namespace tensorflow {
namespace tf2xla {
namespace v1 {

using ::tensorflow::tpu::FunctionToHloArgs;
using ::tensorflow::tpu::GuaranteedConsts;
using ::tensorflow::tpu::MlirToHloArgs;
using ::tensorflow::tpu::ShardingAndIndex;

auto* phase2_bridge_compilation_status =
    tensorflow::monitoring::Counter<1>::New(
        "/tensorflow/core/tf2xla/api/v1/"
        "phase2_compilation_status", /*metric_name*/

    tf_executor.fetch
  }
  func.return
}
// CHECK: "tf.opA"
// device = "/TPU:0"
// CHECK: _parallel_execution_ids = "r0:0"
// CHECK: "tf.opA"
// device = "/TPU:0"
// CHECK: _parallel_execution_ids = "r0:1"
// CHECK: "tf.opA"
// device = "/TPU:1"
// CHECK: _parallel_execution_ids = "r1:0"
// CHECK: "tf.opA"
// device = "/TPU:1"

  %6 = "tf.TPUExecute"(%3#1, %1, %program#1) {_producer_name = "UNKNOWN", device = "/TPU:1"} : (tensor<1x1xf32>, tensor<1x1xf32>, tensor<3x!tf_type.string>) -> tensor<1x1xf32>

  if (!op->hasAttr(TF::kTpuReplicateAttr)) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "missing _tpu_replicate attr");
    return false;
  }
  auto opattr = op->getAttr(TF::kTpuReplicateAttr);
  if (opattr != attr) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "invalid _tpu_replicate attr.")

        if (!exported_model.ok()) return exported_model.status();

        // Remove the `tpu` tag from the debug quantized saved model as it is
        // for CPU. Note the 'tpu' value should be the same as `TPU` defined in
        // tensorflow/python/saved_model/tag_constants.py.
        if (quantization_options.has_debugger_config()) {
          tags.erase("tpu");
        }
        py_function_library.SaveExportedModel(

    mlir::ArrayAttr device_assignment_attr);

// Finds the TPU compilation device and execution devices from `devices` for a
// TPU computation subgraph. Compilation device is determined from looking up
// all TPU_SYSTEM:0 devices and choosing the CPU device associated to the first
// TPU_SYSTEM device sorted lexicographically by replica and task. Execution
// devices are determined by looking up all TPU devices associated with each

    // expected-warning @+1 {{TF2XLA TPU bridge input check: cluster op = tf.opA with cluster = cluster has successor as non cluster op tf.opB}}
    %out2, %c2 = tf_executor.island wraps "tf.opB"(%out) : (tensor<i32>) -> tensor<i32>
    // expected-error @+1 {{tf.TPUReplicatedOutput' op TF2XLA TPU bridge input check: non-cluster op = tf.opB has invalid successor op = tf.TPUReplicatedOutput}}

    arg->set_dtype(dtype);
    // TODO(lyandy): Support other arg kinds.
    if (dtype == tensorflow::DT_RESOURCE)
      arg->set_kind(tensorflow::tpu::TPUCompileMetadataProto::Arg::VARIABLE);
    else
      arg->set_kind(tensorflow::tpu::TPUCompileMetadataProto::Arg::PARAMETER);

    // Populate argument shapes.
    *arg->mutable_shape() = tensorflow::TensorShapeProto();
    if (auto ranked_tensor_type =

// TPU-annotated operations and intended to preserve backward compatibility with
// TFv1.
struct TpuV1BridgeExecutorIslandCoarsening
    : public impl::TpuV1BridgeExecutorIslandCoarseningPassBase<
          TpuV1BridgeExecutorIslandCoarsening> {
  void runOnOperation() override;
};

// Returns name of TPU cluster, if op belongs to a TPU cluster. Otherwise,
// returns `std::nullopt`.

namespace tensorflow {
namespace tf2xla {
namespace v1 {
namespace {

using ::tensorflow::monitoring::testing::CellReader;
using ::tensorflow::tpu::FunctionToHloArgs;
using ::tensorflow::tpu::MlirToHloArgs;
using ::tensorflow::tpu::ShardingAndIndex;
using ::tsl::monitoring::testing::Histogram;

static constexpr char kCompilationTimeStreamzName[] =
    "/tensorflow/core/tf2xla/api/v1/phase2_compilation_time";