class_docstr: Encoder used to isolate features in raw audio for Wav2Vec style training.
  Treated as preprocessor module in NeMo ASR training. Defaults values are
  for base model found in Baeski et al (https://arxiv.org/abs/2006.11477),
  save for use of layer normalization as default schema. (Chosen for stability.) 
class_docstr_tok: ["Encoder","used","to","isolate","features","in","raw","audio","for","Wav2Vec","style","training",".","Treated","as","preprocessor","module","in","NeMo","ASR","training",".","Defaults","values","are","for","base","model","found","in","Baeski","et","al","(","https",":","\/\/arxiv.org\/abs\/2006.11477",")",",","save","for","use","of","layer","normalization","as","default","schema",".","(","Chosen","for","stability",".",")"]
code: def get_lengths(self, audio_lengths):
      # converts audio lengths to timestep lengths
      for conv in self.layer_cfg:
          kernel = conv["kernel_size"]
          stride = conv["stride"]
          audio_lengths = (
              torch.div(
                  audio_lengths - kernel, stride, rounding_mode="floor"
              )
              + 1
          )  # from pytorch documentation
      return audio_lengths
  
imports: import math
  import random
  from typing import Dict, List, Tuple
  import torch
  from omegaconf import DictConfig
  from omegaconf.dictconfig import DictConfig
  from torch import nn
  from torch.nn import functional
  from nemo.collections.common.parts import form_attention_mask, transformer_weights_init
  from nemo.collections.nlp.modules.common.transformer import TransformerEncoder
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types import AcousticEncodedRepresentation, AudioSignal, LengthsType, NeuralType, SpectrogramType
label_desc: Use image node_id 7 for calling the ConvFeatureEncoder obj's underlying member method code with example usage: obj.get_lengths(audio_lengths) and returns: audio_lengths