docstr: Update the context window of the SqueezeExcitation module, in-place if possible.
  
  Will update the pooling layer to either nn.AdaptiveAvgPool1d() (for global SE) or nn.AvgPool1d()
  (for limited context SE).
  
  If only the context window is changing but still a limited SE context block - then
  the earlier instance of nn.AvgPool1d() will be updated.
  
  Args:
      context_window: An integer representing the number of input timeframes that will be used
          to compute the context. Each timeframe corresponds to a single window stride of the
          STFT features.
  
          Say the window_stride = 0.01s, then a context window of 128 represents 128 * 0.01 s
          of context to compute the Squeeze step.
code: def change_context_window(self, context_window: int):
      """
      Update the context window of the SqueezeExcitation module, in-place if possible.
  
      Will update the pooling layer to either nn.AdaptiveAvgPool1d() (for global SE) or nn.AvgPool1d()
      (for limited context SE).
  
      If only the context window is changing but still a limited SE context block - then
      the earlier instance of nn.AvgPool1d() will be updated.
  
      Args:
          context_window: An integer representing the number of input timeframes that will be used
              to compute the context. Each timeframe corresponds to a single window stride of the
              STFT features.
  
              Say the window_stride = 0.01s, then a context window of 128 represents 128 * 0.01 s
              of context to compute the Squeeze step.
      """
      if hasattr(self, "context_window"):
          logging.info(
              f"Changing Squeeze-Excitation context window from {self.context_window} to {context_window}"
          )
  
      self.context_window = context_window
  
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 7 for calling the SqueezeExcite obj's underlying member method code with example usage: obj.change_context_window(context_window) without return types