%0:4 = tf_executor.graph {
      %outputs:4, %control = tf_executor.island {
       "tf.TPUReplicateMetadata"() {_xla_compile_device_type = "TPU", _replication_info = "cluster", device = "device", num_replicas = 1, topology = "topology"} : () -> ()
        %1 = "tf.opA"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i1>) -> tensor<i32>

# bazel test invocation as normal.
test:nonpip_filters --test_tag_filters=-no_oss,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_aarch64,-no_oss_py38,-no_oss_py39,-no_oss_py310
test:nonpip_filters --build_tag_filters=-no_oss,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_aarch64,-no_oss_py38,-no_oss_py39,-no_oss_py310

    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

  // symbol_table caches callees in the CallGraph.
  SymbolTableCollection symbol_table;
  // List pending nodes to traverse with their root TPU cluster.
  llvm::SmallVector<std::pair<CallGraphNode*, tf_device::ClusterOp>>
      pending_call_nodes;
  // Cache the host device for each TPU cluster.
  std::unordered_map<Operation*, std::optional<std::string>> cluster_to_host;

  auto insert_pending_op = [&](Operation* op,

      "tf.TPUReplicateMetadata"() {_xla_compile_device_type = "TPU", _replication_info = "cluster", device = "device", num_replicas = 1 : i64, topology = "topology"} : () -> ()
      %0 = "tf.opA"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i1>) -> tensor<i32>

  });
  return failure(walk_result.wasInterrupted());
}

// Updates the operand of TPU embedding enqueue ops depending on whether
// the graph is in training mode or in non-training mode.
// If SendTPUEmbeddingGradients op is present, this means that graph is in
// training mode. As so, correctly feed in `then` branch value of SelectV2
// operand as inputs to the TPU embedding enqueue ops.
LogicalResult UpdateEmbeddingEnqueueOpInput(

}

// "x's type V is not a named type and T is a type parameter, and x is assignable to each specific type in T's type set."
func _[
	TP0 any,
	TP1 ~_Chan,
	TP2 ~chan int | ~chan byte,
]() {
	var (
		_ TP0 = c // ERRORx `cannot use c .* as TP0 value`
		_ TP0 = C // ERRORx `cannot use C .* as TP0 value`
		_ TP1 = c
		_ TP1 = C // ERRORx `cannot use C .* as TP1 value`

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/protobuf/tpu/compile_metadata.pb.h"
#include "tensorflow/core/tpu/kernels/tpu_compile_op_support.h"
#include "tsl/lib/core/status_test_util.h"

namespace mlir::quant::stablehlo {
namespace {

using ::testing::Test;

    // 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}}

#include "tensorflow/core/protobuf/tpu/compile_metadata.pb.h"
#include "tsl/platform/errors.h"

namespace tensorflow {
namespace tf2xla {
namespace internal {
namespace {

constexpr char kEntryFuncName[] = "main";

absl::Status SetupArguments(mlir::ModuleOp module,
                            std::vector<TensorShape>& arg_shapes,
                            tpu::TPUCompileMetadataProto& metadata_proto) {