class_docstr: 1d convolutional with batch norm.
  conv1d -> relu -> BN blocks.
  
  Note:
      Batch normalization is applied after ReLU regarding the original implementation.
  
  Args:
      in_channels (int): number of input channels.
      out_channels (int): number of output channels.
      kernel_size (int): kernel size for convolutional filters.
      dilation (int): dilation for convolution layers.
class_docstr_tok: ["1d","convolutional","with","batch","norm",".","conv1d","-",">","relu","-",">","BN","blocks",".","Note",":","Batch","normalization","is","applied","after","ReLU","regarding","the","original","implementation",".","Args",":","in_channels","(","int",")",":","number","of","input","channels",".","out_channels","(","int",")",":","number","of","output","channels",".","kernel_size","(","int",")",":","kernel","size","for","convolutional","filters",".","dilation","(","int",")",":","dilation","for","convolution","layers","."]
code: def __init__(self, in_channels, out_channels, kernel_size, dilation):
      super().__init__()
      padding = dilation * (kernel_size - 1)
      pad_s = padding // 2
      pad_e = padding - pad_s
      self.conv1d = nn.Conv1d(
          in_channels, out_channels, kernel_size, dilation=dilation
      )
      self.pad = nn.ZeroPad2d(
          (pad_s, pad_e, 0, 0)
      )  # uneven left and right padding
      self.norm = nn.BatchNorm1d(out_channels)
  
file_path: TTS/TTS/tts/layers/generic/res_conv_bn.py
imports: from torch import nn
label_desc: Use image node_id 1 to create a new Conv1dBN object from inherited base classes: nn with example: obj = Conv1dBN(in_channels, out_channels, kernel_size, dilation)