Avoid empty lists on missing BathNorm2d models as in #7375

🛠️ PR Summary

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🌟 Summary

Refactor optimizer parameter grouping for increased clarity in train.py.

📊 Key Changes

• Simplified the variable naming for optimizer parameter groups from g0, g1, g2 to a single list g.
• Changed parameter group assignments within the optimizer initialization to refer to the new g list indices.
• Updated the parameter group addition to the optimizer to match the new grouping method.
• Clarified logging output to reflect these changes by adjusting the counts for each parameter group.

🎯 Purpose & Impact

• Purpose: To streamline the readability and maintenance of the optimizer parameter grouping by using a single list with indices rather than separate variables.
• Potential Impact: This change should make the code more maintainable and understandable for developers contributing to or reviewing the train.py script, without affecting the end users' experience or the functionality of the training process.