code: def __init__(
      self,
      input_c: int,  # block input channel
      expand_c: int,  # block expand channel
      se_ratio: float = 0.25,
  ):
      super(SqueezeExcite, self).__init__()
      squeeze_c = int(input_c * se_ratio)
      self.conv_reduce = nn.Conv2d(expand_c, squeeze_c, 1)
      self.act1 = nn.SiLU()  # alias Swish
      self.conv_expand = nn.Conv2d(squeeze_c, expand_c, 1)
      self.act2 = nn.Sigmoid()
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Callable, Optional
  import torch.nn
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 to create a new SqueezeExcite object from inherited base classes: nn with example: obj = SqueezeExcite(input_c, expand_c, se_ratio)