sample_weight parameter occurs Rank Error when TripletSemiHardLoss is used in multi-output model.

[ubless607]

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04.3 LTS
• TensorFlow version and how it was installed (source or binary): 2.9.1 (pypi)
• TensorFlow-Addons version and how it was installed (source or binary): 0.17.1 (pypi)
• Python version: 3.9.13
• Is GPU used? (yes/no): yes

Describe the bug

A clear and concise description of what the bug is.

When TripletSemiHardLoss is used as a loss function, using sample_weight parameter doesn't occur any error.
However, when TripletSemiHardLoss is used in multi-output model, using sample_weight parameter occurs Rank Error.

Code to reproduce the issue

Provide a reproducible test case that is the bare minimum necessary to generate the problem.

model.compile(
    optimizer=tf.keras.optimizers.Adam(0.001),
    loss={'embedding_output': tfa.losses.TripletSemiHardLoss(),
          'clf_output': 'sparse_categorical_crossentropy'})

Since class_weight doesn't work in TF2.1+ for multi-output, I applied class weighting using _make_class_weight_map_fn in keras.engine by mapping the function.

model.fit() gives the following error. TripletSemiHardLoss with sample_weight in single-output model works fine, other tensorflow-implemeneted loss functions in multi-output model with sample_weight also works. But when TripletSemiHardLoss and other loss function with sample_weight option gives the following error:

ValueError: Shapes must be equal rank, but are 2 and 0
    	From merging shape 0 with other shapes. for '{{node AddN}} = AddN[N=2, T=DT_FLOAT](TripletSemiHardLoss/weighted_loss/Mul, sparse_categorical_crossentropy/weighted_loss/value)' with input shapes: [?,1], [].

Other info / logs

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