models/efficientnet_v2_m.py [507:535]:
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        self.conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1792)
        self.avgpool = torch.nn.modules.pooling.AdaptiveAvgPool2d((1, 1))
        self.classifier = torch.nn.modules.linear.Linear(1792, 1000)

    def forward(self, input_1):
        features_0_0 = self.features_0_0(input_1)
        features_0_1 = self.features_0_1(features_0_0)
        sigmoid_1 = torch.sigmoid(features_0_1)
        mul_1 = features_0_1.__mul__(sigmoid_1)
        features_1_conv_0 = self.features_1_conv_0(mul_1)
        features_1_conv_1 = self.features_1_conv_1(features_1_conv_0)
        sigmoid_2 = torch.sigmoid(features_1_conv_1)
        mul_2 = features_1_conv_1.__mul__(sigmoid_2)
        features_1_conv_3 = self.features_1_conv_3(mul_2)
        features_1_conv_4 = self.features_1_conv_4(features_1_conv_3)
        add_1 = mul_1.__add__(features_1_conv_4)
        features_2_conv_0 = self.features_2_conv_0(add_1)
        features_2_conv_1 = self.features_2_conv_1(features_2_conv_0)
        sigmoid_3 = torch.sigmoid(features_2_conv_1)
        mul_3 = features_2_conv_1.__mul__(sigmoid_3)
        features_2_conv_3 = self.features_2_conv_3(mul_3)
        features_2_conv_4 = self.features_2_conv_4(features_2_conv_3)
        add_2 = add_1.__add__(features_2_conv_4)
        features_3_conv_0 = self.features_3_conv_0(add_2)
        features_3_conv_1 = self.features_3_conv_1(features_3_conv_0)
        sigmoid_4 = torch.sigmoid(features_3_conv_1)
        mul_4 = features_3_conv_1.__mul__(sigmoid_4)
        features_3_conv_3 = self.features_3_conv_3(mul_4)
        features_3_conv_4 = self.features_3_conv_4(features_3_conv_3)
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models/efficientnet_v2_s.py [355:383]:
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        self.conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1792)
        self.avgpool = torch.nn.modules.pooling.AdaptiveAvgPool2d((1, 1))
        self.classifier = torch.nn.modules.linear.Linear(1792, 1000)

    def forward(self, input_1):
        features_0_0 = self.features_0_0(input_1)
        features_0_1 = self.features_0_1(features_0_0)
        sigmoid_1 = torch.sigmoid(features_0_1)
        mul_1 = features_0_1.__mul__(sigmoid_1)
        features_1_conv_0 = self.features_1_conv_0(mul_1)
        features_1_conv_1 = self.features_1_conv_1(features_1_conv_0)
        sigmoid_2 = torch.sigmoid(features_1_conv_1)
        mul_2 = features_1_conv_1.__mul__(sigmoid_2)
        features_1_conv_3 = self.features_1_conv_3(mul_2)
        features_1_conv_4 = self.features_1_conv_4(features_1_conv_3)
        add_1 = mul_1.__add__(features_1_conv_4)
        features_2_conv_0 = self.features_2_conv_0(add_1)
        features_2_conv_1 = self.features_2_conv_1(features_2_conv_0)
        sigmoid_3 = torch.sigmoid(features_2_conv_1)
        mul_3 = features_2_conv_1.__mul__(sigmoid_3)
        features_2_conv_3 = self.features_2_conv_3(mul_3)
        features_2_conv_4 = self.features_2_conv_4(features_2_conv_3)
        add_2 = add_1.__add__(features_2_conv_4)
        features_3_conv_0 = self.features_3_conv_0(add_2)
        features_3_conv_1 = self.features_3_conv_1(features_3_conv_0)
        sigmoid_4 = torch.sigmoid(features_3_conv_1)
        mul_4 = features_3_conv_1.__mul__(sigmoid_4)
        features_3_conv_3 = self.features_3_conv_3(mul_4)
        features_3_conv_4 = self.features_3_conv_4(features_3_conv_3)
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