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Add more documentation to recommend users to set `drop_remainder=False` when exhausting the eval dataset in Orbit.

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Allow passing a `tf.distribute.InputOptions` to `orbit.make_distributed_dataset` through the `input_options` key word argument.

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Don't early return if the global step is not updated by `num_steps` as expected. This still allows summary and metric reporting.

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Use sparse_categorical_crossentropy for test as the loss object default does not work on tpustrategy +
the single task trainer already handles the reduction.

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In current implementation of StandardEvaluator, the iterator of evaluate dataset will go back (actually create a new iterator) to the beginning when evaluate() is called every time.

In some case, the iterator need to go ahead forever. So add the new option "recreate_iterator_for_each_eval" to control this behavior.

In some case, the iterator creation is time consuming. For example, careting an iterator of distributed dataset for multi worker in eager mode. So it's better to make a repeat dataset instead of recreating an iterator.

If "recreate_iterator_for_each_eval" is True, then the iterator will go back; Otherwise, keep going ahead.

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don't get "while/" as name prefix.

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We use kwargs for all arguments and enforce them should be kwargs.
Reorder args.

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