"content": {"application/json": {"schema": {}}},
                        },
                        "422": {
                            "description": "Validation Error",
                            "content": {
                                "application/json": {
                                    "schema": {

                    "schema": {
                        "$ref": "#/components/schemas/Item"
                    }
                }
            }
        },
        "422": {
            "description": "Validation Error",
            "content": {
                "application/json": {
                    "schema": {
                        "$ref": "#/components/schemas/HTTPValidationError"
                    }
                }

                                }
                            },
                        },
                        "422": {
                            "description": "Validation Error",
                            "content": {
                                "application/json": {
                                    "schema": {

	}

	if policies[0] != "consoleAdmin" {
		t.Fatalf("expected 'consoleAdmin', %s", policies[0])
	}
}

// TestSkipContentSha256Cksum - Test validate the logic which decides whether
// to skip checksum validation based on the request header.
func TestSkipContentSha256Cksum(t *testing.T) {
	testCases := []struct {
		inputHeaderKey   string
		inputHeaderValue string

		inputQueryKey   string
		inputQueryValue string

import org.lastaflute.web.response.HtmlResponse;
import org.lastaflute.web.response.render.RenderData;
import org.lastaflute.web.ruts.process.ActionRuntime;
import org.lastaflute.web.validation.VaErrorHook;
import org.lastaflute.web.validation.exception.ValidationErrorException;

import jakarta.annotation.Resource;

/**
 * @author shinsuke
 * @author Keiichi Watanabe
 */

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

                            "content": {"application/json": {"schema": {}}},
                        },
                        "422": {
                            "description": "Validation Error",
                            "content": {
                                "application/json": {
                                    "schema": {

                            "content": {"application/json": {"schema": {}}},
                        },
                        "422": {
                            "description": "Validation Error",
                            "content": {
                                "application/json": {
                                    "schema": {

                    "schema": {
                        "$ref": "#/components/schemas/Item"
                    }
                }
            }
        },
        "422": {
            "description": "Validation Error",
            "content": {
                "application/json": {
                    "schema": {
                        "$ref": "#/components/schemas/HTTPValidationError"
                    }
                }

	"github.com/dustin/go-humanize"
	"github.com/minio/pkg/v3/wildcard"
	"gopkg.in/yaml.v3"
)

//go:generate msgp -file $GOFILE
//msgp:ignore BatchJobYamlErr

// BatchJobYamlErr can be used to return yaml validation errors with line,
// column information guiding user to fix syntax errors
type BatchJobYamlErr struct {
	line, col int
	msg       string
}

// message returns the error message excluding line, col information.