code: def __init__(
      self,
      embed_dim: int,
      conv_dim: int,
      max_target_positions: int,
      length_dropout: float,
      glu: bool,
      activation,
      pooling_type,
      conv_layers,
  ):
      super().__init__()
      self.length_dropout = length_dropout
      self.conv_layers = nn.ModuleList(conv_layers)
      self.glu = glu
      if glu:
          self.linear1 = nn.Linear(embed_dim, 2 * conv_dim)
      else:
          self.linear1 = nn.Linear(embed_dim, conv_dim)
      self.linear2 = nn.Linear(conv_dim, embed_dim)
      self.activation = get_activation(activation, dim=2)
      self.pooling_type = pooling_type
      self.lengths_pred = nn.Linear(embed_dim, max_target_positions)
  
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 ConvLengthPredictionModule object from inherited base classes: Module with example: obj = ConvLengthPredictionModule(embed_dim, conv_dim, max_target_positions, length_dropout, glu, activation, pooling_type, conv_layers)