class_docstr: Convolution unit of FeatureExtractor
class_docstr_tok: ["Convolution","unit","of","FeatureExtractor"]
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: int,
      stride: int,
      bias: bool,
      layer_norm: Optional[Module],
  ):
      super().__init__()
      self.kernel_size = kernel_size
      self.stride = stride
      self.layer_norm = layer_norm
      self.conv = nn.Conv1d(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          bias=bias,
      )
  
imports: import logging
  from typing import List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import Module, Parameter
  from .wavlm_attention import WavLMSelfAttention
label_desc: Use image node_id 1 to create a new ConvLayerBlock object from inherited base classes: Module with example: obj = ConvLayerBlock(in_channels, out_channels, kernel_size, stride, bias, layer_norm)