// Standard S3 HTTP request constants
const (
	IfModifiedSince   = "If-Modified-Since"
	IfUnmodifiedSince = "If-Unmodified-Since"
	IfMatch           = "If-Match"
	IfNoneMatch       = "If-None-Match"

	// Request tags used in GetObjectAttributes
	Checksum     = "Checksum"
	StorageClass = "StorageClass"
	ObjectSize   = "ObjectSize"
	ObjectParts  = "ObjectParts"

	// S3 storage class
	AmzStorageClass = "x-amz-storage-class"

		languages = append(languages, language)
	}

	user := User{}
	db.Create(&user)
	db.Preload("Languages").FirstOrCreate(&user)

	var wg sync.WaitGroup
	for i := 0; i < count; i++ {
		wg.Add(1)
		go func(user User, language Language) {
			err := db.Model(&user).Association("Languages").Append(&language)
			AssertEqual(t, err, nil)

			wg.Done()
		}(user, languages[i])
	}
	wg.Wait()

	var find User

	var user3 User
	DB.First(&user3, users[1].ID)

	users[1].Age += 50
	CheckUser(t, user3, *users[1])

	// update with struct
	DB.Model(users[1]).UpdateColumns(User{Name: "update_column_02_newnew2", Age: 200})
	if users[1].Name != "update_column_02_newnew2" || users[1].Age != 200 {
		t.Errorf("user 2 should be updated with update column")
	}
	AssertEqual(t, lastUpdatedAt.UnixNano(), users[1].UpdatedAt.UnixNano())

) -> dict[str, Any]:
    users: dict[str, Any] = {}
    for user, count in counter.most_common():
        if count >= min_count:
            author = authors[user]
            users[user] = {
                "login": user,
                "count": count,
                "avatarUrl": author.avatarUrl,
                "url": author.url,
            }
    return users

* **Deep Learning**: este es un subcampo de Machine Learning, por lo tanto, se aplica lo mismo. Es solo que no hay una sola hoja de cálculo de números para multiplicar, sino un enorme conjunto de ellas, y en muchos casos, usas un procesador especial para construir y / o usar esos modelos.

### Concurrencia + Paralelismo: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

                },
            },
            "/users/": {
                "post": {
                    "operationId": "create_user",
                    "requestBody": {
                        "content": {
                            "application/json": {
                                "schema": {
                                    "$ref": "#/components/schemas/User",
                                },

Con `Form` puedes declarar las mismas configuraciones que con `Body` (y `Query`, `Path`, `Cookie`), incluyendo validación, ejemplos, un alias (por ejemplo, `user-name` en lugar de `username`), etc.

/// info | Información

`Form` es una clase que hereda directamente de `Body`.

///

/// tip | Consejo

    pragmatic software license, and a desire to create high quality software that leads the way in its field.
    We consider ourselves not simply a group of projects sharing a server, but rather a community of developers
    and users.
  </description>
  <licenses>
    <license>
      <name>The Apache Software License, Version 2.0</name>
      <url>http://www.apache.org/licenses/LICENSE-2.0.txt</url>
      <distribution>repo</distribution>

## Create and Use Env Vars { #create-and-use-env-vars }

You can **create** and use environment variables in the **shell (terminal)**, without needing Python:

//// tab | Linux, macOS, Windows Bash

<div class="termy">

```console
// You could create an env var MY_NAME with
$ export MY_NAME="Wade Wilson"

// Then you could use it with other programs, like
$ echo "Hello $MY_NAME"

    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  }

  TF_Operation* Use(const std::vector<TF_Operation*>& inputs) {
    return UseT(ToOutput(inputs));
  }

  TF_Operation* UseT(const std::vector<TF_Output>& inputs) {
    TF_Operation* op;
    UseHelper(inputs, &op);
    return op;
  }

  // All the *Helper methods are used as a workaround for the restrictions that