setenv("TF_DUMP_GRAPH_PREFIX", testing::TmpDir().c_str(), 1);
  std::string filepath =
      testing::TmpDir() + "/" + mlir::TF::kStandardPipelineBefore + ".mlir";

  std::string output_dump = testing::TmpDir() + "/" + "output_dump.txt";

  TF_ASSERT_OK(mlir::TF::RunBridgeWithStandardPipeline(
      module_ref.get(),
      /*enable_logging=*/true, /*enable_inliner=*/false));

  std::string errorMessage;

      directory.

  Raises:
    ValueError iff the graph does not contain a valid signature or the file
    prefix tensor is not found in the graph.
  """
  create_empty_output_dir(output_dir)
  v1_builder = builder.SavedModelBuilder(output_dir)

  graph_def = _restore_output_tensor_names(graph_def)
  with session.Session(graph=ops.Graph()) as sess:
    importer.import_graph_def(graph_def, name='')

namespace tensorflow {
namespace generator {
namespace {

TEST(CppGeneratorTest, typical_usage) {
  string category = "testing";
  string name_space = "tensorflow::ops";
  string output_dir = "tensorflow/c/experimental/ops/gen/cpp/golden";
  string source_dir = "tensorflow";
  string api_dirs = "";
  std::vector<string> ops = {
      "Neg",        // Simple unary Op

    public static class TaskWithOutputDirs extends TaskWithAction {
        List<File> outputDirs;

        @Inject
        public TaskWithOutputDirs(List<File> outputDirs) {
            this.outputDirs = outputDirs;
        }

        @OutputDirectories
        public List<File> getOutputDirs() {
            return outputDirs;
        }
    }

      grab_profiles_from_component "${requested_profiles}" "${mem_pprof_flags}" "${binary}" "${tunnel_port}" "${path}/${node}/proxy" "${output_dir}/${component}" "${timestamp}"
    done
  else
    grab_profiles_from_component "${requested_profiles}" "${mem_pprof_flags}" "${binary}" "${tunnel_port}" "${path}" "${output_dir}/${component}" "${timestamp}"
  fi

      function.

  Raises:
    FileExistsError: Iff `output_dir` is not empty and `overwrite` is false.
  """
  dir_not_empty = (
      output_dir is not None
      and file_io.file_exists_v2(output_dir)
      and file_io.list_directory_v2(output_dir)
  )

  if dir_not_empty and not overwrite:
    raise FileExistsError(

  const OpDef* op_def;
  TF_RETURN_IF_ERROR(OpRegistry::Global()->LookUpOpDef(node_def.op(), &op_def));
  DataTypeVector input_dtys, output_dtys;
  TF_RETURN_IF_ERROR(InputTypesForNode(node_def, *op_def, &input_dtys));
  TF_RETURN_IF_ERROR(OutputTypesForNode(node_def, *op_def, &output_dtys));

  mlir::MLIRContext* context = tfr_module_.getContext();
  llvm::SmallVector<mlir::Type, 4> input_tys, output_tys;

                inputDirs.set(createDirs.outputDirs)
            }

            // Set values lazily
            createDirs.inputFile = layout.projectDirectory.file("dir-source.txt")
            createDirs.outputDirs.add(layout.buildDirectory.dir("dir1"))
            createDirs.outputDirs.add(layout.buildDirectory.dir("dir2"))

            buildDir = "output"
"""

        touch(task.outputDirs.get(0).getParentFile())

        when:
        execute(task)

        then:
        def e = thrown WorkValidationException
        validateException(task, e, cannotWriteToDir {
            property('outputDirs')
                .dir(task.outputDirs[0])
                .ancestorIsNotDirectory(task.outputDirs[0].parentFile)
                .includeLink()

// -> tfl.batch_matmul(mhlo.transpose(mhlo.reshape(operand)), ...).
// Note:
// 1) Reshape/transpose are inserted because tfl.BatchMatMul requires
// size(contracting_dimensions) = 1 and size(output_dim) = 1, whereas
// mhlo.dot_general has no such restriction.
// 2) Inserted mhlo.reshape/transpose will be legalized to tf.reshape/transpose
// in LegalizeHloToTf (then from tf to tfl later).