println(failure)
      }
      throw failures.first()
    }
  }

  private fun testToAscii(
    input: String,
    output: String?,
    comment: String?,
  ) {
    val url = "https://$input/".toHttpUrlOrNull()
    assertThat(url?.host, name = comment ?: input).isEqualTo(output)
  }

                    "msg": "Field required",
                    "input": None,
                },
                {
                    "type": "int_parsing",
                    "loc": ["query", "skip"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "a",
                },
                {
                    "type": "int_parsing",

		}
	}
	_ = w.Flush()
	return nil
}

func parseClusterStatuses(input map[string][]byte) (map[string][]cluster.DebugInfo, error) {
	statuses := make(map[string][]cluster.DebugInfo, len(input))
	for istiodKey, bytes := range input {
		var parsed []cluster.DebugInfo
		if err := json.Unmarshal(bytes, &parsed); err != nil {
			return nil, err
		}

  private static final class RoundToDoubleTester {
    private final BigDecimal input;
    private final Map<RoundingMode, Double> expectedValues = new EnumMap<>(RoundingMode.class);
    private boolean unnecessaryShouldThrow = false;

    RoundToDoubleTester(BigDecimal input) {
      this.input = input;
    }

    RoundToDoubleTester setExpectation(double expectedValue, RoundingMode... modes) {

                    "loc": ["query", "token"],
                    "msg": "Field required",
                    "input": None,
                },
                {
                    "type": "missing",
                    "loc": ["header", "x-token"],
                    "msg": "Field required",
                    "input": None,
                },
            ]
        }
    ) | IsDict(

	if flag.NArg() == 0 {
		flag.Usage()
	}

	// Flag refinement.
	if *OutputFile == "" {
		if flag.NArg() != 1 {
			flag.Usage()
		}
		input := filepath.Base(flag.Arg(0))
		input = strings.TrimSuffix(input, ".s")
		*OutputFile = fmt.Sprintf("%s.o", input)
	}

                    "loc": ["body", "item"],
                    "msg": "Field required",
                    "input": None,
                },
                {
                    "type": "missing",
                    "loc": ["body", "user"],
                    "msg": "Field required",
                    "input": None,
                },
                {
                    "type": "missing",

                        "msg": "Input should be less than or equal to 100",
                        "input": "150",
                        "ctx": {"le": 100},
                    },
                    {
                        "type": "greater_than_equal",
                        "loc": ["query", "offset"],
                        "msg": "Input should be greater than or equal to 0",

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will

        String tokenPattern = "name=\"authenticity_token\" +value=\"([^\"]+)\"";
        String content = "<input name=\"authenticity_token\" value=\"abcdefg\">";
        assertEquals("abcdefg", formScheme.getTokenValue(tokenPattern, content));

        tokenPattern = "name=\"authenticity_token\" +value=\"[^\"]+\"";
        content = "<input name=\"authenticity_token\" value=\"abcdefg\">";
        assertNull(formScheme.getTokenValue(tokenPattern, content));