Hello all,

I have been dealing with an error.

At first, I saved the weights of a trained model as follows:

```
torch.save(model.state_dict(), 'final_model.pth')
```

Then try to load the ‘pth’ file for evaluation by the following code:

```
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")
model = ConvNeuralNet(num_classes=4)
model.load_state_dict(torch.load('final_model.pth', map_location=device))
```

The definition of the model is as follows:

```
class ConvNeuralNet(nn.Module):
# Determine what layers and their order in CNN object
def __init__(self, num_classes):
super(ConvNeuralNet, self).__init__()
self.Conv1 = torch.nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(3, 3), padding=1, padding_mode='zeros')
self.Conv2 = torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), padding=1, padding_mode='zeros')
self.Conv3 = torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3, 3), padding=1, padding_mode='zeros')
self.Conv4 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), padding=1, padding_mode='zeros')
self.Conv5 = torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), padding=1, padding_mode='zeros')
self.Conv6 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3, 3), padding=1, padding_mode='zeros')
self.BatchNorm2d_1 = torch.nn.BatchNorm2d(64)
self.BatchNorm2d_2 = torch.nn.BatchNorm2d(128)
self.BatchNorm2d_3 = torch.nn.BatchNorm2d(128)
self.BatchNorm2d_4 = torch.nn.BatchNorm2d(256)
self.BatchNorm2d_5 = torch.nn.BatchNorm2d(256)
self.BatchNorm2d_6 = torch.nn.BatchNorm2d(512)
self.MaxPool2d = torch.nn.MaxPool2d(kernel_size=(2, 2), padding=0)
self.Dropout_25 = torch.nn.Dropout(p=0.25)
self.Relu = torch.nn.ReLU()
self.FC1 = nn.Linear(32768, 1024)
self.FC2 = nn.Linear(1024, 512)
self.FC3 = nn.Linear(512, num_classes)
# Progresses data across layers
def forward(self, x):
out = self.Conv1(x)
out = self.Relu(out)
out = self.BatchNorm2d_1(out)
out = self.MaxPool2d(out)
out = self.Conv2(out)
out = self.Relu(out)
out = self.BatchNorm2d_2(out)
out = self.MaxPool2d(out)
out = self.Conv3(out)
out = self.Relu(out)
out = self.BatchNorm2d_3(out)
out = self.MaxPool2d(out)
out = self.Conv4(out)
out = self.Relu(out)
out = self.BatchNorm2d_4(out)
out = self.MaxPool2d(out)
out = self.Conv5(out)
out = self.Relu(out)
out = self.BatchNorm2d_5(out)
out = self.MaxPool2d(out)
out = self.Conv6(out)
out = self.Relu(out)
out = self.BatchNorm2d_6(out)
out = self.MaxPool2d(out)
out = torch.flatten(out, 1)
out = self.FC1(out)
out = self.Relu(out)
out = self.Dropout_25(out)
out = self.FC2(out)
out = self.Relu(out)
out = self.Dropout_25(out)
out = self.FC3(out)
return out
```

The error message is as follows:

```
TypeError: conv2d() received an invalid combination of arguments - got (str, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:
* (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)
didn't match because some of the arguments have invalid types: (str, Parameter, Parameter, tuple, tuple, tuple, int)
* (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)
didn't match because some of the arguments have invalid types: (str, Parameter, Parameter, tuple, tuple, tuple, int)
```

would you please help me to fix it?