human
Solve the following python programming problem given the following code:
# %load modeling_utils/data_prep_new.py
# %%writefile modeling_utils/data_prep_new.py

train_X, train_y, mean_series, std_dev_series = data_prep.process_data_train(
    train, target='target_strict')
valid_X, valid_y, _, _ = data_prep.process_data_train(
    valid, target='target_strict')

# class DfDataset(Dataset):
#     '''Makes dataset from df of data and df of targets'''
#     def __init__(self, data, targets):
#         self.data = data
#         self.targets = targets
        
#     def __len__(self):
#         return len(self.data)
    
#     def __getitem__(self, idx):
#         return self.data[idx,:], self.targets[idx,:]
    
# def get_dataset(data, targets):
#     return DfDataset(data, targets)
    
# def get_loader(dataset, use_cuda, batch_size=6400, shuffle=True):
#     return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, pin_memory=use_cuda) 

train_dataset = j_utils.get_dataset(train_X.values, train_y.values)
train_loader = j_utils.get_loader(train_dataset, use_cuda, batch_size=2**13, shuffle=True)

valid_dataset = j_utils.get_dataset(valid_X.values, valid_y.values)
valid_loader = j_utils.get_loader(valid_dataset, use_cuda, batch_size=2**13, shuffle=True)
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Until I figure out a good imputation method (e.g. bayes PCA), just drop columns with null still