Clarifying global floating point policy

[Sinacam]

The issue of float precision affects many computations in tensorflow_ranking, such as

ranking/tensorflow_ranking/python/metrics_impl.py

Lines 603 to 628 in a928e2b

	def _compute_impl(self, labels, predictions, weights, mask):
	"""See `_RankingMetric`."""
	topn = tf.shape(predictions)[1] if self._topn is None else self._topn
	# Relevance = 1.0 when labels >= 1.0.
	relevance = tf.cast(tf.greater_equal(labels, 1.0), dtype=tf.float32)
	sorted_relevance, sorted_weights = utils.sort_by_scores(
	predictions, [relevance, weights], topn=topn, mask=mask)
	per_list_relevant_counts = tf.cumsum(sorted_relevance, axis=1)
	per_list_cutoffs = tf.cumsum(tf.ones_like(sorted_relevance), axis=1)
	per_list_precisions = tf.math.divide_no_nan(per_list_relevant_counts,
	per_list_cutoffs)
	total_precision = tf.reduce_sum(
	input_tensor=per_list_precisions * sorted_weights * sorted_relevance,
	axis=1,
	keepdims=True)
	# Compute the total relevance regardless of self._topn.
	total_relevance = tf.reduce_sum(
	input_tensor=weights * relevance, axis=1, keepdims=True)
	per_list_map = tf.math.divide_no_nan(total_precision, total_relevance)
	# per_list_weights are computed from the whole list to avoid the problem of
	# 0 when there is no relevant example in topn.
	per_list_weights = _per_example_weights_to_per_list_weights(
	weights, relevance)
	return per_list_map, per_list_weights

This has been mentioned before in #254, but I want to elaborate on our difficulties.
This type of hardcoded dtypes makes it extremely hard to move our programs to use float64.
For example, if we use tf.keras.backend.set_floatx('float64') anywhere, we get errors within tensorflow_ranking due to conflicting dtypes.

Will the global floating point policy (tf.keras.mixed_precision.set_global_policy and tf.keras.backend.floatx) be supported?
If the official stance on the global policy is to ignore it, can it be documented?