%1 = "tf.Const"() {value = dense<2> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
    %2 = "tf.Identity"(%1) {device = ""} : (tensor<2x2xi32>) -> tensor<2x2xi32>
    // CHECK: SpaceToBatchND{{.*}}-> tensor<4x98x130x128xf32>
    %3 = "tf.SpaceToBatchND"(%arg0, %0, %2) {device = ""} : (tensor<1x192x256x128xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<4x?x?x128xf32>
    func.return
  }

    %8 = stablehlo.convert %7 : (tensor<1x2xi32>) -> tensor<1x2xf32>
    %9 = stablehlo.convert %2 : (tensor<2x3xi8>) -> tensor<2x3xf32>
    %10 = stablehlo.dot_general %8, %9, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    %11 = stablehlo.convert %3 : (tensor<1x3xi32>) -> tensor<1x3xf32>

  %1 = "tf.TensorListGetItem"(%0, %arg2, %arg1) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<!tf_type.variant<tensor<1x32xf32>>>, tensor<i32>, tensor<2xi32>) -> tensor<1x32xf32>
  %2 = "tf.TensorListGetItem"(%0, %arg3, %arg1) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<!tf_type.variant<tensor<1x32xf32>>>, tensor<i32>, tensor<2xi32>) -> tensor<1x32xf32>

  // converted to NHWC data format.

  // Padding in spatial dimension (NCHW)
  %0 = "tf.Const"() {value = dense<[[0, 0], [0, 0], [3, 3], [3, 3]]> : tensor<4x2xi32>} : () -> tensor<4x2xi32>

  // Reduce over spatial dimensions (NCHW)
  %1 = "tf.Const"() {value = dense<[2, 3]> : tensor<2xi32>} : () -> tensor<2xi32>

  // Transpose input: NHWC -> NCHW

  %0 = "tf.Const"() {value = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
  %1 = "tf.Const"() {value = dense<[[3, 4]]> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
  %2 = "tf.BatchToSpaceND"(%arg0, %0, %1) {device = ""} : (tensor<3x5x2xf32>, tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x8x2xf32>

  // CHECK: return [[VAL8]] : tensor<1x8x2xf32>
  func.return %2 : tensor<1x8x2xf32>
}

// -----

// Tests that a float `stablehlo.transpose` is not converted to `tfl.transpose`.

func.func @transpose_float(%arg0: tensor<2x3x4xf32>) -> tensor<4x3x2xf32> {
  %0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<2x3x4xf32>) -> tensor<4x3x2xf32>
  return %0 : tensor<4x3x2xf32>
}
// CHECK-LABEL: transpose_float
// CHECK-NOT: tfl.transpose
// CHECK: stablehlo.transpose

// -----

      %outputs_9, %control_10 = tf_executor.island(%control_4) wraps "tf.Identity"(%outputs_7) {device = ""} : (tensor<2x3xi32>) -> tensor<2x3xi32>
      tf_executor.fetch %outputs_9 : tensor<2x3xi32>
    }
    return %0 : tensor<2x3xi32>
  }
}

// CHECK: module attributes
// CHECK-SAME:  tfl.metadata = {{{.*}}keep_stablehlo_constant = "true"{{.*}}}

  // CHECK: "tf.Const"() <{value = dense<> : tensor<0x3xi32>}> : () -> tensor<0x3xi32>
  // CHECK-NOT: tf.TensorListStack
  %1 = "tf.TensorListStack"(%0, %arg0) : (tensor<!tf_type.variant<tensor<3xi32>>>, tensor<1xi32>) -> tensor<0x3xi32>
  func.return %1 : tensor<0x3xi32>
}

// -----

// CHECK-LABEL: func @tensorlistGetItem

func.func @main(%arg0: tensor<3x2xf32>) -> tensor<3x2xi32> {
  // CHECK-LABEL: @main
  // CHECK: "tfl.bucketize"(%arg0) <{boundaries = [0.000000e+00 : f32, 1.000000e+01 : f32, 1.000000e+02 : f32]}> : (tensor<3x2xf32>) -> tensor<3x2xi32>
  %0 = "tfl.bucketize"(%arg0) {boundaries = [0.0 : f32, 10.0 : f32, 100.0 : f32]} : (tensor<3x2xf32>) -> tensor<3x2xi32>
  func.return %0 : tensor<3x2xi32>

  // CHECK: %[[RES_INT_2:.*]] = mhlo.convert %[[RES_INT_1]] : tensor<3x2xi32>
  %result_int = "tf.Cast"(%result) {Truncate = false} : (tensor<3x2x!tf_type.qint32>) -> tensor<3x2xi32>
  // CHECK: return %[[RES_INT_2]] : tensor<3x2xi32>
  func.return %result_int : tensor<3x2xi32>