class_docstr: The abstract class for a predictor algorithm.
class_docstr_tok: ["The","abstract","class","for","a","predictor","algorithm","."]
docstr: One update of the predictor.
  
  Args:
      x: A PyTorch tensor representing the current state
      t: A Pytorch tensor representing the current time step.
      *args: Possibly additional arguments, in particular `y` for OU processes
  
  Returns:
      x: A PyTorch tensor of the next state.
      x_mean: A PyTorch tensor. The next state without random noise.
              Useful for denoising.
docstr_tok: ["One","update","of","the","predictor",".","Args",":","x",":","A","PyTorch","tensor","representing","the","current","state","t",":","A","Pytorch","tensor","representing","the","current","time","step",".","*","args",":","Possibly","additional","arguments",",","in","particular","`","y","`","for","OU","processes","Returns",":","x",":","A","PyTorch","tensor","of","the","next","state",".","x_mean",":","A","PyTorch","tensor",".","The","next","state","without","random","noise",".","Useful","for","denoising","."]
code: def update_fn(self, x, t, *args):
      """One update of the predictor.
  
      Args:
          x: A PyTorch tensor representing the current state
          t: A Pytorch tensor representing the current time step.
          *args: Possibly additional arguments, in particular `y` for OU processes
  
      Returns:
          x: A PyTorch tensor of the next state.
          x_mean: A PyTorch tensor. The next state without random noise.
                  Useful for denoising.
      """
      pass
  
file_path: espnet/espnet2/enh/diffusion/sampling/predictors.py
imports: import abc
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the Predictor obj's underlying member method code with example usage: obj.update_fn(x, t) without return types