code: def forward(
      self,
      x: torch.Tensor,
      encoder_padding_mask: Optional[torch.Tensor] = None,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      if encoder_padding_mask is not None:
          x = x.masked_fill(
              encoder_padding_mask.transpose(0, 1).unsqueeze(2), 0
          )
      avg_enc = mean(x.transpose(0, 1), encoder_padding_mask)
      lengths_enc = self.lengths_linear(avg_enc)
      lengths_enc = F.relu(lengths_enc)
      lengths_enc = F.dropout(
          lengths_enc, p=self.length_dropout, training=self.training
      )
      predicted_lengths_logits = self.lengths_pred(lengths_enc)
      # Softmax operations should be done in 32 bits when running
      # in FP16
      predicted_lengths = F.log_softmax(
          predicted_lengths_logits.float(), dim=-1
      ).to(predicted_lengths_logits.dtype)
      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 MaskedLengthPredictionModule obj's underlying member method code with example usage: obj.forward(x, encoder_padding_mask) and returns: predicted_lengths,  predicted_lengths_logits