def _load_pretrained_tok2vec(nlp, loc):
with Path(loc).open("<STR_LIT:rb>") as file_:<EOL><INDENT>weights_data = file_.read()<EOL><DEDENT>loaded = []<EOL>for name, component in nlp.pipeline:<EOL><INDENT>if hasattr(component, "<STR_LIT>") and hasattr(component.model, "<STR_LIT>"):<EOL><INDENT>component.tok2vec.from_bytes(weights_data)<EOL>loaded.append(name)<EOL><DEDENT><DEDENT>return loaded<EOL>
Load pre-trained weights for the 'token-to-vector' part of the component
    models, which is typically a CNN. See 'spacy pretrain'. Experimental.