docstr: Computes the output length of the convolutional layers
docstr_tok: ["Computes","the","output","length","of","the","convolutional","layers"]
code: def _get_feat_extract_output_lengths(
      self, input_lengths: torch.LongTensor
  ):
      """
      Computes the output length of the convolutional layers
      """
  
      def _conv_out_length(input_length, kernel_size, stride):
          return torch.floor((input_length - kernel_size) / stride + 1)
  
      for i in range(len(self.feature_enc_layers)):
          input_lengths = _conv_out_length(
              input_lengths,
              self.feature_enc_layers[i][1],
              self.feature_enc_layers[i][2],
          )
  
      return input_lengths.to(torch.long)
  
file_path: fairseq/fairseq/models/speech_to_text/s2t_wav_transformer.py
imports: import math
  import torch
  import torch.nn
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.models import FairseqEncoder
  from fairseq.models.wav2vec import ConvFeatureExtractionModel
  from fairseq.modules import GradMultiply, LayerNorm, SamePad, TransformerEncoderLayer
label_desc: Use image node_id 3 for calling the SpeechWavTransformerEncoder obj's underlying member method code with example usage: obj._get_feat_extract_output_lengths(input_lengths) and returns: input_lengths, torch