U yhI@s*ddlZdgZGdddejjjjZdS)NLSTMc@s<eZdZdZejjjjZ ddZ e ddZ e ddZ dS) 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) cCsdS)NZ QuantizedLSTM)selfrrS/var/www/html/venv/lib/python3.8/site-packages/torch/ao/nn/quantized/modules/rnn.py _get_name#szLSTM._get_namecOs tddS)NzrIt looks like you are trying to convert a non-observed LSTM module. Please, see the examples on quantizable LSTMs.)NotImplementedError)clsargskwargsrrr from_float&szLSTM.from_floatcCs0t||jksttjjj|ddd}||_|S)NFT)ZinplaceZremove_qconfig)type _FLOAT_MODULEAssertionErrortorchaoZ quantizationconvert __class__)rotherZ convertedrrr from_observed.s  zLSTM.from_observedN)__name__ __module__ __qualname____doc__rrnn quantizablerr r classmethodr rrrrrrs  )r__all__rrrrrrrrs