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
      """
      for i in range(self.config.num_conv_layers):
          input_lengths = (input_lengths - 1) // 2 + 1
  
      return input_lengths
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_speech_to_text import Speech2TextConfig
label_desc: Use image node_id 2 for calling the Speech2TextPreTrainedModel obj's underlying member method code with example usage: obj._get_feat_extract_output_lengths(input_lengths) and returns: input_lengths