class_docstr: Adapted Transformer model (https://arxiv.org/abs/1706.03762) for
  speech-to-text tasks. The Transformer encoder/decoder remains the same.
  A trainable input subsampler is prepended to the Transformer encoder to
  project inputs into the encoder dimension as well as downsample input
  sequence for computational efficiency.
class_docstr_tok: ["Adapted","Transformer","model","(","https",":","\/\/arxiv.org\/abs\/1706.03762",")","for","speech-to-text","tasks",".","The","Transformer","encoder\/decoder","remains","the","same",".","A","trainable","input","subsampler","is","prepended","to","the","Transformer","encoder","to","project","inputs","into","the","encoder","dimension","as","well","as","downsample","input","sequence","for","computational","efficiency","."]
code: def hub_models(cls):
      base_url = "http://dl.fbaipublicfiles.com/fairseq/s2t"
      model_ids = [
          "s2t_transformer_s-en-asr-librispeech",
          "s2t_transformer_m-en-asr-librispeech",
          "s2t_transformer_l-en-asr-librispeech",
      ]
      return {i: f"{base_url}/{i}.tar.gz" for i in model_ids}
  
imports: import logging
  import math
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.speech_to_text.hub_interface import S2THubInterface
  from fairseq.models.speech_to_text.modules.convolution import Conv1dSubsampler, Conv2dSubsampler
  from fairseq.models.transformer import Embedding, TransformerDecoder
  from fairseq.modules import FairseqDropout, LayerNorm, PositionalEmbedding, TransformerEncoderLayer
label_desc: Use image node_id 1 for calling the S2TTransformerModel obj's underlying member method code with example usage: obj.hub_models(cls) and returns: unknown