*scanner
	emitDepth     int
	maxDepth      int
	emitKV        bool
	emitRecursive bool
	objectAsKVS   bool

	depth   int
	scratch *scratch
	metaCh  chan *MetaValue
	err     error

	// follow line position to add context to errors
	lineNo    int
	lineStart int64
}

// NewDecoder creates new Decoder to read JSON values at the provided
// emitDepth from the provider io.Reader.

        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
        # queries: security-extended,security-and-quality

    # If the analyze step fails for one of the languages you are analyzing with

/// tip | "Tipp"

Um dasselbe Problem zu lösen, ist es wahrscheinlich viel einfacher, den `body` in einem benutzerdefinierten Handler für `RequestValidationError` zu verwenden ([Fehlerbehandlung](../tutorial/handling-errors.md#den-requestvalidationerror-body-verwenden){.internal-link target=_blank}).

Dieses Beispiel ist jedoch immer noch gültig und zeigt, wie mit den internen Komponenten interagiert wird.

///

  //   2. If ALL matchConditions evaluate to TRUE, the webhook is called.
  //   3. If any matchCondition evaluates to an error (but none are FALSE):
  //      - If failurePolicy=Fail, reject the request
  //      - If failurePolicy=Ignore, the error is ignored and the webhook is skipped
  //
  // This is an alpha feature and managed by the AdmissionWebhookMatchConditions feature gate.
  //

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

  // Reason is optional.  It indicates why a request was allowed or denied.
  // +optional
  optional string reason = 2;

  // EvaluationError is an indication that some error occurred during the authorization check.
  // It is entirely possible to get an error and be able to continue determine authorization status in spite of it.
  // For instance, RBAC can be missing a role, but enough roles are still present and bound to reason about the request.

  // still succeed if there are no other errors, and will only persist
  // the last of any duplicate fields. This is the default in v1.23+
  // - Strict: This will fail the request with a BadRequest error if
  // any unknown fields would be dropped from the object, or if any
  // duplicate fields are present. The error returned from the server
  // will contain all unknown and duplicate fields encountered.

	if err != nil {
		if list, ok := err.(scanner.ErrorList); ok {
			// If err is a scanner.ErrorList, its String will print just
			// the first error and then (+n more errors).
			// Instead, turn it into a new Error that will return
			// details for all the errors.
			for _, e := range list {
				fmt.Fprintln(os.Stderr, e)
			}
			os.Exit(2)
		}
		fatalf("parsing %s: %s", name, err)
	}
	return ast1
}

                    .desc("Quiet output - only show errors")
                    .build());
            options.addOption(Option.builder(VERBOSE)
                    .longOpt("verbose")
                    .desc("Produce execution verbose output")
                    .build());
            options.addOption(Option.builder(SHOW_ERRORS)
                    .longOpt("errors")
                    .desc("Produce execution error messages")

  TestRemoteExecuteUpdateServerDefResourceAccess(true);
}

void TestRemoteExecuteUpdateServerDefWithFailures(bool async) {
  // Fail fast on GetStatus requests so we can get errors instead of timeout
  // when updating cluster with non-exsitent worker
  tensorflow::setenv("GRPC_FAIL_FAST", "TRUE", /*overwrite=*/1);

  tensorflow::ServerDef server_def = GetServerDef(2);