automatically on GitHub.

If you want to contribute, start working through the TensorFlow codebase,
navigate to the
[GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
looking through interesting issues. If you are not sure of where to start, then
start by trying one of the smaller/easier issues here i.e.

# limitations under the License.
# ==============================================================================
setup_file() {
    bazel version  # Start the bazel server
}

# Do a bazel query specifically for the licenses checker. It searches for
# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then

#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "Server functionality is not supported on mobile");
#else
  status->status = server->server->Start();
#endif  // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
}

void TF_ServerStop(TF_Server* server, TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)

# END TF TEST SUITE OPTIONS

# START CROSS-COMPILE CONFIGS
# Set execution platform to Linux x86
# Note: Lot of the "host_" flags such as "host_cpu" and "host_crosstool_top"
# flags seem to be actually used to specify the execution platform details. It

        ],
    ),
    deps = if_cuda([
        "//tensorflow:tensorflow_cc",
        "//tensorflow/python:pywrap_tensorflow_internal",
    ]),
)

# ** Targets for Windows build (start) **

# Build a shared library (DLL) by cc_binary from tf_custom_op_library_additional_deps_impl,
# it contains all object code from its dependencies.

    * Does not support gradients.
    * See `tf.types.experimental.AtomicFunction` documentation for how to call and use it.

*   `tf.data`:

    *   Moved option `warm_start` from `tf.data.experimental.OptimizationOptions` to `tf.data.Options`.

*   `tf.lite`:

    *   `sub_op` and `mul_op` support broadcasting up to 6 dimensions.

// for that Tensor). We also count, for each tape entry, how many of its output
// Tensors need gradients to be computed (Tensors which are not used do not need
// any gradients to be computed).
//
// Finally, we start a backprop stack with a set of tape entries for which we
// have all gradients available. This set usually is a subset of the set of
// targets (not all since targets which have outputs in the tape will not have