code: def __init__(self, input_size, output_size):
      super().__init__()
      self.encoding = PositionalEncoding(input_size)
      self.input_conv = nn.Conv1d(input_size, input_size, 3, padding=1)
      self.output_conv = nn.Conv1d(
          input_size, output_size * 2, 3, padding=1
      )
  
      nn.init.xavier_uniform_(self.input_conv.weight)
      nn.init.xavier_uniform_(self.output_conv.weight)
      nn.init.zeros_(self.input_conv.bias)
      nn.init.zeros_(self.output_conv.bias)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import weight_norm
label_desc: Use image node_id 1 to create a new FiLM object from inherited base classes: nn with example: obj = FiLM(input_size, output_size)