class_docstr: Represents a sequence generator.
class_docstr_tok: ["Represents","a","sequence","generator","."]
docstr: :param prompt_seqs:
      The prompt sequences. *Shape:* :math:`(N,S)`, where :math:`N` is the
      batch size and :math:`S` is the sequence length.
  :param prompt_padding_mask:
      The padding mask of ``prompt_seqs``. *Shape:* Same as ``prompt_seqs``.
docstr_tok: [":","param","prompt_seqs",":","The","prompt","sequences",".","*","Shape",":","*",":","math",":","`","(","N",",","S",")","`",",","where",":","math",":","`","N","`","is","the","batch","size","and",":","math",":","`","S","`","is","the","sequence","length",".",":","param","prompt_padding_mask",":","The","padding","mask","of","``","prompt_seqs","``",".","*","Shape",":","*","Same","as","``","prompt_seqs","``","."]
code: def __call__(
      self,
      prompt_seqs: Tensor,
      prompt_padding_mask: Optional[PaddingMask],
  ) -> SequenceGeneratorOutput:
      """
      :param prompt_seqs:
          The prompt sequences. *Shape:* :math:`(N,S)`, where :math:`N` is the
          batch size and :math:`S` is the sequence length.
      :param prompt_padding_mask:
          The padding mask of ``prompt_seqs``. *Shape:* Same as ``prompt_seqs``.
      """
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import Dict, List, Optional, Protocol
  from torch import Tensor
  from torch.utils.hooks import RemovableHandle
  from fairseq2.models.decoder import DecoderModel
  from fairseq2.models.encoder_decoder import EncoderDecoderModel
  from fairseq2.nn.padding import PaddingMask
label_desc: Use image node_id 2 for calling the SequenceGenerator obj's underlying member method code with example usage: obj.__call__(prompt_seqs, prompt_padding_mask) without return types