Do not use reduce_sum before returning to loss wrapper.

[manuelbre]

Description

Brief Description of the PR:

Handle reduction with loss wrapper not with this function.
Currently when using tfa.losses.SigmoidFocalCrossEntropy(reduction: str = tf.keras.losses.Reduction.NONE), the loss is still reduced by summing over the last axis. I would expect tfa.losses.SigmoidFocalCrossEntropy(reduction: str = tf.keras.losses.Reduction.NONE) to return a loss of the same shape as y_pred which is currently not the case.

Type of change

• [ x] Bug fix
• New Tutorial
• Updated or additional documentation
• Additional Testing
• New Activation and the changes conform to the activation contribution guidelines
• New Callback and the changes conform to the callback contribution guidelines
• New Image addition and the changes conform to the image op contribution guidelines
• New Layer and the changes conform to the layer contribution guidelines
• New Loss and the changes conform to the loss contribution guidelines
• New Metric and the changes conform to the metric contribution guidelines
• New Optimizer and the changes conform to the optimizer contribution guidelines
• New RNN Cell and the changes conform to the rnn contribution guidelines
• New Seq2seq addition and the changes conform to the seq2seq contribution guidelines
• New Text addition and the changes conform to the text op contribution guidelines

Checklist:

• I've properly formatted my code according to the guidelines
  • By running Black + Flake8
  • By running pre-commit hooks
• This PR addresses an already submitted issue for TensorFlow Addons
• I have made corresponding changes to the documentation
• I have added tests that prove my fix is effective or that my feature works
• This PR contains modifications to C++ custom-ops

How Has This Been Tested?

If you're adding a bugfix or new feature please describe the tests that you ran to verify your changes:
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[bot-of-gabrieldemarmiesse]

@AakashKumarNain @SSaishruthi

You are owners of some files modified in this pull request.
Would you kindly review the changes whenever you have the time to?
Thank you very much.