class_docstr: This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig. 1).
  Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3.
  
  Args:
      input_channels (int): Number of channels in the input image
      squeeze_channels (int): Number of squeeze channels
      activation (Callable[..., torch.nn.Module], optional): ``delta`` activation. Default: ``torch.nn.ReLU``
      scale_activation (Callable[..., torch.nn.Module]): ``sigma`` activation. Default: ``torch.nn.Sigmoid``
code: def _scale(self, input: Tensor) -> Tensor:
      scale = self.avgpool(input)
      scale = self.fc1(scale)
      scale = self.activation(scale)
      scale = self.fc2(scale)
      return self.scale_activation(scale)
  
imports: import warnings
  from typing import Callable, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from ..utils import _log_api_usage_once, _make_ntuple
label_desc: Use image node_id 2 for calling the SqueezeExcitation obj's underlying member method code with example usage: obj._scale(input) and returns: self