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A recent change modified the behavior of `FunctionLibraryRuntimeImpl::ReleaseHandle()`
so that it no longer freed the memory associated with an instantiated function. Since
we rely on instantiating and releasing a potentially large number of instances of the
same function in tf.data to isolate the (e.g. random number generator) state in each
instance, this change meant that the memory consumption could grow without bound in
a simple program like:
```python
ds = tf.data.Dataset.from_tensors(0).repeat(None)
# The function `lambda y: y + 1` would be instantiated for each element in the input.
ds = ds.flat_map(lambda x: tf.data.Dataset.from_tensors(x).map(
lambda y: y + tf.random_uniform([], minval=0, maxval=10, dtype=tf.int32)))
iterator = ds.make_one_shot_iterator()
next_elem = iterator.get_next()
with tf.Session() as sess:
while True:
sess.run(next_elem)
```
PiperOrigin-RevId: 195983977
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A. Unique TensorFlower
Derek Murray