message: Enhance description of TF_DETERMINISTIC_OPS in version 2.1 release notes.
diff: @@ -19,7 +19,7 @@ TensorFlow 2.1 will be the last TF release supporting Python 2. Python 2 support
  * Changes rebatching for `tf.data datasets` + distribution strategies for better performance. Note that the dataset also behaves slightly differently, in that the rebatched dataset cardinality will always be a multiple of the number of replicas.
  * `TensorRT`
  * [TensorRT 6.0](https://developer.nvidia.com/tensorrt#tensorrt-whats-new) is now supported and enabled by default. This adds support for more TensorFlow ops including Conv3D, Conv3DBackpropInputV2, AvgPool3D, MaxPool3D, ResizeBilinear, and ResizeNearestNeighbor. In addition, the TensorFlow-TensorRT python conversion API is exported as `tf.experimental.tensorrt.Converter`.
  - * Environment variable `TF_DETERMINISTIC_OPS` added. When set to "true" or "1", this environment variable makes `tf.nn.bias_add` operate deterministically (i.e. reproducibly). It also makes cuDNN convolution and max-pooling operate deterministically. This makes Keras Conv2D, Conv3D, MaxPool2D, and MaxPool3D layers operate deterministically in both the forward and backward directions.
  + * Environment variable `TF_DETERMINISTIC_OPS` added. When set to "true" or "1", this environment variable makes `tf.nn.bias_add` operate deterministically (i.e. reproducibly) when XLA JIT compilation is *not* enabled. It also makes cuDNN convolution and max-pooling operate deterministically. This makes Keras Conv*D and MaxPool*D layers operate deterministically in both the forward and backward directions when running on CUDA-enabled GPU.
  ## Known issues
  Because of [issues with building on windows](https://github.com/tensorflow/tensorflow/issues/10521), we turned off eigen strong inlining for the Windows builds. Windows binaries are expected to be slightly slower until the build issues are resolved.
  