DataLoader(train_data, batch_size=args['batch_size'], num_workers=4, shuffle=True)

This line of code initializes a DataLoader object to load and process training data in batches.

The DataLoader takes in the following arguments:
- `train_data`: This is the training data that will be loaded and processed in batches. It is typically a PyTorch dataset object.
- `batch_size`: This specifies the number of samples in each batch.
- `num_workers`: This specifies the number of worker processes to use for data loading. Using multiple workers can speed up the data loading process.
- `shuffle`: This specifies whether to shuffle the data before loading it. Shuffling the data can help prevent the model from overfitting to the order of the training data.