Return applied alpha for MixUp

[Dipet]

Example:

import albumentations as A
import numpy as np
from albumentations.core.types import ReferenceImage

reference_data = [ReferenceImage(image=np.empty([100, 100, 3], dtype=np.uint8)) for i in range(10)]
augmentation_cls = A.RandomRotate90
params = {}
mix_up = A.MixUp(p=1, reference_data=reference_data, read_fn=lambda x: x)

aug = A.Compose([augmentation_cls(**params), mix_up])

image = np.empty([100, 100, 3], dtype=np.uint8)
mask = np.empty([100, 100, 3], dtype=np.uint8)
global_label = ["A"]
data = aug(image=image, global_label=global_label, mask=mask)
print(data["mix_coef"]). # 0.9991580344142427

[sourcery-ai]

Hey @Dipet - I've reviewed your changes and they look great!

General suggestions:

• Consider simplifying the implementation to reduce complexity and improve maintainability.
• Ensure robustness by adding safeguards or documentation for accessing the 'mix_coef' in the 'params' dictionary.
• Review the necessity of the conditional check for 'mix_coef_return_name' to streamline the code.
• Evaluate the impact of the new feature on the class's design and usage, aiming for a balance between flexibility and simplicity.

Here's what I looked at during the review

• 🟡 General issues: 3 issues found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟡 Complexity: 1 issue found
• 🟢 Docstrings: all looks good

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