code: def forward(
      self, x: Tensor, encoder_padding_mask: Optional[Tensor] = None
  ):
      for conv in self.conv_layers:
          residual = x
          x = self.linear1(x)
          x = self.activation(x)
          if encoder_padding_mask is not None:
              x = x.masked_fill(
                  encoder_padding_mask.transpose(0, 1).unsqueeze(2), 0
              )
          # Input to conv() is T x B x C
          x = conv(x)
          x = self.linear2(x)
          x = F.dropout(
              x, p=self.length_dropout, training=self.training
          )
          x = residual + x
      if encoder_padding_mask is not None:
          x = x.masked_fill(
              encoder_padding_mask.transpose(0, 1).unsqueeze(2), 0
          )
      x = x.transpose(0, 1)  # T x B x C => B x T x C
      x = F.relu(x)
      lengths_enc = pool(self.pooling_type, x, encoder_padding_mask)
      predicted_lengths_logits = self.lengths_pred(lengths_enc)
      predicted_lengths = F.log_softmax(
          predicted_lengths_logits, dim=-1
      )
      return predicted_lengths, predicted_lengths_logits
  
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 2 for calling the ConvLengthPredictionModule obj's underlying member method code with example usage: obj.forward(x, encoder_padding_mask) and returns: predicted_lengths,  predicted_lengths_logits