code: def _se_pool_step(self, x, mask):
      # Negate mask back to represent 1 for signal and 0 for padded timestep.
      mask = ~mask
  
      if self.context_window < 0:
          # [B, C, 1] - Masked Average over value + padding.
          y = torch.sum(x, dim=-1, keepdim=True) / mask.sum(
              dim=-1, keepdim=True
          ).type(x.dtype)
      else:
          # [B, C, 1] - Masked Average over value + padding with limited context.
          # During training randomly subsegments a context_window chunk of timesteps.
          # During inference selects only the first context_window chunk of timesteps.
          if self.training:
              y = _se_pool_step_script_train(
                  x, self.context_window, mask
              )
          else:
              y = _se_pool_step_script_infer(
                  x, self.context_window, mask
              )
      return y
  
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 4 for calling the SqueezeExcite obj's underlying member method code with example usage: obj._se_pool_step(x, mask) and returns: y