class Net(nn.Module):
    def init(self):
        super(Net, self).init()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.fc1 = nn.Linear(64  14  14, 30)
        self.fc2 = nn.Linear(30, 3)
        # self.softmax = nn.Softmax(dim=1)
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))  # output size: [batch_size, 32, 255, 255]
        x = self.pool(F.relu(self.conv2(x)))  # output size: [batch_size, 64, 126, 126]
        x = x.view(-1, 64  14  14)  # output size: [batch_size, 64126126]
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        # x=self.softmax(x)
        return x
x = torch.randn(1, 3, 64, 64)  # (batch size or #of images,channels RGB,width,height)
model = Net()
output = model(x)
model = Net()
state_dict = torch.load(“last_brain1.pth”)[‘state_dict’]
model.load_state_dict(state_dict)
print(model)
here is my code but still not working