return l;
}

void TFE_ContextClearCaches(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->ClearCachesAndThreadExecutors();
}

// Set server_def on the context, possibly updating it.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx,
                                                   int keep_alive_secs,
                                                   const void* proto,

  status->status = absl::OkStatus();
  return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(tensor));
}

// Set server_def on the context, possibly updating it.
TF_CAPI_EXPORT extern void TFE_EnableCollectiveOps(TFE_Context* ctx,
                                                   const void* proto,
                                                   size_t proto_len,

    // This modification only updates the destination node for
    // the purposes of running this graph in a session. Thus, we don't
    // record the source node as being modified.
    RecordMutation(graph, *dst.oper, "updating input tensor");
  }
}

// Apis that are corresponding to python c api. --------------------------

void TF_AddOperationControlInput(TF_Graph* graph, TF_Operation* op,

## Keras

*  `keras.layers.experimental.DynamicEmbedding`
    * Added `DynamicEmbedding` Keras layer
    * Added 'UpdateEmbeddingCallback`
    * `DynamicEmbedding` layer allows for the continuous updating of the
      vocabulary and embeddings during the training process. This layer
      maintains a hash table to track the most up-to-date vocabulary based on