docstr: Args:
      input_size: dimention of input embedding
      kernel_size: kernel_size for CNN
      padding: padding for CNN
      hidden_size: hidden size
docstr_tok: ["Args",":","input_size",":","dimention","of","input","embedding","kernel_size",":","kernel_size","for","CNN","padding",":","padding","for","CNN","hidden_size",":","hidden","size"]
code: def __init__(
      self,
      input_size=50,
      hidden_size=256,
      dropout=0,
      kernel_size=3,
      padding=1,
      activation_function=F.relu,
  ):
      """
      Args:
          input_size: dimention of input embedding
          kernel_size: kernel_size for CNN
          padding: padding for CNN
          hidden_size: hidden size
      """
      super().__init__()
      self.conv = nn.Conv1d(
          input_size, hidden_size, kernel_size, padding=padding
      )
      self.act = activation_function
      self.dropout = nn.Dropout(dropout)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new CNN object from inherited base classes: nn with example: obj = CNN(input_size, hidden_size, dropout, kernel_size, padding, activation_function)