class_docstr: Residual block of type 2 for HiFiGAN Vocoder :cite:`NEURIPS2020_c5d73680`.
  Args:
      channels (int): Number of channels in the input features.
      kernel_size (int, optional): Kernel size for 1D convolutions. (Default: ``3``)
      dilation (tuple of 2 ``int``, optional): Dilations for each 1D convolution. (Default: ``(1, 3)``)
      lrelu_slope (float): Slope of leaky ReLUs in activations.
docstr: Args:
      x (Tensor): input of shape ``(batch_size, channels, time_length)``.
  Returns:
      Tensor of the same shape as input.
docstr_tok: ["Args",":","x","(","Tensor",")",":","input","of","shape","``","(","batch_size",",","channels",",","time_length",")","``",".","Returns",":","Tensor","of","the","same","shape","as","input","."]
code: def forward(self, x: torch.Tensor):
      """
      Args:
          x (Tensor): input of shape ``(batch_size, channels, time_length)``.
      Returns:
          Tensor of the same shape as input.
      """
      for c in self.convs:
          xt = F.leaky_relu(x, self.lrelu_slope)
          xt = c(xt)
          x = xt + x
      return x
  
imports: from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Conv1d, ConvTranspose1d
label_desc: Use image node_id 2 for calling the ResBlock2 obj's underlying member method code with example usage: obj.forward(x) and returns: x