"start_date": {
                            "type": "string",
                            "format": "date-time",
                            "title": "Start Date",
                        },
                    },
                    "type": "object",
                    "required": ["username", "monthly_fee", "start_date"],

        return eventSpyDispatcher;
    }

    @Override
    public Map<String, Object> getData() {
        if (data == null) {
            data = new HashMap<>();
        }

        return data;
    }

                          type: string
                          format: date-time
                          example: "2017-10-09T22:28:56.000Z"
                        content_length:
                          type: string
                          example: "29624"
                        timestamp:
                          type: string
                          format: date-time
                          example: "2017-10-09T22:28:56.000Z"

when dealing with untrusted data. Based on the security history of these
libraries we consider that it is safe to work with untrusted inputs for PNG,
BMP, GIF, WAV, RAW, RAW\_PADDED, CSV and PROTO formats. All other input formats,
including tensorflow-io should be sandboxed if used to process untrusted data.

For example, if an attacker were to upload a malicious video file, they could

        Map<String, Object> data = new HashMap<>();
        data.put("plexus", context.lookup.lookup(PlexusContainer.class));
        data.put("workingDirectory", invokerRequest.cwd().toString());
        data.put("systemProperties", toProperties(invokerRequest.systemProperties()));
        data.put("userProperties", toProperties(invokerRequest.userProperties()));

    "Content-Length": "401",
    "Content-Type": "application/octet-stream",
    "User-Agent": "MinIO (linux; amd64) minio-go/v7.0.70 mc/RELEASE.2024-04-30T17-44-48Z",
    "X-Amz-Content-Sha256": "STREAMING-AWS4-HMAC-SHA256-PAYLOAD",
    "X-Amz-Date": "20240509T073810Z",

You write standard Python with types:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

That can then be used like:

```Python

            final String queryId = data.getQueryId();
            final String highlightParams = data.getAppendHighlightParams();
            final boolean nextPage = data.isExistNextPage();
            final boolean prevPage = data.isExistPrevPage();
            final long startRecordNumber = data.getCurrentStartRecordNumber();
            final long endRecordNumber = data.getCurrentEndRecordNumber();

type WriteTest struct {
	Name   string
	Data   []byte
	Method uint16
	Mode   fs.FileMode
}

var writeTests = []WriteTest{
	{
		Name:   "foo",
		Data:   []byte("Rabbits, guinea pigs, gophers, marsupial rats, and quolls."),
		Method: Store,
		Mode:   0666,
	},
	{
		Name:   "bar",
		Data:   nil, // large data set in the test
		Method: Deflate,
		Mode:   0644,
	},
	{

     * to eliminate copying data when writing NbtSession data one might
     * manage that 4 byte header specifically and therefore the initial
     * bufferIndex, and thus headerStart, would be 4).(NOTE: If one where
     * looking for a way to improve perfomance this is precisly what you
     * would want to do as the jcifs.smb1.netbios.SocketXxxputStream classes
     * arraycopy all data read or written into a new buffer shifted over 4!)