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)
  
      conv_cfg_list = eval(self.cfg.conv_feature_layers)
  
      for i in range(len(conv_cfg_list)):
          input_lengths = _conv_out_length(
              input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2]
          )
  
      return input_lengths.to(torch.long)
  
imports: import math
  from dataclasses import dataclass, field
  from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.distributed import fsdp_wrap
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.distributed.fully_sharded_data_parallel import FullyShardedDataParallel
  from fairseq.modules import Fp32GroupNorm, Fp32LayerNorm, GradMultiply, GumbelVectorQuantizer, LayerNorm, MultiheadAttention, RelPositionalEncoding, SamePad, TransposeLast
  from fairseq.modules.checkpoint_activations import checkpoint_wrapper
  from fairseq.modules.conformer_layer import ConformerWav2Vec2EncoderLayer
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import buffered_arange, index_put, is_xla_tensor
  from .utils import pad_to_multiple
label_desc: Use image node_id 7 for calling the Wav2Vec2Model obj's underlying member method code with example usage: obj._get_feat_extract_output_lengths(input_lengths) and returns: input_lengths, torch