*
   * <p>Because the values for a given key may have duplicates and follow the insertion ordering,
   * this method returns a {@link List}, instead of the {@link java.util.Collection} specified in
   * the {@link Multimap} interface.
   */
  @Override
  List<V> get(@ParametricNullness K key);

  /**
   * {@inheritDoc}
   *
   * <p>Because the values for a given key may have duplicates and follow the insertion ordering,

   
   | 1.13.1 - 2.0  | 10.0  |
   
   | 1.5.0 - 1.12.0 | 9.0 |
   
     * If you have above configuration and using _**Windows**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
       * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).
     * If you have above configuration and using _**Ubuntu/Linux**_ platform -

)
async def create_item(item: Item):
    """
    Create an item with all the information:

    - **name**: each item must have a name
    - **description**: a long description
    - **price**: required
    - **tax**: if the item doesn't have tax, you can omit this
    - **tags**: a set of unique tag strings for this item
    """

   * filters to be compatible, they must:
   *
   * <ul>
   *   <li>not be the same instance
   *   <li>have the same number of hash functions
   *   <li>have the same bit size
   *   <li>have the same strategy
   *   <li>have equal funnels
   * </ul>
   *
   * @param that The Bloom filter to check for compatibility.
   * @since 15.0
   */

                    "operationId": "read_items_items__get",
                    "parameters": [
                        {
                            "description": "Query string for the items to search in the database that have a good match",
                            "required": False,
                            "deprecated": True,
                            "schema": IsDict(
                                {

                    "operationId": "read_items_items__get",
                    "parameters": [
                        {
                            "description": "Query string for the items to search in the database that have a good match",
                            "required": False,
                            "deprecated": True,
                            "schema": IsDict(
                                {

async def create_item(item: Item):
    """
    Create an item with all the information:

    - **name**: each item must have a name
    - **description**: a long description
    - **price**: required
    - **tax**: if the item doesn't have tax, you can omit this
    - **tags**: a set of unique tag strings for this item
    """

### Edit it

It's a very simple program.

But now imagine that you were writing it from scratch.

At some point you would have started the definition of the function, you had the parameters ready...

But then you have to call "that method that converts the first letter to upper case".

Was it `upper`? Was it `uppercase`? `first_uppercase`? `capitalize`?

				time.Unix(0, 3).UTC(),
				time.Unix(0, 3).UTC(),
				time.Unix(0, 3).UTC(),
			},
			time.Unix(0, 3).UTC(),
			4,
		},
		{
			// 3. Tests common time obtained when elements have a mixture of
			// sentinel values and don't have read quorum on any of the values.
			[]time.Time{
				time.Unix(0, 3).UTC(),
				time.Unix(0, 3).UTC(),
				time.Unix(0, 2).UTC(),
				time.Unix(0, 1).UTC(),
				time.Unix(0, 3).UTC(),

## Dataclasses in Nested Data Structures

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: