Test case that can only run in v1 has bug attached.

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Similar to cl/198786266 specify the `maximum_iterations` to tf.while_loop in tf.foldl and tf.foldr to be compatible with XLA.

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Eager function: Do not create a set of input ops each time. This can take a very long time for big models.
For e.g. when building a function for ResNet50, this increased the time to create the eager function by 72 times.

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Move reduce non distributed values and share the code with TPU Strategy and also improve print output of TPUMirroredVariable.

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the FuncGraph.as_default scope instead of __init__. Fixes issues with the
grobal Keras FuncGraph keeping state between tests.

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Adds some explanation of this in the docstring and some better exceptions. Having it non-experimental would be pretty confusing, since most users would try it without enable_eager_execution() and run into strange errors which we don't plan to fix.

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Copies and pastes the existing Optimizer checkpointing code, and stops adding unconditional dependencies on slot variables (which were based on ops.uid() and so not reproducible across program runs).

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custom op, it is up to the tf-lite user to provide the implementation. Best to
assume it exists so the user can implement.

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Reduce the cost of serializing ConversionOptions to code, by using a more efficient inspect.util.getqualifiedname, reducing its max_depth and falling back to caching the value in the namespace. The latter step makes it more difficult to run the generated code afterwards, but it should in turn speed up the conversion process. This also adds an extra check to tf_decorator to improve robustness.

PiperOrigin-RevId: 225226256

These tests share the same assertion: that weighting a particular class's loss over other classes (by passing in `sample_weight` into `model.fit`) leads to a lower evaluation loss when evaluating test data limited to that class compared to evaluating all test data. My theory is that the models in these tests are not trained enough for that assumption to always hold true, which is why they are flaky. Increased the weight from 2 to 10 and the training epochs from 5 to 10.

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1. Only MIN_COMBINED mode is supported;
2. Reshape the output to [d0,..., dn * unpack_size] if input shape is [d0, ..., dn].
3. Only uint32 is supported for the input;
4. Output data type is bfloat16;
5. Only uint8 or uint16 is supported for the original unpacked input.

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This contrain was originally added due to the different weights format issue
between canonical and cudnn (extra input bias). Now since the input bias is
feeded as zeros in cudnn mode, and weights are unified into one format. Having
bias regularizer should not be a issue.

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Removing the LoopCond of a while_loop can cause the partitioner to fail with:
  A cross-device loop must have a pivot predicate

For some reason this only triggers with while_v2 (the lowered while
loop is slightly different than what would be produced by the original
while_loop).

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In addition, fix a few eval() calls as well as remove some
@test_util.run_v1_only annotations.

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Format in the previous state didn't give the timezone.

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Remove :android_tensorflow_lib_selective_registration* aliases, targets using selective registration can now use the :android_tensorflow_lib_lite* targets.
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