func.func @slice_variable_start(%arg0: tensor<3x4xi32>, %arg1: tensor<2xi64>) -> tensor<1x4xi32> {
  // CHECK: %[[SLICED_START1:.*]] = "mhlo.slice"(%arg1)
  // CHECK-DAG-SAME: {limit_indices = dense<1> : tensor<1xi64>,
  // CHECK-DAG-SAME: start_indices = dense<0> : tensor<1xi64>,
  // CHECK-DAG-SAME: strides = dense<1> : tensor<1xi64>} : (tensor<2xi64>) -> tensor<1xi64>

    func.func @main(%arg0: tensor<f32>) {
      %elem = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
      %shape = "tf.Const"() <{value = dense<> : tensor<0xi64>}> : () -> tensor<0xi64>
      %zero = "tf.Fill"(%shape, %elem) : (tensor<0xi64>, tensor<f32>) -> tensor<f32>
      tf_device.replicate([%arg0, %zero] as %arg1: tensor<f32>) {n = 2 : i32} {
        %arg1_id = "tf_device.launch"() <{device = "TPU_REPLICATED_HOST_0"}> ({

  %32 = "tf.ConcatV2"(%29#0, %31, %14) {device = ""} : (tensor<2xi64>, tensor<1xi64>, tensor<i32>) -> tensor<3xi64>
  %33 = "tf.GatherV2"(%24#2, %29#1, %14) {batch_dims = 0 : i64, device = ""} : (tensor<?xi64>, tensor<?xi64>, tensor<i32>) -> tensor<?xi64>
  %34 = "tf.ConcatV2"(%33, %22, %14) {device = ""} : (tensor<?xi64>, tensor<1xi64>, tensor<i32>) -> tensor<?xi64>

  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
  %list = "tf.TensorListFromTensor"(%arg0, %cst) : (tensor<3x2x2xf32>, tensor<2xi32>) -> tensor<!tf_type.variant<tensor<f32>>>
  %t:2 = "tf.TensorListConcatV2"(%list, %cst, %lead) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<2xi32>, tensor<i64>) -> (tensor<?x2xf32>, tensor<0xi64>)
  func.return %t#0, %t#1 : tensor<?x2xf32>, tensor<0xi64>

  // CHECK-DAG: %[[X_NORMED:.+]] = mhlo.multiply %[[X]], %[[MULTIPLIER_BCAST]] : tensor<4x256xf32>
  // CHECK-DAG: %[[RHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[RHS]]) <{broadcast_dimensions = dense<1> : tensor<1xi64>}> : (tensor<256xf32>) -> tensor<4x256xf32>

    %16 = "tf.BroadcastArgs"(%11, %14) : (tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64>
    %17 = "tf.Concat"(%cst_2, %16, %12) : (tensor<i32>, tensor<1xi64>, tensor<2xi64>) -> tensor<3xi64>
    %18 = "tf.Concat"(%cst_2, %16, %15) : (tensor<i32>, tensor<1xi64>, tensor<2xi64>) -> tensor<3xi64>
    %19 = "tf.BroadcastTo"(%4, %17) : (tensor<2x?x?xi8>, tensor<3xi64>) -> tensor<2x?x?xi8>

  %end = "tf.Const"() { value = dense<[5, 10]> : tensor<2xi64> } : () -> tensor<?xi64>
  %strides = "tf.Const"() { value = dense<[2, 3, 4]> : tensor<3xi64> } : () -> tensor<?xi64>

  // expected-error @+1 {{to have the same number of elements}}
  %1 = "tf.StridedSlice"(%input, %begin, %end, %strides) : (tensor<4x8xf32>, tensor<?xi64>, tensor<?xi64>, tensor<?xi64>) -> tensor<?x?xf32>
}

    // CHECK: "mhlo.pad"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME-DAG: edge_padding_high = dense<[1, 2]> : tensor<2xi64>
    // CHECK-SAME-DAG: edge_padding_low = dense<[2, 1]> : tensor<2xi64>
    // CHECK-SAME-DAG: interior_padding = dense<[1, 0]> : tensor<2xi64>

    %0 = mhlo.constant dense<[2, 1]> : tensor<2xi32>
    %1 = mhlo.constant dense<[1, 2]> : tensor<2xi32>

  // CHECK: mhlo.reshape {{.*}} : (tensor<2x1xi64>) -> tensor<2xi64>
  // CHECK: "mhlo.slice"({{.*}}) <{limit_indices = dense<1> : tensor<1xi64>, start_indices = dense<0> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}> : (tensor<2xi64>) -> tensor<1xi64>

// CHECK-DAG:       %[[VAL_2:.*]] = "tf.Const"() <{value = dense<[1, 0]> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK-DAG:       %[[VAL_3:.*]] = "tf.Const"() <{value = dense<[1, 0]> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK:           %[[VAL_4:.*]] = "tf.Transpose"(%[[VAL_0]], %[[VAL_3]]) : (tensor<2x3xf32>, tensor<2xi64>) -> tensor<3x2xf32>