class_docstr: 1d convolutional block with batch norm. It is a set of conv1d -> relu -> BN blocks.
  
  Args:
      in_channels (int): number of input channels.
      out_channels (int): number of output channels.
      hidden_channels (int): number of inner convolution channels.
      kernel_size (int): kernel size for convolutional filters.
      dilation (int): dilation for convolution layers.
      num_conv_blocks (int, optional): number of convolutional blocks. Defaults to 2.
class_docstr_tok: ["1d","convolutional","block","with","batch","norm",".","It","is","a","set","of","conv1d","-",">","relu","-",">","BN","blocks",".","Args",":","in_channels","(","int",")",":","number","of","input","channels",".","out_channels","(","int",")",":","number","of","output","channels",".","hidden_channels","(","int",")",":","number","of","inner","convolution","channels",".","kernel_size","(","int",")",":","kernel","size","for","convolutional","filters",".","dilation","(","int",")",":","dilation","for","convolution","layers",".","num_conv_blocks","(","int",",","optional",")",":","number","of","convolutional","blocks",".","Defaults","to","2","."]
docstr: Shapes:
      x: (B, D, T)
docstr_tok: ["Shapes",":","x",":","(","B",",","D",",","T",")"]
code: def forward(self, x):
      """
      Shapes:
          x: (B, D, T)
      """
      return self.conv_bn_blocks(x)
  
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 Conv1dBNBlock obj's underlying member method code with example usage: obj.forward(x) and returns: self