%group_assignment = "tf.Const"() { value = dense<[[0],[1]]> : tensor<2x1xi32> } : () -> tensor<2x1xi32>
  // CHECK: "mhlo.all_reduce"
  // CHECK{LITERAL}: replica_groups = dense<[[0], [1]]> : tensor<2x1xi64>
  // CHECK-NOT: channel_handle
  // CHECK: mhlo.add
  %0 = "tf.XlaAllReduce"(%input, %group_assignment) {reduce_op = "Add", mode = "CrossReplica"} : (tensor<f32>, tensor<2x1xi32>) -> tensor<f32>
  func.return %0 : tensor<f32>
}

// -----

    %11 = "tf.Pad"(%arg0, %10) : (tensor<2x224x224x3xf32>, tensor<4x2xi32>) -> tensor<2x230x230x3xf32>
    %12 = "tf.Cast"(%arg1) {Truncate = false} : (tensor<2x1xf32>) -> tensor<2x1xi64>
    %13 = "tf.Reshape"(%12, %9) : (tensor<2x1xi64>, tensor<1xi32>) -> tensor<2xi64>
    %14 = "tf.Squeeze"(%arg1) {squeeze_dims = [-1]} : (tensor<2x1xf32>) -> tensor<2xf32>
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: strides = [1, 1, 1, 1]

  %28 = "tf.Minimum"(%26, %27) {device = ""} : (tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64>
  %29:2 = "tf.RaggedRange"(%28, %27, %13) {T = i64, Tsplits = i64, device = ""} : (tensor<1xi64>, tensor<1xi64>, tensor<i64>) -> (tensor<2xi64>, tensor<?xi64>)

  func.func @xla_spmd_full_to_shard_shape(%arg0: tensor<2x2xi64>) -> (tensor<1x2xi64>) {
    // CHECK: %[[SHARDING:.*]] = mhlo.custom_call @Sharding(%arg0) {backend_config = "", mhlo.sharding = "{devices=[2,1]0,1}"} : (tensor<2x2xi64>) -> tensor<2x2xi64>
    // CHECK: %[[MANUAL:.*]] = mhlo.custom_call @SPMDFullToShardShape(%[[SHARDING]]) {backend_config = "", mhlo.sharding = "{manual}"} : (tensor<2x2xi64>) -> tensor<1x2xi64>
    // CHECK: return %[[MANUAL]]

func.func @dynamic_update_slice(%arg0: tensor<4x4xi64>, %arg1: tensor<2x3xi64>, %arg2: tensor<i64>, %arg3: tensor<i64>) -> tensor<4x4xi64> {
  %0 = "vhlo.dynamic_update_slice_v1"(%arg0, %arg1, %arg2, %arg3) : (tensor<4x4xi64>, tensor<2x3xi64>, tensor<i64>, tensor<i64>) -> tensor<4x4xi64>
  return %0 : tensor<4x4xi64>
}

  // CHECK: "mhlo.transpose"({{.*}}) <{permutation = dense<[1, 0]> : tensor<2xi64>}> : (tensor<1x2xi64>) -> tensor<2x1xi64>
  // 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>

func.func @space_to_batch_nd_element_type_f16(%input: tensor<3x5x7x10xf16>, %block_shape: tensor<2xi64>, %paddings: tensor<2x2xi64>) -> tensor<?x?x?x10xf16> {
  // CHECK-DAG: "tf.PadV2"(%arg0, {{.*}}, {{.*}}) {{.*}} -> tensor<3x?x?x10xf16>
  // CHECK-DAG: return {{.*}}: tensor<?x?x?x10xf16>
  %0 = "tf.SpaceToBatchND"(%input, %block_shape, %paddings) : (tensor<3x5x7x10xf16>, tensor<2xi64>, tensor<2x2xi64>) -> tensor<?x?x?x10xf16>
  func.return %0 : tensor<?x?x?x10xf16>
}

        # CHECK: value = dense<{{\[\[}}1, 3, 2], [5, 4, 7]]> : tensor<2x3xi64>
      }
    }
  }
}
node {
  name: "z"
  op: "Const"
  device: "/device:CPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {

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>

        %1 = "tf.OpB"() {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\02"} : () -> tensor<2x2xi64>
        // expected-error @+1 {{should have the same sharding}}
        %2 = "tf.OpC"(%0, %1) {_xla_map_outside_compilation = "0", _xla_outside_compilation = "from_launch"} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>
        "tf.OpD"(%2) : (tensor<2x2xi64>) -> ()
        tf_device.return