docstr: Predict mask for WPE dereverberation.
  
  Args:
      data (torch.complex64/ComplexTensor): (B, T, C, F), double precision
      ilens (torch.Tensor): (B,)
  Returns:
      masks (torch.Tensor or List[torch.Tensor]): (B, T, C, F)
      ilens (torch.Tensor): (B,)
code: def predict_mask(
      self,
      data: Union[torch.Tensor, ComplexTensor],
      ilens: torch.LongTensor,
  ) -> Tuple[torch.Tensor, torch.LongTensor]:
      """Predict mask for WPE dereverberation.
  
      Args:
          data (torch.complex64/ComplexTensor): (B, T, C, F), double precision
          ilens (torch.Tensor): (B,)
      Returns:
          masks (torch.Tensor or List[torch.Tensor]): (B, T, C, F)
          ilens (torch.Tensor): (B,)
      """
      if self.use_dnn_mask:
          masks, ilens = self.mask_est(
              to_float(data.permute(0, 3, 2, 1)), ilens
          )
          # (B, F, C, T) -> (B, T, C, F)
          masks = [m.transpose(-1, -3) for m in masks]
          if self.nmask == 1:
              masks = masks[0]
      else:
          masks = None
      return masks, ilens
  
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 3 for calling the DNN_WPE obj's underlying member method code with example usage: obj.predict_mask(data, ilens) and returns: masks,  ilens