class_docstr: KL-Divergence metric based on pre-trained PASST classifier on AudioSet.
  
  From: PaSST: Efficient Training of Audio Transformers with Patchout
  Paper: https://arxiv.org/abs/2110.05069
  Implementation: https://github.com/kkoutini/PaSST
  
  Follow instructions from the github repo:
  ```
  pip install 'git+https://github.com/kkoutini/passt_hear21@0.0.19#egg=hear21passt'
  ```
  
  Args:
      pretrained_length (float, optional): Audio duration used for the pretrained model.
class_docstr_tok: ["KL-Divergence","metric","based","on","pre-trained","PASST","classifier","on","AudioSet",".","From",":","PaSST",":","Efficient","Training","of","Audio","Transformers","with","Patchout","Paper",":","https",":","\/\/arxiv.org\/abs\/2110.05069","Implementation",":","https",":","\/\/github.com\/kkoutini\/PaSST","Follow","instructions","from","the","github","repo",":","``","`","pip","install","'git+https",":","\/\/github.com\/kkoutini\/passt_hear21","@","0.0.19","#","egg=hear21passt'","``","`","Args",":","pretrained_length","(","float",",","optional",")",":","Audio","duration","used","for","the","pretrained","model","."]
docstr: Initialize underlying PaSST audio classifier.
docstr_tok: ["Initialize","underlying","PaSST","audio","classifier","."]
code: def _initialize_model(
      self, pretrained_length: tp.Optional[float] = None
  ):
      """Initialize underlying PaSST audio classifier."""
      model, sr, max_frames, min_frames = self._load_base_model(
          pretrained_length
      )
      self.min_input_frames = min_frames
      self.max_input_frames = max_frames
      self.model_sample_rate = sr
      self.model = model
      self.model.eval()
      self.model.to(self.device)
  
imports: import contextlib
  from functools import partial
  import logging
  import os
  import typing
  import torch
  import torchmetrics
  from ..data.audio_utils import convert_audio
label_desc: Use image node_id 2 for calling the PasstKLDivergenceMetric obj's underlying member method code with example usage: obj._initialize_model(pretrained_length) without return types