Repository: Lightning-AI/pytorch-lightning
File: pytorch_lightning/utilities/seed.py (Python)

Code before:
import torch

from pytorch_lightning import _logger as log


def seed_everything(seed: Optional[int] = None) -> int:
    """
    Function that sets seed for pseudo-random number generators in:
    pytorch, numpy, python.random
    In addition, sets the env variable `PL_GLOBAL_SEED` which will be passed to
    spawned subprocesses (e.g. ddp_spawn backend).

    Args:
        seed: the integer value seed for global random state in Lightning.
            If `None`, will read seed from `PL_GLOBAL_SEED` env variable
            or select it randomly.
    """
    max_seed_value = np.iinfo(np.uint32).max
    min_seed_value = np.iinfo(np.uint32).min

    try:
        if seed is None:
            if "PL_GLOBAL_SEED" in os.environ:
                seed = os.environ["PL_GLOBAL_SEED"]
            else:
                seed = _select_seed_randomly(min_seed_value, max_seed_value)
        seed = int(seed)
    except (TypeError, ValueError):
        seed = _select_seed_randomly(min_seed_value, max_seed_value)

    if (seed > max_seed_value) or (seed < min_seed_value):
        log.warning(
            f"{seed} is not in bounds, \
            numpy accepts from {min_seed_value} to {max_seed_value}"
        )
        seed = _select_seed_randomly(min_seed_value, max_seed_value)

    os.environ["PL_GLOBAL_SEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    return seed


def _select_seed_randomly(min_seed_value: int = 0, max_seed_value: int = 255) -> int:
    seed = random.randint(min_seed_value, max_seed_value)
    log.warning(f"No correct seed found, seed set to {seed}")
    return seed


Review comment (suggestion):
```suggestion
            seed = os.environ.get("PL_GLOBAL_SEED", _select_seed_randomly(min_seed_value, max_seed_value)
```
let's use dict get and define the default value

Code after:
import torch
from pytorch_lightning import _logger as log
from pytorch_lightning.utilities import rank_zero_warn


def seed_everything(seed: Optional[int] = None) -> int:
    """
    Function that sets seed for pseudo-random number generators in:
    pytorch, numpy, python.random
    In addition, sets the env variable `PL_GLOBAL_SEED` which will be passed to
    spawned subprocesses (e.g. ddp_spawn backend).

    Args:
        seed: the integer value seed for global random state in Lightning.
            If `None`, will read seed from `PL_GLOBAL_SEED` env variable
            or select it randomly.
    """
    max_seed_value = np.iinfo(np.uint32).max
    min_seed_value = np.iinfo(np.uint32).min

    try:
        if seed is None:
            seed = os.environ.get("PL_GLOBAL_SEED")
        seed = int(seed)
    except (TypeError, ValueError):
        seed = _select_seed_randomly(min_seed_value, max_seed_value)
        rank_zero_warn(f"No correct seed found, seed set to {seed}")

    if not (min_seed_value <= seed <= max_seed_value):
        rank_zero_warn(f"{seed} is not in bounds, numpy accepts from {min_seed_value} to {max_seed_value}")
        seed = _select_seed_randomly(min_seed_value, max_seed_value)

    log.info(f"Global seed set to {seed}")
    os.environ["PL_GLOBAL_SEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    return seed


def _select_seed_randomly(min_seed_value: int = 0, max_seed_value: int = 255) -> int:
    return random.randint(min_seed_value, max_seed_value)