tensorflow: Cannot use dictionary embeddings metadata for keras callbacks

Using dictionary as embeddings_metadata does not seem to be handled correctly in the Keras callbacks v2. Indeed, while trying to do so I get the following error message: ValueError: Unrecognized `Embedding` layer names passed to `keras.callbacks.TensorBoard` `embeddings_metadata` argument: dict_keys(['embedding_1', 'embedding_2']) After looking at the source code (see here), I figured that, when the metadata is not a string, it checked if the layer names was in embedding.metadata_path. However, this variable is never set in this case and it is an empty string by default.

Looking at how it is handled in the v1, I think the check should be done on self.embeddings_metadata (assuming if it is not a string it is a dictionary). So if layer.name in embedding.metadata_path: should be something like if layer.name in self.embeddings_metadata: Maybe it should be a copy of this dictionary or there should be a test to check that this is indeed a dictionary.