x4x4xf32>, tensor<4xi32>) -> tensor<1x4x8x4xf32>
  %3 = "tf.Transpose"(%arg1, %0) : (tensor<1x4x4x8xf32>, tensor<4xi32>) -> tensor<1x8x4x4xf32>
  %4 = "tf.Transpose"(%3, %0) : (tensor<1x8x4x4xf32>, tensor<4xi32>) -> tensor<1x4x8x4xf32>

  %0 = "tf.AddV2"(%arg0, %arg1) {device = ""} : (tensor<1x1x2x3xf32>, tensor<2x3xf32>) -> tensor<1x1x2x3xf32>
  %1 = "tf.Transpose"(%0, %cst) {device = ""} : (tensor<1x1x2x3xf32>, tensor<4xi32>) -> tensor<1x2x1x3xf32>

  func.return %1 : tensor<1x2x1x3xf32>

  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[0, 2, 1, 3]> : tensor<4xi32>}> : () -> tensor<4xi32>

    // CHECK: tf.Const
    // CHECK-SAME: () -> tensor<4xi32>
    %0 = "tf.Const"() {value = dense<[200, 26, 26, 32]> : tensor<4xi32>} : () -> tensor<*xi32>
    // CHECK: tf.Const
    // CHECK-SAME: () -> tensor<4xi32>
    %1 = "tf.Const"() {value = dense<[200, 26, 26, 32]> : tensor<4xi32>} : () -> tensor<*xi32>
    // CHECK: tf.Add
    // CHECK-SAME: (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>

  func.return %0: tensor<2xi32>
}

// CHECK-LABEL: func @shift_left
func.func @shift_left(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>) -> tensor<4xi32> {
  // CHECK:  mhlo.shift_left %arg0, %arg1 : tensor<4xi32>
  %0 = "tf.LeftShift"(%arg0, %arg1) : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>
  func.return %0 : tensor<4xi32>
}

// CHECK-LABEL: func @div_unranked

        %cst_0 = "tf.Const"() {device = "", value = dense<[3, 4, 5, 6]> : tensor<4xi32>} : () -> tensor<4xi32>
        %1 = "tf.VarHandleOp"() {container = "", device = "/device:CPU:0", shared_name = "var_1"} : () -> tensor<!tf_type.resource<tensor<4xi32>>>
        "tf.AssignVariableOp"(%1, %cst_0) : (tensor<!tf_type.resource<tensor<4xi32>>>, tensor<4xi32>) -> ()

        return
      }
    }
  )mlir";

}

// CHECK-LABEL: conv2d_backprop_input_with_add
func.func @conv2d_backprop_input_with_add(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> {
  %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>

func.func @main(%arg0: tensor<32x4x4x128xf32>, %arg1: tensor<1x32x42x128xf32>, %arg2: tensor<4xi32>) -> tensor<1x64x84x32xf32> {
  %0 = "tfl.custom"(%arg0, %arg1, %arg2) {custom_code = "Convolution2DTransposeBias", custom_option = #tfl<const_bytes : "0x010000000200000002000000">} : (tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>, tensor<4xi32>) -> tensor<1x64x84x32xf32>
  func.return %0 : tensor<1x64x84x32xf32>
}
// CHECK-LABEL: main

func.func @main(%arg0: tensor<32x4x4x128xf32>, %arg1: tensor<1x32x42x128xf32>, %arg2: tensor<4xi32>) -> tensor<1x64x84x32xf32> {
  %0 = "tfl.custom"(%arg0, %arg1, %arg2) {custom_code = "Convolution2DTransposeBias", custom_option = #tfl<const_bytes : "0x010000000200000002000000">} : (tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>, tensor<4xi32>) -> tensor<1x64x84x32xf32>
  func.return %0 : tensor<1x64x84x32xf32>
}
// CHECK-LABEL: main

    // expected-remark@+1 {{Cost: 1}}
    %9 = "tf.StridedSlice"(%8, %5, %4, %4) {begin_mask = 0 : i64, device = "/job:localhost/replica:0/task:0/device:CPU:0", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<4xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>
    // expected-remark@+1 {{Cost: 1}}