code: def main():
      wandb.init(tensorboard=True)
  
      class ConvNet(nn.Module):
          def __init__(self):
              super().__init__()
              self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
              self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
              self.conv2_drop = nn.Dropout2d()
              self.fc1 = nn.Linear(320, 50)
              self.fc2 = nn.Linear(50, 10)
  
          def forward(self, x):
              x = F.relu(F.max_pool2d(self.conv1(x), 2))
              x = F.relu(
                  F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)
              )
              x = x.view(-1, 320)
              x = F.relu(self.fc1(x))
              x = F.dropout(x, training=self.training)
              x = self.fc2(x)
              return F.log_softmax(x, dim=1)
  
      writer = SummaryWriter()
      net = ConvNet()
      wandb.watch(net, log_freq=2)
      for i in range(10):
          output = net(torch.ones((64, 1, 28, 28)))
          loss = F.mse_loss(output, torch.ones((64, 10)))
          output.backward(torch.ones(64, 10))
          writer.add_scalar("loss", loss / 64, i + 1)
          writer.add_image("example", torch.ones((1, 28, 28)), i + 1)
      writer.close()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import wandb
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: F