code: def forward_for_export(self, x, lengths):
      # The use of negative indices on the transpose allow for expanded SqueezeExcite
      max_len = x.shape[-1]
      if max_len > self.max_len:
          self.set_max_len(max_len)
      dtype = x.dtype
      # Computes in float32 to avoid instabilities during training with AMP.
      with torch.cuda.amp.autocast(enabled=False):
          # Create sample mask - 1 represents value, 0 represents pad
          mask = self.make_pad_mask(
              lengths, max_audio_length=max_len, device=x.device
          )
          mask = ~mask  # 0 represents value, 1 represents pad
          x = x.float()  # For stable AMP, SE must be computed at fp32.
          x.masked_fill_(
              mask, 0.0
          )  # mask padded values explicitly to 0
          y = self._se_pool_step(x, mask)  # [B, C, 1]
          y = y.transpose(1, -1)  # [B, 1, C]
          y = self.fc(y)  # [B, 1, C]
          y = y.transpose(1, -1)  # [B, C, 1]
  
          # Note: Keep for future, in case we improve WER from doing so.
          # if self.context_window >= 0:
          #     y = F.interpolate(y, size=x.shape[-1], mode=self.interpolation_mode)
  
          y = torch.sigmoid(y)
          y = x * y
      return y, lengths
  
imports: import math
  from typing import Callable, Iterable, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.init import _calculate_correct_fan
  from torch.nn.modules.utils import _single
  from nemo.collections.common.parts.utils import activation_registry
  from nemo.core.classes.mixins import AccessMixin
  from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling the SqueezeExcite obj's underlying member method code with example usage: obj.forward_for_export(x, lengths) and returns: y,  lengths