code: def __init__(
      self,
      dim: int,
      kernel_size: int = 7,
      expansion: int = 4,
      lrelu_slope: float = 0.3,
  ):
      super().__init__()
      padding = calc_same_padding(kernel_size)
      self.depthwise = nn.Conv1d(
          dim,
          dim * expansion,
          kernel_size=kernel_size,
          padding=padding[0],
          groups=dim,
      )
      self.act = nn.LeakyReLU(lrelu_slope)
      self.out = nn.Conv1d(dim * expansion, dim, 1, 1, 0)
      self.ln = nn.LayerNorm(dim)
  
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 DepthwiseConvModule object from inherited base classes: nn with example: obj = DepthwiseConvModule(dim, kernel_size, expansion, lrelu_slope)