std::list<Key> lru_list_ ABSL_GUARDED_BY(mu_);

  /// The LRA (least recently added) list of block keys. The front of the list
  /// identifies the most recently added block.
  ///
  /// Note: blocks are added to lra_list_ only after they have successfully been
  /// fetched from the underlying block store.
  std::list<Key> lra_list_ ABSL_GUARDED_BY(mu_);

    TFE_Context* ctx, TFE_ContextDevicePlacementPolicy policy) {
  tensorflow::unwrap(ctx)->SetThreadLocalDevicePlacementPolicy(
      static_cast<tensorflow::ContextDevicePlacementPolicy>(policy));
}

// Note: this function looks up a thread local policy. So it should be called in
// the appropriate client thread. In particular, in async mode, it may not be
// safe to call this function from the async EagerExecutor threads.

      // Name
      "AssertAndCollective",
      // Args
      {"x: float", "condition: bool"},
      // Return values
      {"y: float"},
      // Attr def
      {},
      // Nodes
      {
          {{"assert"},
           "Assert",
           {"condition", "x"},
           {{"T", std::vector<DataType>{DT_FLOAT}}}},
          {{"y"},
           "CollectiveReduce",
           {"x"},

-#  endif
-# endif
-
-__STRING_INLINE size_t __strspn_c1 (const char *__s, int __accept);
-__STRING_INLINE size_t
-__strspn_c1 (const char *__s, int __accept)
-{
-  size_t __result = 0;
-  /* Please note that __accept never can be '\0'.  */
-  while (__s[__result] == __accept)
-    ++__result;
-  return __result;
-}
-
-__STRING_INLINE size_t __strspn_c2 (const char *__s, int __accept1,
-				    int __accept2);

    TF_ShapeAndTypeList** shape_list_array, int num_items);

// Infer shapes for the given `op`. The arguments mimic the arguments of the
// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`

    [Environment]::SetEnvironmentVariable(\"PATH\", $old_path + \";C:\VS\VC\Tools\MSVC\14.33.31629\bin\Hostx64\x64;C:\VS\Common7\Tools;C:\VS\MSBuild\Current\Bin\", \"Machine\");

# Add signtool.exe to the PATH. Note this path may need to be edited if updates
# are made to the Windows 10 SDK.
RUN $old_path = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\"); \

    ids.push_back(i);
  }
  const int64_t op_id = next_op_id_++;
  std::vector<TapeTensor> tensors;
  tensors.reserve(output_tensors.size());
  for (const TapeTensor& o : output_tensors) {
    // Note: the tensor can have already been watched and hence be in the tape,
    // so we cannot check that we're inserting it here.
    tensor_tape_[o.GetID()] = op_id;
    tensor_usage_[o.GetID()] = 1;
    tensors.push_back(o);

  // A vector to keep track of newly created `tf::Tensor` objects.
  std::vector<Tensor> all_input_tensors;
  // Update the vector with information from `input_tensors` if provided.
  if (input_tensors != nullptr) {
    // Note that we take the address of the elements in `all_input_tensors`
    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);

    }
  }

  // Compute body nodes.
  std::vector<const Node*> body_nodes;
  status->status = tensorflow::ComputeBodyNodes(
      fn_body, fn_name, num_opers, opers, input_nodes, &body_nodes);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // Compute body nodes.
  std::vector<const Node*> control_output_nodes;
  control_output_nodes.reserve(ncontrol_outputs);

        // parallel device.
        device_(device),
        executor_(
            TFE_NewExecutor(is_async, /*enable_streaming_enqueue=*/true,
                            /*in_flight_nodes_limit=*/in_flight_nodes_limit)),
        op_(nullptr),
        thread_(tensorflow::Env::Default()->StartThread(
            tensorflow::ThreadOptions(), "parallel_device_execute",
            std::bind(&DeviceThread::Run, this))) {}