elem.tail = indent_str


def create_xml_file(result_store_dict: ResultDictType,
                    output_path: str,
                    verbose: bool = False):
  """Creates a JUnit-based XML file, with each invocation as a testcase."""
  os.makedirs(os.path.dirname(output_path), exist_ok=True)
  failure_count = 0
  error_count = 0

  date_time = datetime.datetime

opt<std::string> input_model(llvm::cl::Positional,
                             llvm::cl::desc("<input model path>"),
                             llvm::cl::Required);

// NOLINTNEXTLINE
opt<std::string> output_path("o", llvm::cl::desc("<output path>"),
                             llvm::cl::Required);

// NOLINTNEXTLINE
opt<std::string> export_type("export-type", llvm::cl::desc("<export type>"),

kube::golang::outfile_for_binary() {
  local binary=$1
  local platform=$2
  local output_path="${KUBE_GOPATH}/bin"
  local bin
  bin=$(basename "${binary}")
  if [[ "${platform}" != "${host_platform}" ]]; then
    output_path="${output_path}/${platform//\//_}"
  fi
  if [[ ${GOOS} == "windows" ]]; then
    bin="${bin}.exe"
  fi
  echo "${output_path}/${bin}"
}

# Argument: the name of a Kubernetes package.

Once you have a converted TfLite model ready, you can use the following command
to use TAC to optimize for your model:

```
bazel run -c opt //tensorflow/compiler/mlir/lite/experimental/tac:tac-translate -- <PATH_TO_YOUR_MODEL> -o=<OUTPUT_PATH> -device-specs=<HARDWARE_BACKENDS>
```

The devices_specs is a list of the names of the desired hardware backends,
separated by comma, e.g., "GPU,CPU".