private static void testReverse(double[] input, double[] expectedOutput) {
    input = Arrays.copyOf(input, input.length);
    Doubles.reverse(input);
    assertThat(input).isEqualTo(expectedOutput);
  }

  private static void testReverse(
      double[] input, int fromIndex, int toIndex, double[] expectedOutput) {
    input = Arrays.copyOf(input, input.length);
    Doubles.reverse(input, fromIndex, toIndex);

  }

  static void testSort(byte[] input, byte[] expected) {
    input = Arrays.copyOf(input, input.length);
    UnsignedBytes.sort(input);
    assertThat(input).isEqualTo(expected);
  }

  static void testSort(byte[] input, int from, int to, byte[] expected) {
    input = Arrays.copyOf(input, input.length);
    UnsignedBytes.sort(input, from, to);
    assertThat(input).isEqualTo(expected);
  }

# Separate OpenAPI Schemas for Input and Output or Not

When using **Pydantic v2**, the generated OpenAPI is a bit more exact and **correct** than before. 😎

In fact, in some cases, it will even have **two JSON Schemas** in OpenAPI for the same Pydantic model, for input and output, depending on if they have **default values**.

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output

  }

  private static void testReverse(char[] input, char[] expectedOutput) {
    input = Arrays.copyOf(input, input.length);
    Chars.reverse(input);
    assertThat(input).isEqualTo(expectedOutput);
  }

  private static void testReverse(char[] input, int fromIndex, int toIndex, char[] expectedOutput) {
    input = Arrays.copyOf(input, input.length);
    Chars.reverse(input, fromIndex, toIndex);

        dummy += ORDERING.immutableSortedCopy(input).get(0);
      }
    }
    return dummy;
  }

  @Benchmark
  int sortedSet(int reps) {
    int dummy = 0;
    if (mutable) {
      for (int i = 0; i < reps; i++) {
        dummy += new TreeSet<Integer>(input).first();
      }
    } else {
      for (int i = 0; i < reps; i++) {
        dummy += ImmutableSortedSet.copyOf(input).first();
      }
    }

    false

-   type: input id: Mobile attributes: label: Mobile device description:
    placeholder: e.g., Linux Ubuntu 16.04 validations: required: false

-   type: input id: Python attributes: label: Python version description:
    placeholder: e.g., 3.9 validations: required: false

-   type: input id: Bazel attributes: label: Bazel version description: if

  }

  private static void assertMessageDigestHashing(byte[] input, String algorithmName) {
    try {
      MessageDigest digest = MessageDigest.getInstance(algorithmName);
      assertEquals(
          HashCode.fromBytes(digest.digest(input)), ALGORITHMS.get(algorithmName).hashBytes(input));
      for (int bytes = 4; bytes <= digest.getDigestLength(); bytes++) {
        assertEquals(

            field=param,
            schema_generator=schema_generator,
            model_name_map=model_name_map,
            field_mapping=field_mapping,
            separate_input_output_schemas=separate_input_output_schemas,
        )
        parameter = {
            "name": param.alias,
            "in": field_info.in_.value,
            "required": param.required,
            "schema": param_schema,

// Creates parameters (placeholders) in the tracing `ctx` using the shape and
// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params);

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,

    // the output, so the input must be at least 32 bits, since the output has to be that long
    assertPutString(new char[] {'p', HashTestUtils.randomLowSurrogate(new Random())});
  }

  public void testPutStringWithHighSurrogate() {
    // we pad because the dummy hash function we use to test this, merely copies the input into