: AbstractContext(kind) {}
  ~ImmediateExecutionContext() override {}
};

namespace internal {
struct ImmediateExecutionContextDeleter {
  void operator()(ImmediateExecutionContext* p) const {
    if (p != nullptr) {
      p->Release();
    }
  }
};
}  // namespace internal

using ImmediateContextPtr =
    std::unique_ptr<ImmediateExecutionContext,

  virtual Status RemoveFunction(const string& func) = 0;

 private:
  const AbstractContextKind kind_;
};

namespace internal {
struct AbstractContextDeleter {
  void operator()(AbstractContext* p) const {
    if (p != nullptr) {
      p->Release();
    }
  }
};
}  // namespace internal

using AbstractContextPtr =

class OpDeleter {
 public:
  void operator()(TFE_Op* to_delete) const { TFE_DeleteOp(to_delete); }
};

using OpPtr = std::unique_ptr<TFE_Op, OpDeleter>;

class StatusDeleter {
 public:
  void operator()(TF_Status* to_delete) const { TF_DeleteStatus(to_delete); }
};

using StatusPtr = std::unique_ptr<TF_Status, StatusDeleter>;

class ExecutorDeleter {
 public:
  void operator()(TFE_Executor* to_delete) const {

    // The block was evicted from another thread. Allow it to remain evicted.
    return TF_SetStatus(status, TF_OK, "");
  }
  if (block->lru_iterator != lru_list_.begin()) {
    lru_list_.erase(block->lru_iterator);
    lru_list_.push_front(key);
    block->lru_iterator = lru_list_.begin();
  }

  // Check for inconsistent state. If there is a block later in the same file

              << ", n = " << n;
        }
        // Verify the contents of the read.
        std::vector<char>::const_iterator begin = buf.begin() + offset;
        std::vector<char>::const_iterator end =
            offset + n > buf.size() ? buf.end() : begin + n;
        std::vector<char> want(begin, end);
        EXPECT_EQ(got, want) << "block size = " << block_size

  TFE_OpAddInput(minOp, axis, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  tensorflow::AttrValueMap attr_values = ExtractAttrs(minOp);
  tensorflow::AttrValueMap::const_iterator attr_found = attr_values.find("T");
  EXPECT_NE(attr_found, attr_values.cend());
  EXPECT_EQ(attr_found->second.type(), tensorflow::DataType::DT_FLOAT);
  attr_found = attr_values.find("Tidx");

limitations under the License.
==============================================================================*/

#include "tensorflow/c/c_api.h"

#include <algorithm>
#include <cstddef>
#include <iterator>
#include <memory>
#include <vector>

#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/c_test_util.h"
#include "tensorflow/c/tf_buffer.h"
#include "tensorflow/c/tf_buffer_internal.h"

#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"

namespace tensorflow {
namespace parallel_device {
namespace {

class OpDeleter {
 public:
  void operator()(TFE_Op* to_delete) const { TFE_DeleteOp(to_delete); }
};

using OpPtr = std::unique_ptr<TFE_Op, OpDeleter>;

using MaybeParallelTensorOwned =
    absl::variant<std::unique_ptr<ParallelTensor>, TensorHandlePtr>;

    T value;

    /// A list iterator pointing to the entry's position in the LRU list.
    std::list<std::string>::iterator lru_iterator;
  };

  bool LookupLocked(const std::string& key, T* value)
      ABSL_EXCLUSIVE_LOCKS_REQUIRED(mu_) {
    auto it = cache_.find(key);
    if (it == cache_.end()) {
      return false;
    }
    lru_list_.erase(it->second.lru_iterator);

    *   Add NUMA-aware MapAndBatch dataset.
    *   Deprecate `tf.data.Dataset.make_one_shot_iterator()` in V1, removed it
        from V2, and added tf.compat.v1.data.make_one_shot_iterator()`.
    *   Deprecate `tf.data.Dataset.make_initializable_iterator()` in V1, removed
        it from V2, and added `tf.compat.v1.data.make_initializable_iterator()`.
    *   Enable nested dataset support in core `tf.data` transformations.