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PyTorch-nightly can not me imported using python-3.7.0 #74087
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high prioritymodule: python frontendFor issues relating to PyTorch's Python frontendFor issues relating to PyTorch's Python frontendmodule: regressionIt used to work, and now it doesn'tIt used to work, and now it doesn'ttriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module
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🐛 Describe the bug
Steps to reproduce:
conda create -n py3.7-torch-nightly python==3.7.0 pytorch cpuonly -c pytorch-nightly
conda activate py3.7-torch-nightly
python -c "import torch"
which would fail with:
% python -c "import torch"
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/Users/nshulga/git/pytorch/pytorch/torch/__init__.py", line 751, in <module>
from .functional import * # noqa: F403
File "/Users/nshulga/git/pytorch/pytorch/torch/functional.py", line 9, in <module>
import torch.nn.functional as F
File "/Users/nshulga/git/pytorch/pytorch/torch/nn/__init__.py", line 1, in <module>
from .modules import * # noqa: F403
File "/Users/nshulga/git/pytorch/pytorch/torch/nn/modules/__init__.py", line 1, in <module>
from .module import Module
File "/Users/nshulga/git/pytorch/pytorch/torch/nn/modules/module.py", line 205, in <module>
class Module:
File "/Users/nshulga/git/pytorch/pytorch/torch/nn/modules/module.py", line 1318, in Module
def state_dict(self, *, prefix: str = ..., keep_vars: bool = ...) -> typing.OrderedDict[str, Tensor]:
AttributeError: module 'typing' has no attribute 'OrderedDict'
Same works fine if Python-3.7.2 or newer is used
Introduced by #72211
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high prioritymodule: python frontendFor issues relating to PyTorch's Python frontendFor issues relating to PyTorch's Python frontendmodule: regressionIt used to work, and now it doesn'tIt used to work, and now it doesn'ttriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module