text: Title: Tensorflow Variable Aggregation testing in Keras 3.7.0
  Body: Forking this from: https://github.com/keras-team/keras/issues/20568
  
  Specifically tagging @james77777778.
  
  tl; dr: It's `SUM`.
  
  ------
  
  The recommendation was: 
  
  > Since there is no reproducible script for debugging, this is a random guess:
  > Before https://github.com/keras-team/keras/commit/28d39c0cc766767f4db54edc8b8ce68d3a05d4b4, the aggregation behavior might have been broken due to incorrect propagation of the aggregation attr to the variables.
  Essentially, the training would be an aggregation=None setting (the default value for tf.Variable), which is likely incorrect.
  >
  >@das-apratim could you first try training the model without using tf.distribute.MirroredStrategy() to check if any NaNs occur?
  >
  >If the training runs without issues, try adding back tf.distribute.MirroredStrategy() and modifying _map_aggregation in keras/src/backend/tensorflow/core.py as follows:
  
          mapping = {
              "none": tf.VariableAggregation.NONE,
              "sum": tf.VariableAggregation.NONE,
              "mean": tf.VariableAggregation.NONE,
              "only_first_replica": tf.VariableAggregation.NONE,
          }
  
  >This adjustment reflects the behavior in Keras 3.6. See if the training runs well with this change.
  >
  >If it does, incrementally restore the original mapping to identify which key is causing the issue. Here's a general guideline:
  >
  >    "sum" is associated with metrics.
      "mean" is associated with model/optimizer weights.
      "only_first_replica" is associated with counters.
  
  I can make the following report for you:
  
  ```
  NONE, NONE, NONE,  NONE -- okay
  NONE,  SUM, NONE,  NONE -- fail, metrics nan first. Regression metrics go inf, "counting" metrics go nan; 123/341
  NONE, NONE, MEAN,  NONE -- okay
  NONE, NONE, NONE, FIRST -- okay
  NONE, NONE, MEAN, FIRST -- okay
  ```
  
  If it matters at all, this was using a Logical split of an RTX A6000 Ada for testing.