class_docstr: Decoder with Relative Positional Transformer.
  
  Note:
      Default params
          params={
              'hidden_channels_ffn': 128,
              'num_heads': 2,
              "kernel_size": 3,
              "dropout_p": 0.1,
              "num_layers": 8,
              "rel_attn_window_size": 4,
              "input_length": None
          }
  
  Args:
      in_channels (int): number of input channels.
      out_channels (int): number of output channels.
      hidden_channels (int): number of hidden channels including Transformer layers.
      params (dict): dictionary for residual convolutional blocks.
class_docstr_tok: ["Decoder","with","Relative","Positional","Transformer",".","Note",":","Default","params","params=","{","'hidden_channels_ffn","'",":","128",",","'num_heads","'",":","2",",","``","kernel_size","''",":","3",",","``","dropout_p","''",":","0.1",",","``","num_layers","''",":","8",",","``","rel_attn_window_size","''",":","4",",","``","input_length","''",":","None","}","Args",":","in_channels","(","int",")",":","number","of","input","channels",".","out_channels","(","int",")",":","number","of","output","channels",".","hidden_channels","(","int",")",":","number","of","hidden","channels","including","Transformer","layers",".","params","(","dict",")",":","dictionary","for","residual","convolutional","blocks","."]
code: def forward(
      self, x, x_mask=None, g=None
  ):  # pylint: disable=unused-argument
      o = self.prenet(x) * x_mask
      o = self.rel_pos_transformer(o, x_mask)
      return o
  
imports: import torch
  from torch import nn
  from TTS.tts.layers.generic.res_conv_bn import Conv1dBN, Conv1dBNBlock, ResidualConv1dBNBlock
  from TTS.tts.layers.generic.transformer import FFTransformerBlock
  from TTS.tts.layers.generic.wavenet import WNBlocks
  from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
label_desc: Use image node_id 2 for calling the RelativePositionTransformerDecoder obj's underlying member method code with example usage: obj.forward(x, x_mask, g) and returns: o