sgxddlZdgZGddejjjj Zy)NLSTMceZdZdZej j jjZ dZ e dZ e dZ y)rarA quantized long short-term memory (LSTM). For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` Attributes: layers : instances of the `_LSTMLayer` .. note:: To access the weights and biases, you need to access them per layer. See examples in :class:`~torch.ao.nn.quantizable.LSTM` Examples:: >>> # xdoctest: +SKIP >>> custom_module_config = { ... 'float_to_observed_custom_module_class': { ... nn.LSTM: nn.quantizable.LSTM, ... }, ... 'observed_to_quantized_custom_module_class': { ... nn.quantizable.LSTM: nn.quantized.LSTM, ... } ... } >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) >>> tq.convert(model, convert_custom_module_class=custom_module_config) cy)N QuantizedLSTM)selfs T/var/www/html/venv/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py _get_namezLSTM._get_name%sctd)NzrIt looks like you are trying to convert a non-observed LSTM module. Please, see the examples on quantizable LSTMs.)NotImplementedError)clsargskwargss r from_floatzLSTM.from_float(s" 1  r ct||jk(sJtjjj |dd}||_|S)NFT)inplaceremove_qconfig)type _FLOAT_MODULEtorchao quantizationconvert __class__)rother converteds r from_observedzLSTM.from_observed2sPE{c/////HH))11 52 " r N)__name__ __module__ __qualname____doc__rrnn quantizablerrr classmethodrrrr r rr sO0HHKK++00M  r )r__all__rr#r$rrr r r's4    /588;; " " ' '/r