code: def __init__(
      self,
      embed_dim: int,
      length_hidden_dim: int,
      max_target_positions: int,
      length_dropout: float,
  ):
      super().__init__()
      self.lengths_linear = nn.Linear(embed_dim, length_hidden_dim)
      self.lengths_pred = nn.Linear(
          length_hidden_dim, max_target_positions
      )
      self.length_dropout = length_dropout
  
imports: from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from pytext.config.module_config import Activation, ModuleConfig
  from pytext.models.module import create_module, Module
  from pytext.optimizer import get_activation
  from torch import Tensor
  from .light_conv import LightweightConv
label_desc: Use image node_id 1 to create a new MaskedLengthPredictionModule object from inherited base classes: Module with example: obj = MaskedLengthPredictionModule(embed_dim, length_hidden_dim, max_target_positions, length_dropout)