(self, x, z, layer_past=None, attention_mask=None, head_mask=N

(self, hidden_states: torch.Tensor, adapter_res:bool=True, residual: torch.Tensor=None, adapte

(self, hidden_states: torch.Tensor, adapter_res:bool=True, residual: torch.Tensor=None, adapt

(self, x, z=None, layer_past=None, attention_mask=None, head_m

(self, x, z, layer_past=None, attention_mask=None, head_mask=None)

( self, x, layer_past=None, attention_mask=None, head_mask=None, encoder_hidden_states=None,

(self, x, z, layer_past=None, attention_mask=None, head_mask=N

( self, input_ids=None, past=None, attention_mask=None,

( self, input_ids=None, past=None, attention_mask=None,

(self, z)

(self, inputs)

( self, input_ids=None, past=None, attention_mask=None, token_type_ids

( self, input_ids=None, past=None, attention_mask=None, token_type_ids

(self, x: torch.Tensor)

(self, x)

(self, x, add_residual=True, residual=None)

(self, bsz, nsamples=1, device="cuda")

(self, bsz, nsamples=1, device="gpu")

(self, bsz, nsamples=1, device="cuda")

(self, x)

(self, x)

(self, input_ids=None, labels=None, past=None,

(n_iter, start=0.0, stop=1.0, n_cycle=4, ratio=0.5)

(model)

(model: nn.Module)

(file_path: str, x_label: str = 'text', y_label: str = 'label')

(file_path: str)

(file_path: str, dataset: str)

(file_path: str)

(module)

(module)

(module)

(self, mean1, logvar1, mean2, logvar2)

(x, mean, average=False, dim=None)

(x, mean, logvar, average=False, reduce=True, dim=None)

(x, average=False, dim=None)

(x, u, s)

(data_dir, dataset_name, tokenizer, train_bsz, train_seq_len, val_bsz, val_seq_len, test_bsz=1,

(path)

(mode)

(z)

(self, past, context)

(self, past, context)

(module, value)

(x)

(args)

(args)