Looking at the training part you’re defining net = conv_Net() after you’ve used Adam(net.parameters()), so this would cause an error. The model is a little confusing, what is convNxN and activ_Function? Also BatchNorm2d should be used after conv and before activation function. I’ve rewritten your code and the following works to run for me
class conv_Net(nn.Module):
    def __init__(self):
        super(conv_Net,self).__init__()
        self.bn1=nn.BatchNorm2d(64)
        self.bn2=nn.BatchNorm2d(16)
        self.Conv1=nn.Conv2d(3,64,3)
        self.Conv2=nn.Conv2d(64,64,3)
        self.Conv3=nn.Conv2d(64,16,1)
        self.FullC = nn.Linear(in_features=784,out_features=10)
        
    def forward(self,x):
        out=F.relu(self.bn1(self.Conv1(x)))
        out=F.relu(self.Conv2(out))
        out=F.avg_pool2d(out,(2,2),2,0,False,True,1)
        out=F.relu(self.bn2(self.Conv3(out)))
        out=F.avg_pool2d(out,(2,2),2,0,False,True,1)
        out=out.view(out.size(0),-1)
        out=self.FullC(out)
        return out
    
# Load Data
batch_size=64
train_dataset = datasets.CIFAR10(root='dataset/', train=True, transform=transforms.ToTensor(), download=True)
trainloader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)

net = conv_Net()
optimizer = optim.Adam(net.parameters(), lr=1e-4) 

device = 'cuda' if torch.cuda.is_available() else 'cpu'
net = net.to(device)
loss_function=nn.CrossEntropyLoss()

for epoch in range(10):
    losses = []
    for batch in trainloader:
        images, labels = batch
        images=images.to(device)
        labels=labels.to(device)
        optimizer.zero_grad() 
        preds = net(images)
        loss = loss_function(preds,labels)
        loss.backward() 
        optimizer.step() 
        losses.append(loss.item())
  
    print(sum(losses)/len(losses))

EDIT: Changed to use F.relu as this might be more clear for you.