(
    dataset: Dataset, batch_size: int, num_workers: int = 3
)
return DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=num_workers,
        drop_last=True,
    )
generate the data_loader from the given dataset
    Args:
        dataset: Torch dataset object
        batch_size: batch size for training
        num_workers: num of parallel readers for reading the data
    Returns: dataloader for the dataset
generate the data_loader from the given dataset
    Args:
        dataset: Torch dataset object
        batch_size: batch size for training
        num_workers: num of parallel readers for reading the data
    Returns: dataloader for the dataset
generate the data_loader from the given dataset Args : dataset : Torch dataset object batch_size : batch size for training num_workers : num of parallel readers for reading the data Returns : dataloader for the dataset
def get_data_loader(
    dataset: Dataset, batch_size: int, num_workers: int = 3
) -> DataLoader:
    """
    generate the data_loader from the given dataset
    Args:
        dataset: Torch dataset object
        batch_size: batch size for training
        num_workers: num of parallel readers for reading the data
    Returns: dataloader for the dataset
    """
    return DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=num_workers,
        drop_last=True,
    )
def get_data_loader ( dataset : Dataset , batch_size : int , num_workers : int = 3 ) -> DataLoader : return DataLoader ( dataset , batch_size = batch_size , shuffle = True , num_workers = num_workers , drop_last = True , )