defer env.LockSetEnv()()

	// Disable merging env values with config for validation.
	env.SetEnvOff()

	// Enable env values to validate KMS.
	defer env.SetEnvOn()
	if subSys != "" {
		return validateSubSysConfig(ctx, s, subSys, objAPI)
	}

	// No sub-system passed. Validate all of them.
	for _, ss := range config.SubSystems.ToSlice() {
		if err := validateSubSysConfig(ctx, s, ss, objAPI); err != nil {
			return err

        when(otherContext.getConfig()).thenReturn(otherConfig);
        assertFalse(response.canReuse(otherContext, false));
    }

    @Test
    @DisplayName("Should validate response correctly")
    void testIsValid() throws Exception {
        // Setup valid response
        setResponseAsReceived(response);
        setPrivateField(response, "dialectRevision", 0x0311);

## Results { #results }

With just that Python type declaration, **FastAPI** will:

* Read the body of the request as JSON.
* Convert the corresponding types (if needed).
* Validate the data.
    * If the data is invalid, it will return a nice and clear error, indicating exactly where and what was the incorrect data.
* Give you the received data in the parameter `item`.

			// mime package in MinIO, which correctly encodes strings with fixed B/Q encodings,
			// avoiding S3’s heuristic-driven issues.
			//
			// For MinIO developers, decode S3 metadata with mime.WordDecoder, validate outputs,
			// report encoding bugs to AWS, and use ASCII-only metadata to ensure reliable S3 API
			// compatibility.
			if needsMimeEncoding(v) {

			[]byte("10000;chunk-signature=ad80c730a21e5b8d04586a2213dd63b9a0e99e0e2307b0ade35a65485a288648    \t \n"),
			[]byte("10000"),
			[]byte("ad80c730a21e5b8d04586a2213dd63b9a0e99e0e2307b0ade35a65485a288648"),
		},
	}
	// Validate chunk extension removal.
	for i, tt := range tests {
		// Extract chunk size and chunk signature after parsing a standard chunk-extension format.
		hexChunkSize, hexChunkSignature := parseS3ChunkExtension(tt.buf)

		return arn, err
	}

	if ps[2] != string(arnServiceIAM) {
		err = errors.New("invalid ARN - bad service field")
		return arn, err
	}

	// ps[3] is region and is not validated here. If the region is invalid,
	// the ARN would not match any configured ARNs in the server.
	if ps[4] != "" {
		err = errors.New("invalid ARN - unsupported account-id field")
		return arn, err
	}

	// Setup and capture test results for directory with files and symlink.
	testResults = append(testResults, setupTestReadDirSymlink(t)...)

	// Remove all dirs once tests are over.
	defer teardown(testResults)

	// Validate all the results.
	for _, r := range testResults {
		if entries, err := readDir(r.dir); err != nil {
			t.Fatal("failed to run test.", err)
		} else {
			// Keep entries sorted for easier comparison.

You declare the "shape" of the data as classes with attributes.

And each attribute has a type.

Then you create an instance of that class with some values and it will validate the values, convert them to the appropriate type (if that's the case) and give you an object with all the data.

And you get all the editor support with that resulting object.

An example from the official Pydantic docs:

    // ===================================================================================
    // Search Execute
    //

    /**
     * Performs bulk document operations (index multiple documents).
     * Validates document fields and adds default values where necessary.
     *
     * @param body the bulk request body containing documents to process
     * @return JSON response with bulk operation results
     */

          @Override
          public Integer getIfPresent(Object key) {
            throw new UnsupportedOperationException();
          }

          @Override
          public void invalidate(Object key) {
            invalidated.add(key);
          }
        };

    List<Integer> toInvalidate = ImmutableList.of(1, 2, 3, 4);
    cache.invalidateAll(toInvalidate);