Res<TensorOf<[TF_Float16, TF_Float32]>, [{A 1-D float tensor of shape `[M]` representing the corresponding
scores for each selected box, where `M <= max_output_size`.  Scores only differ
from corresponding input scores when using Soft NMS (i.e. when
`soft_nms_sigma>0`)}]>:$selected_scores,
    Res<TF_Int32Tensor, [{A 0-D integer tensor representing the number of valid elements in

- Scores from PodTopologySpreading have reduced differentiation as maxSkew increases. ([#90820](https://github.com/kubernetes/kubernetes/pull/90820), [@alculquicondor](https://github.com/alculquicondor)) [SIG Scheduling]

*   In `Attention` and `AdditiveAttention` layers, the `call()` method now
    accepts a `return_attention_scores` argument. When set to True, the layer
    returns the attention scores as an additional output argument.
*   Adds `tf.metrics.log_cosh` and `tf.metrics.logcosh` API entrypoints with the
    same implementation as their `tf.losses` equivalent.