*   Removed the following experimental tf.data optimization APIs:
        *   `tf.data.experimental.MapVectorizationOptions.*`
        *   `tf.data.experimental.OptimizationOptions.filter_with_random_uniform_fusion`
        *   `tf.data.experimental.OptimizationOptions.hoist_random_uniform`
        *   `tf.data.experimental.OptimizationOptions.map_vectorization` *

from the value obtained by executing and rounding the instructions individually.
An explicit floating-point type <a href="#Conversions">conversion</a> rounds to
the precision of the target type, preventing fusion that would discard that rounding.
</p>

<p>
For instance, some architectures provide a "fused multiply and add" (FMA) instruction

from the value obtained by executing and rounding the instructions individually.
An explicit <a href="#Numeric_types">floating-point type</a> <a href="#Conversions">conversion</a> rounds to
the precision of the target type, preventing fusion that would discard that rounding.
</p>

<p>
For instance, some architectures provide a "fused multiply and add" (FMA) instruction