// Implementations are free to override it with something more informative.
  virtual std::string DebugString() const;

  AbstractTensorHandleKind getKind() const { return kind_; }

 private:
  const AbstractTensorHandleKind kind_;
};

namespace internal {
struct AbstractTensorHandleDeleter {
  void operator()(AbstractTensorHandle* p) const {
    if (p != nullptr) {
      p->Unref();
    }
  }

  void Run(TF_Status* s);

  void CloseAndDelete(TF_Status* s);

  TF_Tensor* output_tensor(int i) { return output_values_[i]; }

  TF_Session* mutable_session() { return session_; }

 private:
  void DeleteInputValues();
  void ResetOutputValues();

  TF_Session* session_;
  std::vector<TF_Output> inputs_;
  std::vector<TF_Tensor*> input_values_;
  std::vector<TF_Output> outputs_;

    hdrs = ["tfe_executor_internal.h"],
    visibility = ["//visibility:private"],
    deps = [
        "//tensorflow/core/common_runtime/eager:eager_executor",
    ],
)

cc_library(
    name = "tfe_monitoring_internal",
    hdrs = ["tfe_monitoring_internal.h"],
    visibility = ["//visibility:private"],
    deps = [
        "//tensorflow/core:lib",
        "@com_google_absl//absl/memory",

        "//tensorflow:tf_framework_version_script.lds",
        "//tensorflow:tf_private_symbols.lds",
    ],
    framework_so = [],
    linkopts = select({
        "//tensorflow:macos": [
            "-Wl,-rename_section,__TEXT,text_env,__TEXT,__text",
            "-Wl,-unexported_symbols_list,$(location //tensorflow:tf_private_symbols.lds)",
        ],
        "//tensorflow:windows": [],

  static void PruneThread(void* param) {
    auto ram_file_block_cache = static_cast<RamFileBlockCache*>(param);
    ram_file_block_cache->Prune();
  }

 private:
  /// The size of the blocks stored in the LRU cache, as well as the size of the
  /// reads from the underlying filesystem.
  const size_t block_size_;

      return device_.shall_pin_to_this_device(tensorflow::wrap(op), &status);
    }
    return errors::Unimplemented("No custom device pinning implementation.");
  }

 private:
  TFE_Context* context_;
  TFE_CustomDevice device_;
  void* info_;
  string name_;
};

// An adapter which wraps the shape/data produced by C custom devices and uses

/// be used without recompilation in the widest range of TensorFlow versions.
///
/// The header is divided into sections, as follows:
///   1. Opaque plugin private data structures and wrappers for type safety;
///   2. Function tables for plugin functionality;
///   3. Versioning metadata;
///   4. Plugin registration API and the DSO entry point.

#ifdef __cplusplus
extern "C" {

        -  If you believe it should definitely be a public Keras API, please open a feature request in keras GitHub repo.
        -  As a workaround, you could import the same private symbol keras `keras.src`, but keep in mind the `src` namespace is not stable and those APIs may change or be removed in the future.

### Major Features and Improvements

load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    default_visibility = ["//visibility:private"],
    licenses = ["notice"],
)

# Filesystem implementation for POSIX environments: Linux, MacOS, Android, etc.
tf_cc_shared_object(
    name = "libposix_filesystem.so",
    framework_so = [],

  virtual Status GetFunctionDef(const FunctionDef**) = 0;

  // Returns a shared reference to the wrapped function.
  virtual absl::StatusOr<core::RefCountPtr<FunctionRecord>>
  GetFunctionRecord() = 0;

 private:
  const AbstractFunctionKind kind_;
};

using AbstractFunctionPtr =
    tensorflow::core::IntrusivePtr<tensorflow::AbstractFunction>;

}  // namespace tensorflow