class_docstr: Completes text prompts.
class_docstr_tok: ["Completes","text","prompts","."]
code: def _do_complete(
      self,
      prompt_seqs: Tensor,
      prompt_padding_mask: Optional[PaddingMask],
  ) -> Tuple[List[StringLike], SequenceGeneratorOutput]:
      generator_output = self.generator(
          prompt_seqs, prompt_padding_mask
      )
  
      texts: List[StringLike] = []
  
      for idx, hypotheses in enumerate(generator_output.hypotheses):
          if len(hypotheses) == 0:
              raise RuntimeError(
                  f"The sequence generator returned no hypothesis at index {idx}. Please file a bug report."
              )
  
          texts.append(self.text_decoder(hypotheses[0].seq))
  
      return texts, generator_output
  
imports: from abc import ABC
  from typing import List, Optional, Sequence, Tuple, final
  from torch import Tensor
  from fairseq2.data import StringLike
  from fairseq2.data.text import TextTokenDecoder, TextTokenEncoder, TextTokenizer
  from fairseq2.generation.generator import Seq2SeqGenerator, Seq2SeqGeneratorOutput, SequenceGenerator, SequenceGeneratorOutput
  from fairseq2.nn.padding import PaddingMask, pad_seqs
  from fairseq2.nn.utils.module import infer_device
label_desc: Use image node_id 4 for calling the TextCompleter obj's underlying member method code with example usage: obj._do_complete(prompt_seqs, prompt_padding_mask) and returns: texts,  generator_output