shxSSKrS/r"SS\RRRR 5rg)NLSTMc\rSrSrSr\R RRRr Sr \ S5r \ S5rSrg)r a&A 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) cg)N QuantizedLSTM)selfs u/Users/tiagomarins/Projetos/claudeai/copy_bank/venv/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py _get_nameLSTM._get_name%sc[S5e)NzrIt looks like you are trying to convert a non-observed LSTM module. Please, see the examples on quantizable LSTMs.)NotImplementedError)clsargskwargss r from_floatLSTM.from_float(s" 1  r c[XR5(de[RRR USSS9nXlU$)NFT)inplaceremove_qconfig) isinstance _FLOAT_MODULEtorchao quantizationconvert __class__)rother converteds r from_observedLSTM.from_observed2sM%!2!23333HH))11 52 "r rN)__name__ __module__ __qualname____firstlineno____doc__rrnn quantizablerrr classmethodrr!__static_attributes__rr r rr sO0HHKK++00M  r )r__all__rr(r)rrr r r-s4    /588;; " " ' '/r