message: Adding Deterministic Op Functionality
  Adding Deterministic Op Functionality
diff: the MLIR bridge in a \"safe\" mode. This runs the MLIR bridge in a
  FallbackEnabled mode when an analysis of the graph determines
  that the graph does not have unsupported features.
  +* Deterministic Op Functionality:
  + * Add determinism-unimplemented exception-throwing to the segment-sum ops.
  + When the environment variable `TF_DETERMINISTIC_OPS` is set to `"true"`
  + or `"1"` (when op-determinism is expected), an attempt to run the
  + folowing ops on a GPU will throw `tf.errors.UnimplementedError` (with an
  + understandable message) when `data` is a floating-point type, including
  + complex types (if supported): `tf.math.segment_prod`,
  + `tf.math.segment_sum`, `tf.math.unsorted_segment_mean`,
  + `tf.math.unsorted_segment_sqrt_n`, `tf.math.unsorted_segment_prod`,
  + `tf.math.unsorted_segment_sum`, and therefore also
  + `tf.convert_to_tensor` when `value` is of type `tf.IndexedSlices` (such
  + as in the backprop though `tf.gather` into a dense embedding). See
  + issue [39751](https://github.com/tensorflow/tensorflow/issues/39751)
  + which this change addresses, but does not solve. This exception-throwing
  + behavior can be disabled by setting the environment variable
  + `TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS` to `"true"` or
  + `"1"`. For more information about these changes, see the description in
  + pull request
  + [47772](https://github.com/tensorflow/tensorflow/pull/47772).
  + * In previous versions of TensorFlow, when a GPU was available,
  + `tf.sparse.sparse_dense_matmul` introduced truly random noise in the
  + forward path for data of type `tf.float32` but not for data of type
  + `tf.float64` (for which there was no GPU implementation). In this
  + current release, GPU support for other floating-point types
  + (`tf.float16`, `tf.float64`, `tf.complex64`, and `tf.complex128`) has
  + been added for this op. If you were relying on the determinism of the
  + `tf.float64` CPU implementation being automatically selected because of
  + the absence of the `tf.float64` GPU implementation, you with either
  + need to force the op to run on the CPU or use a different data type.
  * Other
  * Added `show_debug_info` to `mlir.convert_graph_def` and
  