```bash
    docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10
    ```

    Once you have the packages installed, you can run a specific unit test in
    bazel by doing as follows:

    ```bash
    export flags="--config=opt -k"
    ```

    If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and
    add the `cuda` option flag

*   TensorBoard fixes: graphs with only one data point, Nan values, reload
    button and auto-reload, tooltips in scalar charts, run filtering, stable
    colors
*   Tensorboard graph visualizer now supports run metadata. Clicking on nodes
    while viewing a stats for a particular run will show runtime statistics,
    such as memory or compute usage. Unused nodes will be faded out.

## Thanks to our Contributors

        ":fuchsia",
        ":emscripten",
        ":ios",
    ],
    visibility = ["//visibility:public"],
)

# This flag disables all google production dependencies, intended for
# applications run with non-prod environment.
# TODO(timshen): Currently this option only disables some dependencies.
# See b/122528503.
# copybara:uncomment_begin(google-only)
# bool_flag(
#     name = "tf_no_prod_deps",

# Linux x86 so that we can use RBE. Since tests still need to run on the single
# host Arm64 machine, the build becomes too slow (~30 min) to be a presubmit.
# For testing purposes, we want to see the runtime performance of an
# experimental job that is build-only, i.e, we only build the test targets and
# do not run them. By prefixing the configs with "build", we can run both