class_docstr: Streaming implementation for Demucs. It supports being fed with any amount
  of audio at a time. You will get back as much audio as possible at that
  point.
  
  Args:
      - demucs (Demucs): Demucs model.
      - dry (float): amount of dry (e.g. input) signal to keep. 0 is maximum
          noise removal, 1 just returns the input signal. Small values > 0
          allows to limit distortions.
      - num_frames (int): number of frames to process at once. Higher values
          will increase overall latency but improve the real time factor.
      - resample_lookahead (int): extra lookahead used for the resampling.
      - resample_buffer (int): size of the buffer of previous inputs/outputs
          kept for resampling.
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code: def _separate_frame(self, frame):
      demucs = self.demucs
      skips = []
      next_state = []
      first = self.conv_state is None
      stride = self.stride * demucs.resample
      x = frame[None]
      for idx, encode in enumerate(demucs.encoder):
          stride //= demucs.stride
          length = x.shape[2]
          if idx == demucs.depth - 1:
              # This is sligthly faster for the last conv
              x = fast_conv(encode[0], x)
              x = encode[1](x)
              x = fast_conv(encode[2], x)
              x = encode[3](x)
          else:
              if not first:
                  prev = self.conv_state.pop(0)
                  prev = prev[..., stride:]
                  tgt = (
                      length - demucs.kernel_size
                  ) // demucs.stride + 1
                  missing = tgt - prev.shape[-1]
                  offset = (
                      length
                      - demucs.kernel_size
                      - demucs.stride * (missing - 1)
                  )
                  x = x[..., offset:]
              x = encode[1](encode[0](x))
              x = fast_conv(encode[2], x)
              x = encode[3](x)
              if not first:
                  x = th.cat([prev, x], -1)
              next_state.append(x)
          skips.append(x)
  
      x = x.permute(2, 0, 1)
      x, self.lstm_state = demucs.lstm(x, self.lstm_state)
      x = x.permute(1, 2, 0)
      # In the following, x contains only correct samples, i.e. the one
      # for which each time position is covered by two window of the upper
      # layer. extra contains extra samples to the right, and is used only as
      # a better padding for the online resampling.
      extra = None
      for idx, decode in enumerate(demucs.decoder):
          skip = skips.pop(-1)
          x += skip[..., : x.shape[-1]]
          x = fast_conv(decode[0], x)
          x = decode[1](x)
  
          if extra is not None:
              skip = skip[..., x.shape[-1] :]
              extra += skip[..., : extra.shape[-1]]
              extra = decode[2](decode[1](decode[0](extra)))
          x = decode[2](x)
          next_state.append(
              x[..., -demucs.stride :] - decode[2].bias.view(-1, 1)
          )
          if extra is None:
              extra = x[..., -demucs.stride :]
          else:
              extra[..., : demucs.stride] += next_state[-1]
          x = x[..., : -demucs.stride]
  
          if not first:
              prev = self.conv_state.pop(0)
              x[..., : demucs.stride] += prev
          if idx != demucs.depth - 1:
              x = decode[3](x)
              extra = decode[3](extra)
      self.conv_state = next_state
      return x[0], extra[0]
  
imports: import math
  import time
  import torch
  from torch import nn
  from torch.nn import functional
  from .resample import downsample2, upsample2
  from .utils import capture_init
label_desc: Use image node_id 6 for calling the DemucsStreamer obj's underlying member method code with example usage: obj._separate_frame(frame) and returns: x,  extra