code: def __init__(
      self,
      input_width: int,
      block_size: int,
      output_width: int,
      n_blocks: int,
      k: int = 1,
  ):
      super().__init__()
      self.input_width = input_width
      self.block_size = block_size
      self.output_width = output_width
      self.n_blocks = n_blocks
      self.k = k
  
      self.w1s = nn.Parameter(
          torch.empty(
              (n_blocks, input_width, block_size), dtype=torch.float
          ).uniform_(-1.0, +1.0),
          requires_grad=True,
      )
      self.b1s = nn.Parameter(
          torch.empty(
              (n_blocks, block_size), dtype=torch.float
          ).uniform_(-1.0, +1.0),
          requires_grad=True,
      )
      self.w2s = nn.Parameter(
          torch.empty(
              (n_blocks, block_size, output_width), dtype=torch.float
          ).uniform_(-1.0, +1.0),
          requires_grad=True,
      )
      self.b2s = nn.Parameter(
          torch.empty(
              (n_blocks, output_width), dtype=torch.float
          ).uniform_(-1.0, +1.0),
          requires_grad=True,
      )
  
      self.mixer = nn.Parameter(
          torch.zeros((input_width, n_blocks), dtype=torch.float),
          requires_grad=True,
      )
      self.noise_controller = nn.Parameter(
          torch.zeros((input_width, n_blocks), dtype=torch.float),
          requires_grad=True,
      )
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new MoENetwork object from inherited base classes: nn with example: obj = MoENetwork(input_width, block_size, output_width, n_blocks, k)