code: def forward_streaming(self, input_frame: torch.Tensor, states=None):
      # input_frame # B, 1, N
  
      # if complex spectrum,
      if is_complex(input_frame):
          feature = abs(input_frame)
      else:
          feature = input_frame
  
      ilens = torch.ones(feature.shape[0], device=feature.device)
  
      x, _, states = self.rnn(feature, ilens, states)
  
      masks = []
  
      for linear in self.linear:
          y = linear(x)
          y = self.nonlinear(y)
          masks.append(y)
  
      if self.predict_noise:
          *masks, mask_noise = masks
  
      masked = [input_frame * m for m in masks]
  
      others = OrderedDict(
          zip(
              ["mask_spk{}".format(i + 1) for i in range(len(masks))],
              masks,
          )
      )
      if self.predict_noise:
          others["noise1"] = input * mask_noise
  
      return masked, states, others
  
file_path: espnet/espnet2/enh/separator/rnn_separator.py
imports: from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from packaging.version import parse
  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 4 for calling the RNNSeparator obj's underlying member method code with example usage: obj.forward_streaming(input_frame, states) and returns: masked,  states,  others