auto insert = cache_.insert(std::make_pair(key, entry));
    if (!insert.second) {
      lru_list_.erase(insert.first->second.lru_iterator);
      insert.first->second = entry;
    } else if (max_entries_ > 0 && cache_.size() > max_entries_) {
      cache_.erase(lru_list_.back());
      lru_list_.pop_back();
    }
  }

  bool DeleteLocked(const std::string& key) ABSL_EXCLUSIVE_LOCKS_REQUIRED(mu_) {

// `max_func`. In usual usage, max_func should be set to the result of
// TF_GraphNumFunctions(g). In this case, all the functions registered in
// `g` will be returned. Else, an unspecified subset.
//
// If successful, returns the number of TF_Function* successfully set in
// `funcs` and sets status to OK. The caller takes ownership of

            zero_indices.push_back(i);
          }
        }
      } else {
        any_gradient_nonzero = true;
        Gradient* new_gradients = nullptr;
        if (grad_it->second.size() == 1) {
          new_gradients = grad_it->second.at(0);
        } else {
          new_gradients = vspace.AggregateGradients(grad_it->second);
        }

// destination tensor to be placed in host memory).
// If async execution is enabled, the copy may be enqueued and the call will
// return "non-ready" handle. Else, this function returns after the copy has
// been done.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopyToDevice(
    TFE_TensorHandle* h, TFE_Context* ctx, const char* device_name,
    TF_Status* status);

#ifdef SWIG
#define TF_CAPI_EXPORT
#else
#if defined(_WIN32)
#ifdef TF_COMPILE_LIBRARY
#define TF_CAPI_EXPORT __declspec(dllexport)
#else
#define TF_CAPI_EXPORT __declspec(dllimport)
#endif  // TF_COMPILE_LIBRARY
#else
#ifdef TF_CAPI_WEAK
#define TF_CAPI_EXPORT \
  __attribute__((visibility("default"))) __attribute((weak))
#else
#define TF_CAPI_EXPORT __attribute__((visibility("default")))

// Runs `model` maybe wrapped in a function call op. This can be thought as
// being equivalent to the following python code.
//
// if use_function:
//   outputs = tf.function(model)(inputs)
// else:
//   outputs = model(inputs)
Status RunModel(Model model, AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,
                absl::Span<AbstractTensorHandle*> outputs, bool use_function);

                                  const char* path, TF_TransactionToken** token,
                                  TF_Status* status);

  /// Returns transaction token for `path` if it is part of a transaction else
  /// starts a new transaction and adds `path` to that transaction
  ///
  /// Transaction token is returned in `token`. Ownership of the token is in