class_docstr: SqueezeExcitation
class_docstr_tok: ["SqueezeExcitation"]
code: def __init__(
      self,
      input_channels: int,
      activation_2: nn.Module,
      activation_1: nn.Module,
      conv_type: str,
      causal: bool,
      squeeze_factor: int = 4,
      bias: bool = True,
  ) -> None:
      super().__init__()
      self.causal = causal
      se_multiplier = 2 if causal else 1
      squeeze_channels = make_divisible(
          input_channels // squeeze_factor * se_multiplier, 8
      )
      self.temporal_cumualtive_gavg3d = TemporalCGAvgPool3D()
      self.fc1 = ConvBlock3D(
          input_channels * se_multiplier,
          squeeze_channels,
          kernel_size=(1, 1, 1),
          padding=0,
          tf_like=False,
          causal=causal,
          conv_type=conv_type,
          bias=bias,
      )
      self.activation_1 = activation_1()
      self.activation_2 = activation_2()
      self.fc2 = ConvBlock3D(
          squeeze_channels,
          input_channels,
          kernel_size=(1, 1, 1),
          padding=0,
          tf_like=False,
          causal=causal,
          conv_type=conv_type,
          bias=bias,
      )
  
imports: from typing import Any, Callable, Optional, Tuple, Union
  from einops import rearrange
  import torch
  from torch.nn.modules.utils import _triple
  import torch.nn.functional
  from torch import nn, Tensor
  from towhee.models.utils.general_utils import make_divisible
  from towhee.models.utils.causal_module import CausalModule
  from towhee.models.layers.activations import HardSigmoid
  from towhee.models.layers.conv_bn_activation import Conv2dBNActivation, Conv3DBNActivation
  from towhee.models.layers.padding_functions import same_padding
  from towhee.models.layers.tf_avgpool3d import TfAvgPool3D
  from towhee.models.layers.temporal_cg_avgpool3d import TemporalCGAvgPool3D
label_desc: Use image node_id 1 to create a new SqueezeExcitation object from inherited base classes: nn with example: obj = SqueezeExcitation(input_channels, activation_2, activation_1, conv_type, causal, squeeze_factor, bias)