#ifndef TENSORFLOW_COMPILER_JIT_RESOURCE_OPERATION_SAFETY_ANALYSIS_H_
#define TENSORFLOW_COMPILER_JIT_RESOURCE_OPERATION_SAFETY_ANALYSIS_H_

#include "xla/service/graphcycles/graphcycles.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/graph/graph.h"

namespace tensorflow {
// An XLA cluster hoists all resource reads to be beginning of the cluster
// execution and all the resource writes to the end.  This means it cannot

#define TENSORFLOW_COMPILER_MLIR_TF2XLA_MLIR_BRIDGE_ROLLOUT_POLICY_H_

#include <optional>

#include "mlir/IR/BuiltinOps.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/protobuf/config.pb.h"

namespace tensorflow {

enum class MlirBridgeRolloutPolicy {
  // The MLIR bridge is explicitly disabled by the user and must not be run.
  kDisabledByUser = 0,

#define TENSORFLOW_C_EXPERIMENTAL_NEXT_PLUGGABLE_DEVICE_TENSOR_PJRT_BUFFER_UTIL_H_

#include "xla/pjrt/c/pjrt_c_api.h"
#include "xla/pjrt/pjrt_c_api_client.h"
#include "tensorflow/core/framework/tensor.h"

namespace tensorflow {

absl::StatusOr<PJRT_Buffer*> GetPjRtCBufferFromTensor(const Tensor* tensor);

absl::Status SetPjRtCBufferToTensor(PJRT_Buffer* c_buffer,

#include "absl/types/optional.h"
#include "tensorflow/compiler/jit/xla_activity.pb.h"
#include "xla/service/graphcycles/graphcycles.h"
#include "xla/statusor.h"
#include "tensorflow/core/common_runtime/optimization_registry.h"
#include "tensorflow/core/graph/algorithm.h"

namespace tensorflow {

// The attribute that marks nodes to be grouped into functions by the
// encapsulate subgraphs pass.
extern const char* const kXlaClusterAttr;

#define TENSORFLOW_COMPILER_MLIR_TF2XLA_INTERNAL_MLIR_BRIDGE_PASS_UTIL_H_

#include <optional>

#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "tensorflow/core/framework/function.h"

namespace tensorflow {

// Checks if a graph or reachable functions in the library have any
// StatefulPartitionedOps with _XlaMustCompile=true. The function library will

#define TENSORFLOW_COMPILER_MLIR_TF2XLA_API_V2_TF_DIALECT_TO_EXECUTOR_H_

#include "llvm/ADT/StringRef.h"
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace tf2xla {
namespace v2 {

// Given the input Module op that's in the Tensorflow Dialect, convert the MLIR

#include "absl/strings/string_view.h"
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "tensorflow/core/public/session.h"

namespace tensorflow {
namespace quantization {

// Default MLIR dump file prefix for TensorFlow quantization passes.
inline constexpr absl::string_view kDefaultTfQuantMlirDumpFilePrefix =

==============================================================================*/

#ifndef TENSORFLOW_C_PYTHON_API_H_
#define TENSORFLOW_C_PYTHON_API_H_

#include <string>

#include "tensorflow/c/c_api.h"
#include "tensorflow/core/framework/full_type.pb.h"

// These functions can be removed without notice. They exist to facilitate some
// refactoring of graph construction code in the Python API.

namespace tensorflow {

#ifndef TENSORFLOW_COMPILER_AOT_EMBEDDED_PROTOCOL_BUFFERS_H_
#define TENSORFLOW_COMPILER_AOT_EMBEDDED_PROTOCOL_BUFFERS_H_

#include "absl/types/span.h"
#include "xla/statusor.h"
#include "tensorflow/core/platform/protobuf.h"

namespace tensorflow {
namespace tfcompile {
using absl::StatusOr;

// Represents a set of protocol buffers embedded into an object file and
// describes how to access them at runtime.

      entry->compilation_status = *compilation_status;
    }
    if (compilation_result.has_value()) {
      entry->compilation_result = std::move(*compilation_result);
    }
    if (executable.has_value()) {
      entry->executable = std::move(*executable);
    }
  }

  VLOG(4) << "Added/updated cache entry: key=" << key.HumanString()
          << ", entry=" << entry->DebugString();
}