code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: int,
      stride: int = 1,
      padding: int = 0,
      dilation: int = 1,
      groups: int = 1,
      bias: bool = True,
      with_r: bool = False,
  ):
      super().__init__(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          dilation,
          groups,
          bias,
      )
      self.rank = 1
      self.addcoords = AddCoords(self.rank, with_r)
      self.conv = nn.Conv1d(
          in_channels + self.rank + int(with_r),
          out_channels,
          kernel_size,
          stride,
          padding,
          dilation,
          groups,
          bias,
      )
  
imports: from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import parametrize
  from TTS.tts.layers.delightful_tts.kernel_predictor import KernelPredictor
label_desc: Use image node_id 1 to create a new CoordConv1d object from inherited base classes: nn.modules.conv with example: obj = CoordConv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, with_r)