docstr: Forward.
  
  Args:
      input (torch.Tensor or ComplexTensor): Encoded feature [B, T, F]
      ilens (torch.Tensor): input lengths [Batch]
      additional (Dict or None): other data included in model
          NOTE: not used in this model
  
  Returns:
      masked (List[Union(torch.Tensor, ComplexTensor)]): [(B, T, N), ...]
      ilens (torch.Tensor): (B,)
      others predicted data, e.g. tf_embedding: OrderedDict[
          'tf_embedding': learned embedding of all T-F bins (B, T * F, D),
      ]
code: def forward(
      self,
      input: Union[torch.Tensor, ComplexTensor],
      ilens: torch.Tensor,
      additional: Optional[Dict] = None,
  ) -> Tuple[
      List[Union[torch.Tensor, ComplexTensor]],
      torch.Tensor,
      OrderedDict,
  ]:
      """Forward.
  
      Args:
          input (torch.Tensor or ComplexTensor): Encoded feature [B, T, F]
          ilens (torch.Tensor): input lengths [Batch]
          additional (Dict or None): other data included in model
              NOTE: not used in this model
  
      Returns:
          masked (List[Union(torch.Tensor, ComplexTensor)]): [(B, T, N), ...]
          ilens (torch.Tensor): (B,)
          others predicted data, e.g. tf_embedding: OrderedDict[
              'tf_embedding': learned embedding of all T-F bins (B, T * F, D),
          ]
      """
      # if complex spectrum,
      if is_complex(input):
          feature = abs(input)
      else:
          feature = input
      B, T, F = input.shape
      # x:(B, T, F)
      x, ilens, _ = self.blstm(feature, ilens)
      # x:(B, T, F*D)
      x = self.linear(x)
      # x:(B, T, F*D)
      x = self.nonlinear(x)
      tf_embedding = x.view(B, -1, self.D)
  
      if self.training:
          masked = None
      else:
          # K-means for batch
          centers = tf_embedding[:, : self._num_spk, :].detach()
          dist = torch.empty(
              B, T * F, self._num_spk, device=tf_embedding.device
          )
          last_label = torch.zeros(B, T * F, device=tf_embedding.device)
          while True:
              for i in range(self._num_spk):
                  dist[:, :, i] = torch.sum(
                      (tf_embedding - centers[:, i, :].unsqueeze(1))
                      ** 2,
                      dim=2,
                  )
              label = dist.argmin(dim=2)
              if torch.sum(label != last_label) == 0:
                  break
              last_label = label
              for b in range(B):
                  for i in range(self._num_spk):
                      centers[b, i] = tf_embedding[
                          b, label[b] == i
                      ].mean(dim=0)
          label = label.view(B, T, F)
          masked = []
          for i in range(self._num_spk):
              masked.append(input * (label == i))
  
      others = OrderedDict(
          {"tf_embedding": tf_embedding},
      )
  
      return masked, ilens, others
  
file_path: espnet/espnet2/enh/separator/dpcl_separator.py
imports: from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.layers.complex_utils import is_complex
  from espnet2.enh.separator.abs_separator import AbsSeparator
  from espnet.nets.pytorch_backend.rnn.encoders import RNN
label_desc: Use image node_id 2 for calling the DPCLSeparator obj's underlying member method code with example usage: obj.forward(input, ilens, additional) and returns: masked,  ilens,  others