Bạn viết chuẩn Python với kiểu dữ liệu như sau:

```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
```

Sau đó có thể được sử dụng:

```Python

编写带有类型标注的标准 Python：

```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
```

可以像这样来使用：

```Python

		t, n := binary.Uvarint(b)
		if n <= 0 {
			break
		}
		b = b[n:]

		typ := ChecksumType(t)
		length := typ.RawByteLen()
		if length == 0 || len(b) < length {
			break
		}
		// Skip main checksum
		b = b[length:]
		parts, n := binary.Uvarint(b)
		if n <= 0 {
			break
		}
		if !typ.Is(ChecksumIncludesMultipart) {
			continue
		}

		if len(res) == 0 {

package lex

import (
	"fmt"
	"os"
	"path/filepath"
	"slices"
	"strconv"
	"strings"
	"text/scanner"

	"cmd/asm/internal/flags"
	"cmd/internal/objabi"
	"cmd/internal/src"
)

// Input is the main input: a stack of readers and some macro definitions.
// It also handles #include processing (by pushing onto the input stack)
// and parses and instantiates macro definitions.
type Input struct {
	Stack

                exclude_defaults=exclude_defaults,
                exclude_none=exclude_none,
            )

        def __hash__(self) -> int:
            # Each ModelField is unique for our purposes, to allow making a dict from
            # ModelField to its JSON Schema.
            return id(self)

    def get_annotation_from_field_info(
        annotation: Any, field_info: FieldInfo, field_name: str
    ) -> Any:

  }
  // TODO(b/166669239): This is needed to support AttrBuilder::Get for string
  // attributes. Number type attrs and DataType attrs work fine without this.
  // Consider getting rid of this and making the behavior between number types
  // and string consistent.
  forward_op_->attrs.BuildNodeDef();
  std::unique_ptr<GradientFunction> gradient_fn;
  TF_RETURN_IF_ERROR(registry.Lookup(*forward_op_, &gradient_fn));

### Step 3: Test on NATS

If you use NATS server, check out this sample program below to log the bucket notification added to NATS.

```go
package main

// Import Go and NATS packages
import (
 "log"
 "runtime"

 "github.com/nats-io/nats.go"
)

func main() {

 // Create server connection
 natsConnection, _ := nats.Connect("nats://yourusername:yoursecret@localhost:4222")
 log.Println("Connected")

podAnnotations: {} annotations: {} securityContext: enabled: false runAsUser: 1000 runAsGroup: 1000 fsGroup: 1000 resources: requests: memory: 128Mi nodeSelector: {} tolerations: [] affinity: {} extraVolumes: [] extraVolumeMounts: [] # Command to run after the main command on exit exitCommand: "" ## List of users to be created after minio install ## users: ## Username, password and policy to be assigned to the user ## Default policies are [readonly|readwrite|writeonly|consoleAdmin|diagnostics] ## Add new policies...

여러분은 타입을 이용한 표준 파이썬을 다음과 같이 적을 수 있습니다:

```Python
from datetime import date

from pydantic import BaseModel

# 변수를 str로 선언
# 그 후 함수 안에서 편집기 지원을 받으세요
def main(user_id: str):
    return user_id


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

위의 코드는 다음과 같이 사용될 수 있습니다:

```Python

			retryIntvl = 3 * time.Second
		}

		maxRetries := h.config.MaxRetry

	retry:
		// If the channel reaches above half capacity
		// we spawn more workers. The workers spawned
		// from this main worker routine will exit
		// once the channel drops below half capacity
		// and when it's been at least 30 seconds since
		// we launched a new worker.
		if mainWorker && len(h.logCh) > cap(h.logCh)/2 {