code: def __init__(
      self,
      wtype: str = "blstmp",
      widim: int = 257,
      wlayers: int = 3,
      wunits: int = 300,
      wprojs: int = 320,
      dropout_rate: float = 0.0,
      taps: int = 5,
      delay: int = 3,
      use_dnn_mask: bool = True,
      nmask: int = 1,
      nonlinear: str = "sigmoid",
      iterations: int = 1,
      normalization: bool = False,
      eps: float = 1e-6,
      diagonal_loading: bool = True,
      diag_eps: float = 1e-7,
      mask_flooring: bool = False,
      flooring_thres: float = 1e-6,
      use_torch_solver: bool = True,
  ):
      super().__init__()
      self.iterations = iterations
      self.taps = taps
      self.delay = delay
      self.eps = eps
  
      self.normalization = normalization
      self.use_dnn_mask = use_dnn_mask
  
      self.inverse_power = True
      self.diagonal_loading = diagonal_loading
      self.diag_eps = diag_eps
      self.mask_flooring = mask_flooring
      self.flooring_thres = flooring_thres
      self.use_torch_solver = use_torch_solver
  
      if self.use_dnn_mask:
          self.nmask = nmask
          self.mask_est = MaskEstimator(
              wtype,
              widim,
              wlayers,
              wunits,
              wprojs,
              dropout_rate,
              nmask=nmask,
              nonlinear=nonlinear,
          )
      else:
          self.nmask = 1
  
file_path: espnet/espnet2/enh/layers/dnn_wpe.py
imports: from typing import Tuple, Union
  import torch
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.layers.complex_utils import to_double, to_float
  from espnet2.enh.layers.mask_estimator import MaskEstimator
  from espnet2.enh.layers.wpe import wpe_one_iteration
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
label_desc: Use image node_id 1 to create a new DNN_WPE object from inherited base classes: torch.nn with example: obj = DNN_WPE(wtype, widim, wlayers, wunits, wprojs, dropout_rate, taps, delay, use_dnn_mask, nmask, nonlinear, iterations, normalization, eps, diagonal_loading, diag_eps, mask_flooring, flooring_thres, use_torch_solver)