class_docstr: Apply filtering or resampling.
  
  Args:
      initial_sr (int): Initial sample rate.
      target_sr (int): Target sample rate.
      use_resampling: Whether to use resampling or not.
      use_filter (bool):
      n_bands (int): Number of bands to consider.
      idx_band (int):
      device (torch.device or str):
      cutoffs ():
      boost (bool):
class_docstr_tok: ["Apply","filtering","or","resampling",".","Args",":","initial_sr","(","int",")",":","Initial","sample","rate",".","target_sr","(","int",")",":","Target","sample","rate",".","use_resampling",":","Whether","to","use","resampling","or","not",".","use_filter","(","bool",")",":","n_bands","(","int",")",":","Number","of","bands","to","consider",".","idx_band","(","int",")",":","device","(","torch.device","or","str",")",":","cutoffs","(",")",":","boost","(","bool",")",":"]
docstr: Upsampling only.
docstr_tok: ["Upsampling","only","."]
code: def inverse_process(self, x):
      """Upsampling only."""
      if self.use_resampling:
          x = julius.resample_frac(
              x, old_sr=self.target_sr, new_sr=self.target_sr
          )
      return x
  
imports: import typing
  import flashy
  import julius
  import omegaconf
  import torch
  import torch.nn.functional
  from .None import builders
  from .None import base
  from ..None import models
  from ..modules.diffusion_schedule import NoiseSchedule
  from ..metrics import RelativeVolumeMel
  from ..models.builders import get_processor
  from ..utils.samples.manager import SampleManager
  from ..solvers.compression import CompressionSolver
label_desc: Use image node_id 3 for calling the DataProcess obj's underlying member method code with example usage: obj.inverse_process(x) and returns: x