from .core import dispatch
from .dispatcher import (Dispatcher, halt_ordering, restart_ordering,
                         MDNotImplementedError)

            

Reported by Pylint.

              from .core import dispatch
from .dispatcher import (Dispatcher, halt_ordering, restart_ordering,
                         MDNotImplementedError)

            

Reported by Pylint.

              from .core import dispatch
from .dispatcher import (Dispatcher, halt_ordering, restart_ordering,
                         MDNotImplementedError)

            

Reported by Pylint.

              # torchvision imports tqdm from here.
from torch.hub import tqdm, load_state_dict_from_url as load_url  # noqa: F401

            

Reported by Pylint.

              # torchvision imports tqdm from here.
from torch.hub import tqdm, load_state_dict_from_url as load_url  # noqa: F401

            

Reported by Pylint.

              # torchvision imports tqdm from here.
from torch.hub import tqdm, load_state_dict_from_url as load_url  # noqa: F401

            

Reported by Pylint.

              import torch.nn as nn


class Net(nn.Module):

    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 20)
        self.relu = nn.ReLU()

            

Reported by Pylint.

              import torch.nn as nn


class Net(nn.Module):

    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 20)
        self.relu = nn.ReLU()

            

Reported by Pylint.

              import torch.nn as nn


class Net(nn.Module):

    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 20)
        self.relu = nn.ReLU()

            

Reported by Pylint.

              import torch.nn as nn


class Net(nn.Module):

    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 20)

            

Reported by Pylint.

              import torch.nn as nn


class Net(nn.Module):

    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 20)

            

Reported by Pylint.

              import torch.nn as nn


class Net(nn.Module):

    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 20)

            

Reported by Pylint.

              from .trace_dependencies import (
    trace_dependencies,
)

            

Reported by Pylint.

              from .trace_dependencies import (
    trace_dependencies,
)

            

Reported by Pylint.

                      self.mode: str = mode
        if self.mode not in ('b', 't', 'rb', 'rt', 'r'):
            raise ValueError("Invalid mode {}".format(mode))
        # TODO: enforce typing for each instance based on mode, otherwise
        #       `argument_validation` with this DataPipe may be potentially broken
        self.length: int = length

    # Remove annotation due to 'IOBase' is a general type and true type
    # is determined at runtime based on mode. Some `DataPipe` requiring

            

Reported by Pylint.

              from io import IOBase
from typing import Iterable, Tuple

from torch.utils.data import IterDataPipe
from torch.utils.data.datapipes.utils.common import get_file_binaries_from_pathnames


class LoadFilesFromDiskIterDataPipe(IterDataPipe[Tuple[str, IOBase]]):
    r""" :class:`LoadFilesFromDiskIterDataPipe`.

            

Reported by Pylint.

              from .modules import *  # noqa: F403

            

Reported by Pylint.

              from .modules import *  # noqa: F403

            

Reported by Pylint.

              from torch.utils.data.datapipes.iter.callable import (
    CollateIterDataPipe as Collate,
    MapIterDataPipe as Map,
    TransformsIterDataPipe as Transforms,
)
from torch.utils.data.datapipes.iter.combinatorics import (
    SamplerIterDataPipe as Sampler,
    ShuffleIterDataPipe as Shuffle,
)

            

Reported by Pylint.

                         'Zip']

# Please keep this list sorted
assert __all__ == sorted(__all__)

            

Reported by Bandit.

                  import multiprocessing.util as _util

    def _register(func):
        def wrapper(arg):
            func()
        _util.register_after_fork(_register, wrapper)
else:
    import os


            

Reported by Pylint.

              import sys

__all__ = ['register_after_fork']

if sys.platform == 'win32' or sys.version_info < (3, 7):
    import multiprocessing.util as _util

    def _register(func):
        def wrapper(arg):

            

Reported by Pylint.

              from .modules import *  # noqa: F403

            

Reported by Pylint.

              from .modules import *  # noqa: F403

            

Reported by Pylint.