// Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.
  void DeleteTrace(const AbstractTensorHandle*);

  // Consumes the internal state of the tape (so cannot be called more than
  // once unless the tape is persistent) and produces the gradient of the target
  // tensors with respect to the source tensors. The output gradients are used

  ModularReadOnlyMemoryRegion(const ModularReadOnlyMemoryRegion&) = delete;
  void operator=(const ModularReadOnlyMemoryRegion&) = delete;
};

// Registers a filesystem plugin so that core TensorFlow can use it.
Status RegisterFilesystemPlugin(const std::string& dso_path);

}  // namespace tensorflow

  // Block until all pending nodes are finished.
  virtual Status AsyncWait() = 0;

  // Add a function (serialized FunctionDef protocol buffer) so that it can
  // be executed as an op. Return error if the function with the same name
  // already exists.
  virtual Status AddFunctionDef(const FunctionDef& fdef) = 0;

  // Notifies about the function removal.

  // is always executed during Read.
  bool IsCacheEnabled() const { return block_size_ > 0 && max_bytes_ > 0; }

  // We can not pass a lambda with capture as a function pointer to
  // `TF_StartThread`, so we have to wrap `Prune` inside a static function.
  static void PruneThread(void* param) {
    auto ram_file_block_cache = static_cast<RamFileBlockCache*>(param);
    ram_file_block_cache->Prune();
  }

 private:

/// different compiler options breaks compatibility. For more details about how
/// this is used, please consult next sections. Here we just wrap these tables
/// in lint warnings so that changes here cause changes to the versioning data
/// as well. Here is a short summary of what changes are allowed:
///   * adding a new method at the end of a table is allowed at any time;