code: def train(
      model, dataloader, num_classes, lr=0.001, momentum=0.9, epochs=20
  ):
      # criterion = nn.CrossEntropyLoss()
      criterion = nn.MSELoss(reduction="sum")
      # optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)
      optimizer = optim.Adam(model.parameters(), lr=lr)
  
      EPOCHS = epochs
      for epoch in range(
          EPOCHS
      ):  # loop over the dataset multiple times
          print("Epoch: ", epoch)
          running_loss = 0.0
          # for i, data in enumerate(trainloader, 0):
          for i, batch in tqdm(enumerate(dataloader)):
              # get the inputs; data is a list of [inputs, labels]
              # forward + backward + optimize
  
              batch["tabular"].requires_grad = True
  
              outputs = model(batch["tabular"])
  
              one_hot_encoded_target = onehot_encoding(
                  batch["target"], num_classes
              )
  
              # loss = criterion(outputs, batch['target'])
              loss = criterion(outputs, one_hot_encoded_target)
  
              # zero the parameter gradients
              optimizer.zero_grad()
  
              loss.backward()
              optimizer.step()
  
              # print statistics
              running_loss += loss.item()
              # print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
          print("Loss: ", running_loss)
  
      print("Finished Training")
  
imports: import numpy
  from sklearn.preprocessing import MinMaxScaler
  from tqdm import tqdm
  import pandas
  from torch.utils.data import Dataset
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.nn.parameter import Parameter
  import torch.nn.functional
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.model_selection import train_test_split
  import torch.optim
  from torch.utils.data import DataLoader
label_desc: Use image node_id 4 for calling a global function with example usage: train(model, dataloader, num_classes, lr, momentum, epochs) without return types