class_docstr: Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
  
  Network::
  
      x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
      |--------------------------------------------------------------------------------------------------|
  
  
  Args:
      channels (int): number of hidden channels for the convolutional layers.
      kernel_size (int): size of the convolution filter in each layer.
      dilations (list): list of dilation value for each conv layer in a block.
docstr: Args:
      x (Tensor): input tensor.
  Returns:
      Tensor: output tensor.
  Shapes:
      x: [B, C, T]
docstr_tok: ["Args",":","x","(","Tensor",")",":","input","tensor",".","Returns",":","Tensor",":","output","tensor",".","Shapes",":","x",":","[","B",",","C",",","T","]"]
code: def forward(self, x):
      """
      Args:
          x (Tensor): input tensor.
      Returns:
          Tensor: output tensor.
      Shapes:
          x: [B, C, T]
      """
      for c1, c2 in zip(self.convs1, self.convs2):
          xt = F.leaky_relu(x, LRELU_SLOPE)
          xt = c1(xt)
          xt = F.leaky_relu(xt, LRELU_SLOPE)
          xt = c2(xt)
          x = xt + x
      return x
  
imports: import torch
  import torchaudio
  from torch import nn
  from torch.nn import Conv1d, ConvTranspose1d
  from torch.nn import functional
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  from TTS.utils.io import load_fsspec
label_desc: Use image node_id 2 for calling the ResBlock1 obj's underlying member method code with example usage: obj.forward(x) and returns: x