pytext/models/doc_model.py [157:182]:
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            def forward(
                self,
                texts: Optional[List[str]] = None,
                multi_texts: Optional[List[List[str]]] = None,
                tokens: Optional[List[List[str]]] = None,
                languages: Optional[List[str]] = None,
            ):
                # PyTorch breaks with 2 'not None' checks right now.
                if texts is not None:
                    if tokens is not None:
                        raise RuntimeError("Can't set both tokens and texts")
                    if self.tokenizer is not None:
                        tokens = [
                            [t[0] for t in self.tokenizer.tokenize(text)]
                            for text in texts
                        ]

                if tokens is None:
                    raise RuntimeError("tokens is required")

                tokens = truncate_tokens(tokens, self.max_seq_len, self.vocab.pad_token)
                seq_lens = make_sequence_lengths(tokens)
                word_ids = self.vocab.lookup_indices_2d(tokens)
                word_ids = pad_2d(word_ids, seq_lens, self.pad_idx)
                logits = self.model(torch.tensor(word_ids), torch.tensor(seq_lens))
                return self.output_layer(logits)
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pytext/models/word_model.py [159:184]:
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            def forward(
                self,
                texts: Optional[List[str]] = None,
                multi_texts: Optional[List[List[str]]] = None,
                tokens: Optional[List[List[str]]] = None,
                languages: Optional[List[str]] = None,
            ):
                # PyTorch breaks with 2 'not None' checks right now.
                if texts is not None:
                    if tokens is not None:
                        raise RuntimeError("Can't set both tokens and texts")
                    if self.tokenizer is not None:
                        tokens = [
                            [t[0] for t in self.tokenizer.tokenize(text)]
                            for text in texts
                        ]

                if tokens is None:
                    raise RuntimeError("tokens is required")

                tokens = truncate_tokens(tokens, self.max_seq_len, self.vocab.pad_token)
                seq_lens = make_sequence_lengths(tokens)
                word_ids = self.vocab.lookup_indices_2d(tokens)
                word_ids = pad_2d(word_ids, seq_lens, self.pad_idx)
                logits = self.model(torch.tensor(word_ids), torch.tensor(seq_lens))
                return self.output_layer(logits)
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