NjSSKJr SSKrS/r"SS\RR R R5rg))AnyNLSTMc\rSrSrSr\R RRRr S\ 4Sjr \ S\S\SS4Sj5r\ S \SS \R RRRSS4S j5rS rg) 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) returncg)N QuantizedLSTM)selfs r/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py _get_nameLSTM._get_name'sargskwargsNc[S5e)NzrIt looks like you are trying to convert a non-observed LSTM module. Please, see the examples on quantizable LSTMs.)NotImplementedError)clsrrs r from_floatLSTM.from_float*s" 1  rrotherc[XR5(d$[SURS[U535e[R R RUSSS9nXlU$)NzExpected module type z, got FT)inplaceremove_qconfig) isinstance _FLOAT_MODULEAssertionErrortypetorchao quantizationconvert __class__)rr converteds r from_observedLSTM.from_observed4sq%!2!233 '(9(9':&e N HH))11 52 "rr )__name__ __module__ __qualname____firstlineno____doc__rr nn quantizablerrstrr classmethodrrrr%__static_attributes__r rr rr s2HHKK++00M3 s c d   4<  0G0G0L0L QW  r)typingrr__all__r r,r-rr rr r3s7    3588;; " " ' '3r