Imported scikitlearn pyobj becomes null in a module

When working with Julia, it is not uncommon to encounter issues with imported Python objects becoming null in a module. This can be frustrating, especially when you are relying on these objects for your code to run smoothly. However, there are several ways to solve this problem and ensure that your imported scikitlearn pyobj remains intact in your module.

Option 1: Import the Python module within the module

One way to solve this issue is to import the necessary Python module directly within your Julia module. By doing so, you ensure that the imported scikitlearn pyobj remains within the scope of your module and does not become null.


module MyModule
    using PyCall
    @pyimport scikitlearn as skl

    # Your code here
end

By importing the scikitlearn module within your Julia module, you can access the necessary pyobj without any issues.

Option 2: Pass the pyobj as an argument

Another way to solve this problem is to pass the imported scikitlearn pyobj as an argument to the functions or methods that require it. By doing so, you ensure that the pyobj remains intact and does not become null within the module.


module MyModule
    using PyCall
    @pyimport scikitlearn as skl

    function my_function(pyobj)
        # Your code here
    end
end

By passing the pyobj as an argument to your functions or methods, you can ensure that it remains intact and accessible within your module.

Option 3: Use a global variable

Lastly, you can solve this issue by using a global variable to store the imported scikitlearn pyobj. By doing so, you ensure that the pyobj remains accessible throughout your module and does not become null.


module MyModule
    using PyCall
    @pyimport scikitlearn as skl

    const pyobj = skl

    # Your code here
end

By using a global variable to store the pyobj, you can ensure that it remains intact and accessible within your module.

Out of the three options, the best solution depends on the specific requirements of your code and the structure of your module. However, option 1 (importing the Python module within the module) is generally considered the most reliable and recommended approach. This ensures that the imported scikitlearn pyobj remains within the scope of your module and does not become null.

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