# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.
# See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation
build --distinct_host_configuration=false

# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.
# See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation
build --distinct_host_configuration=false

      environ_cp,
      var_name='GCC_HOST_COMPILER_PATH',
      var_default=default_gcc_host_compiler_path,
      ask_for_var='Please specify which gcc should be used by nvcc as the host '
      'compiler.',
      check_success=os.path.exists,
      resolve_symlinks=True,
      error_msg='Invalid gcc path. %s cannot be found.',
  )

  write_action_env_to_bazelrc('GCC_HOST_COMPILER_PATH', gcc_host_compiler_path)

# 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
# seems it is this way because these flags are old and predate the distinction
# between host and execution platform.
build:cross_compile_base --host_cpu=k8

# limitations under the License.
# ==============================================================================


# This Workflow updates tensorflow/tools/toolchains/remote_config/configs.bzl
# to reference the most recent versions of the SIG Build Docker images.
name: Update RBE Configs
on:
  workflow_dispatch:

permissions:
  contents: read

jobs:
  rbe:
    name: Update RBE Configs
    runs-on: ubuntu-latest

      maintains a hash table to track the most up-to-date vocabulary based on
      the inputs received by the layer and the eviction policy. When this layer
      is used with an `UpdateEmbeddingCallback`, which is a time-based callback,
      the vocabulary lookup tensor is updated at the time interval set in the
      `UpdateEmbeddingCallback` based on the most up-to-date vocabulary hash

*   Include unit tests when you contribute new features, as they help to a)
    prove that your code works correctly, and b) guard against future breaking
    changes to lower the maintenance cost.
*   Bug fixes also generally require unit tests, because the presence of bugs
    usually indicates insufficient test coverage.
*   Keep API compatibility in mind when you change code in core TensorFlow,
    e.g., code in

    int num_dims, void* data, size_t len,
    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status);

// Retrieves the address space (i.e. job, replia, task) of the local host and
// saves it in the buffer.
TF_CAPI_EXPORT extern void TFE_HostAddressSpace(TFE_Context* ctx,
                                                TF_Buffer* buf);

  echo "More than one wheel file is present: moving the oldest to"
  echo "$TFCI_OUTPUT_DIR/extra_wheels."
  # List all .whl files by their modification time (ls -t) and move anything
  # other than the most recently-modified one (the newest one).
  mkdir -p $TFCI_OUTPUT_DIR/extra_wheels
  ls -t *.whl | tail -n +2 | xargs mv -t $TFCI_OUTPUT_DIR/extra_wheels
fi

# Repair wheels with auditwheel and delete the old one.

}

void ReplaceTaskInServerDef(tensorflow::ServerDef* server_def, int task_index,
                            const string& host, int port) {
  tensorflow::JobDef* job_def = server_def->mutable_cluster()->mutable_job(0);
  job_def->mutable_tasks()->at(task_index) =
      tensorflow::strings::StrCat(host, ":", port);
}

std::vector<std::string> ListDeviceNames(TFE_Context* ctx) {
  TF_Status* status = TF_NewStatus();