class_docstr: Speedy speech encoder built on Transformer with Relative Position encoding.
  
  TODO: Integrate speaker conditioning vector.
  
  Args:
      in_channels (int): number of input channels.
      out_channels (int): number of output channels.
      hidden_channels (int): number of hidden channels
      params (dict): dictionary for residual convolutional blocks.
code: def forward(
      self, x, x_mask=None, g=None
  ):  # pylint: disable=unused-argument
      if x_mask is None:
          x_mask = 1
      o = self.prenet(x) * x_mask
      o = self.rel_pos_transformer(o, x_mask)
      return o
  
file_path: TTS/TTS/tts/layers/feed_forward/encoder.py
imports: from torch import nn
  from TTS.tts.layers.generic.res_conv_bn import ResidualConv1dBNBlock
  from TTS.tts.layers.generic.transformer import FFTransformerBlock
  from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
label_desc: Use image node_id 2 for calling the RelativePositionTransformerEncoder obj's underlying member method code with example usage: obj.forward(x, x_mask, g) and returns: o