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","."]
code: def __init__(
      self,
      in_channels,
      out_channels,
      hidden_channels,
      kernel_size,
      dilation,
      num_conv_blocks=2,
  ):
      super().__init__()
      self.conv_bn_blocks = []
      for idx in range(num_conv_blocks):
          layer = Conv1dBN(
              in_channels if idx == 0 else hidden_channels,
              out_channels
              if idx == (num_conv_blocks - 1)
              else hidden_channels,
              kernel_size,
              dilation,
          )
          self.conv_bn_blocks.append(layer)
      self.conv_bn_blocks = nn.Sequential(*self.conv_bn_blocks)
  
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 Conv1dBNBlock object from inherited base classes: nn with example: obj = Conv1dBNBlock(in_channels, out_channels, hidden_channels, kernel_size, dilation, num_conv_blocks)