What is the purpose of torch.utils.data.random_split() and how do you create train/validation/test splits in PyTorch?

import torch
from torch.utils.data import Dataset, DataLoader, random_split

class MyDataset(Dataset):
    def __init__(self, n=1000):
        self.data   = torch.randn(n, 20)
        self.labels = torch.randint(0, 3, (n,))
    def __len__(self):
        return len(self.data)
    def __getitem__(self, idx):
        return self.data[idx], self.labels[idx]

full_dataset = MyDataset(n=1000)

# Split: 70% train, 15% val, 15% test
train_size = int(0.7 * len(full_dataset))
val_size   = int(0.15 * len(full_dataset))
test_size  = len(full_dataset) - train_size - val_size  # remainder, avoids rounding loss

# Use a generator for reproducible splits
generator = torch.Generator().manual_seed(42)
train_ds, val_ds, test_ds = random_split(
    full_dataset,
    [train_size, val_size, test_size],
    generator=generator,
)

print(len(train_ds), len(val_ds), len(test_ds))   # 700 150 150

train_loader = DataLoader(train_ds, batch_size=32, shuffle=True)
val_loader   = DataLoader(val_ds,   batch_size=32, shuffle=False)  # no shuffle needed
test_loader  = DataLoader(test_ds,  batch_size=32, shuffle=False)

# IMPORTANT GOTCHA: if your Dataset applies different transforms
# (e.g. data augmentation only for training), random_split alone
# does NOT let you apply different transforms per split, because
# all splits reference the SAME underlying Dataset object.
# Common workaround: split INDICES, then wrap with two separate
# Dataset instances using different transforms
from torch.utils.data import Subset
indices = torch.randperm(len(full_dataset), generator=generator).tolist()
train_idx = indices[:train_size]
val_idx   = indices[train_size:train_size+val_size]
# train_dataset_aug = Subset(MyDatasetWithAugmentation(...), train_idx)
# val_dataset_plain = Subset(MyDatasetPlain(...), val_idx)