Add binary focal cross entropy loss function

[ZJUGuoShuai]

Proposed changes

This PR adds the Binary Focal Cross-entropy Loss, which is introduced by Lin et al., 2018 and often used in object detection tasks.

The interface is nearly the same with TensorFlow's tf.keras.losses.BinaryFocalCrossentropy and the test ground-truth values are calculated by it to ensure correctness.

This implementation supports both logits and probabilities as input (like the normal binary_cross_entropy). It also supports class balancing in Lin et al., 2018, like TF's BinaryFocalCrossentropy.

Checklist

Put an x in the boxes that apply.

• I have read the CONTRIBUTING document
• I have run pre-commit run --all-files to format my code / installed pre-commit prior to committing changes
• I have added tests that prove my fix is effective or that my feature works
• I have updated the necessary documentation (if needed)

[awni]

Thanks for the contribution! We decided against adding this to MLX core in #336 and #324 as it's a bit niche. We may revisit in the future.

For future reference, it's a good idea to open an issue about possible new features before or in tandem to starting a PR so we can let you know if there is ongoing work / plans already / not for core.