class_docstr: Solver for compression task.
  
  The compression task combines a set of perceptual and objective losses
  to train an EncodecModel (composed of an encoder-decoder and a quantizer)
  to perform high fidelity audio reconstruction.
class_docstr_tok: ["Solver","for","compression","task",".","The","compression","task","combines","a","set","of","perceptual","and","objective","losses","to","train","an","EncodecModel","(","composed","of","an","encoder-decoder","and","a","quantizer",")","to","perform","high","fidelity","audio","reconstruction","."]
code: def run_epoch(self):
      # reset random seed at the beginning of the epoch
      self.rng = torch.Generator()
      self.rng.manual_seed(1234 + self.epoch)
      # run epoch
      super().run_epoch()
  
imports: import logging
  import multiprocessing
  from pathlib import Path
  import typing
  import flashy
  import omegaconf
  import torch
  from torch import nn
  from .None import base, builders
  from ..None import models, quantization
  from ..utils import checkpoint
  from ..utils.samples.manager import SampleManager
  from ..utils.utils import get_pool_executor
label_desc: Use image node_id 7 for calling the CompressionSolver obj's underlying member method code with example usage: obj.run_epoch() without return types