Using import with multiple versions of a module with different structure

Question

Suppose a function was moved from one module to another between package versions, e.g. as in this question, and we'd like to support both versions in our client code, that imports this function. Should we simply check the version and compare it to the first major version where change happened, or is there a more elegant solution? I.e. something like this (in the context of the linked question):

import tensorflow
from packaging import version
if version.parse(tensorflow.__version__) >= version.parse("1.12"):
    from tensorflow.python.training import device_util
else:
    from tensorflow.python.distribute import device_util

ivan_pozdeev · Accepted Answer · 2019-06-03 12:34:10Z

1

Use an exception handler catching ImportError.

try:
    from tensorflow.python.training import device_util
except ImportError:
    # This method was moved in tensorflow 1.12
    from tensorflow.python.distribute import device_util

edited Jun 3, 2019 at 12:34

ivan_pozdeev

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answered Jun 3, 2019 at 11:30

Tim

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ivan_pozdeev · Accepted Answer · 2019-06-03 11:39:26Z

The standard way is to not check the version but rather if the characteristic you are interested in is present. This way, you are not tied to a particlar version line and provider. E.g.:

if hasattr(module, 'foo'):
    # new version
else:
    # old version


try:
    import tkinter
except ImportError:
    import Tkinter as tkinter


ironpython = hasattr(Exception, 'clsException')    #the feature that interests us
<...>
if ironpython: import System

Sometimes though, checking for the feature is hard, so to check for version is much easier:

# ABI config variables are introduced in PEP 425
if sys.version_info[:2] < (3, 2):
    import warnings
    warnings.filterwarnings('ignore', r"Config variable '[^']+' is unset, "
                                      r"Python ABI tag may be incorrect",
                            category=RuntimeWarning)

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