self.create_tempfile(file_path='dir_a/w.txt', content='abcd')

    self.path_b = self.create_tempdir('dir_b').full_path
    self.create_tempfile(file_path='dir_b/x.txt', content='1234')
    self.create_tempfile(file_path='dir_b/y.txt', content='56')
    self.create_tempfile(file_path='dir_b/z.txt', content='78')

  def test_get_dir_size(self):
    self.assertEqual(testing.get_dir_size(self.path_a), 4)

	"CVTSS2SQ",
	"CVTTPD2PL",
	"CVTTPS2PL",
	"CVTTSD2SL",
	"CVTTSD2SQ",
	"CVTTSS2SL",
	"CVTTSS2SQ",
	"CWD",
	"CWDE",
	"DAA",
	"DAS",
	"DECB",
	"DECL",
	"DECQ",
	"DECW",
	"DIVB",
	"DIVL",
	"DIVPD",
	"DIVPS",
	"DIVQ",
	"DIVSD",
	"DIVSS",
	"DIVW",
	"DPPD",
	"DPPS",
	"EMMS",
	"ENTER",
	"EXTRACTPS",
	"F2XM1",
	"FABS",
	"FADDD",
	"FADDDP",

    if (output_type.getRank() != 4)
      return op.emitOpError()
             << "requires output to be a 4D tensor, but got " << output_type;

    auto static_dims = [](int64_t dim_a, int64_t dim_b) {
      return dim_a != ShapedType::kDynamic && dim_b != ShapedType::kDynamic;
    };

    auto output_shape = output_type.getShape();

    // output batch = input batch / (block_size * block_size).
    int64_t input_batch = input_shape[0];