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There are several potential solutions to address this issue:

  1. Verify that the file being loaded is indeed a pickled model file, and not corrupted or modified in any way. If the file is damaged, it may not be possible to load the model.

  2. Ensure that the module used to define the objects in the model is available and imported correctly. If the module is missing or not in the correct location, the pickle module may not be able to load the objects.

  3. Make sure that the same version of the module is used to load the pickled model as was used to save the model. If there are any differences in versions, the pickle module may not be able to properly deserialize the objects.

  4. Try using alternative serialization formats, such as joblib or TensorFlow's SavedModel format, if the pickle module continues to fail. These formats may offer better compatibility between Python environments and third-party libraries.

  5. If all else fails, consider retraining the model and saving it in a different format that can be more reliably loaded. This may be necessary if there are fundamental incompatibilities between the model and the Python environment it is being loaded into.