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 forward(self, x):
      o = self.conv1d(x)
      o = self.pad(o)
      o = nn.functional.relu(o)
      o = self.norm(o)
      return o
  
file_path: TTS/TTS/tts/layers/generic/res_conv_bn.py
imports: from torch import nn
label_desc: Use image node_id 2 for calling the Conv1dBN obj's underlying member method code with example usage: obj.forward(x) and returns: o