Make take_along_axis with TF backend compilable.
#21239
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When there are dynamic dimensions, like typically the batch size, `tf.broadcast_dynamic_shape` is not always compilable. Replace with an adhoc implementation for dynamic dimensions where we rely on the broadcast itself to fail when the shapes are not broadcastable. Tested with https://github.com/keras-team/keras-rs/blob/main/examples/listwise_ranking.py on GPU as I was not able to distill a simple reproduction of this.
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fchollet
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Change keras-team#21239 broke one use case when the axis dimension is dynamic, the type of the indices is not int32, and the op is run in graph mode. Note that the additional unit tests don't actually cover this.
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Change #21239 broke one use case when the axis dimension is dynamic, the type of the indices is not int32, and the op is run in graph mode. Note that the additional unit tests don't actually cover this.
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When there are dynamic dimensions, like typically the batch size,
tf.broadcast_dynamic_shapeis not always compilable.Replace with an adhoc implementation for dynamic dimensions where we rely on the broadcast itself to fail when the shapes are not broadcastable.
Tested with https://github.com/keras-team/keras-rs/blob/main/examples/listwise_ranking.py on GPU as I was not able to distill a simple reproduction of this.