class_docstr: *Dual-path recurrent neural networks (DPRNN)* :cite:`luo2020dual`.
  
  Args:
      feat_dim (int, optional): The feature dimension after Encoder module. (Default: 64)
      hidden_dim (int, optional): Hidden dimension in the RNN layer of DPRNN. (Default: 128)
      num_blocks (int, optional): Number of DPRNN layers. (Default: 6)
      rnn_type (str, optional): Type of RNN in DPRNN. Valid options are ["RNN", "LSTM", "GRU"]. (Default: "LSTM")
      d_model (int, optional): The number of expected features in the input. (Default: 256)
      chunk_size (int, optional): Chunk size of input for DPRNN. (Default: 100)
      chunk_stride (int, optional): Stride of chunk input for DPRNN. (Default: 50)
code: def merging(self, x: torch.Tensor, rest: int) -> torch.Tensor:
      batch_size, dim, _, _ = x.shape
      out = (
          x.transpose(2, 3)
          .contiguous()
          .view(batch_size, dim, -1, self.chunk_size * 2)
      )
      out1 = (
          out[:, :, :, : self.chunk_size]
          .contiguous()
          .view(batch_size, dim, -1)[:, :, self.chunk_stride :]
      )
      out2 = (
          out[:, :, :, self.chunk_size :]
          .contiguous()
          .view(batch_size, dim, -1)[:, :, : -self.chunk_stride]
      )
      out = out1 + out2
      if rest > 0:
          out = out[:, :, :-rest]
      out = out.contiguous()
      return out
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 4 for calling the DPRNN obj's underlying member method code with example usage: obj.merging(x, rest) and returns: out