* IDE warnings become more accurate, reducing warnings fatigue.
* Public API nullability annotations become closer to reality.
* Java compilation of Gradle code suffers from small performance penalty (up to 10%).
* Time has to be allocated to clean up existing projects.
* One has to write NullAway-conformant code even when hacking.
* Some patterns used to satisfy NullAway can be slightly more verbose.

   */
  public static MappedByteBuffer map(File file, MapMode mode, long size) throws IOException {
    checkArgument(size >= 0, "size (%s) may not be negative", size);
    return mapInternal(file, mode, size);
  }

  private static MappedByteBuffer mapInternal(File file, MapMode mode, long size)
      throws IOException {
    checkNotNull(file);
    checkNotNull(mode);

    Closer closer = Closer.create();
    try {

# FastAPI CLI { #fastapi-cli }

**FastAPI <abbr title="command line interface">CLI</abbr>** is a command line program that you can use to serve your FastAPI app, manage your FastAPI project, and more.

When you install FastAPI (e.g. with `pip install "fastapi[standard]"`), it comes with a command line program you can run in the terminal.

To run your FastAPI app for development, you can use the `fastapi dev` command:

<div class="termy">

```console

    "parameter response_model=None. Read more: "
    "https://fastapi.tiangolo.com/tutorial/response-model/"
)


def create_model_field(
    name: str,
    type_: Any,
    default: Any | None = Undefined,
    field_info: FieldInfo | None = None,
    alias: str | None = None,
    mode: Literal["validation", "serialization"] = "validation",
) -> ModelField:

    sent to the client.

    Read more about it in the
    [FastAPI docs for Background Tasks](https://fastapi.tiangolo.com/tutorial/background-tasks/).

    ## Example

    ```python
    from fastapi import BackgroundTasks, FastAPI

    app = FastAPI()


    def write_notification(email: str, message=""):
        with open("log.txt", mode="w") as email_file:

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0

I'll tell you a bit more about these **concepts** here, and that would hopefully give you the **intuition** you would need to decide how to deploy your API in very different environments, possibly even in **future** ones that don't exist yet.

By considering these concepts, you will be able to **evaluate and design** the best way to deploy **your own APIs**.

      for (Map.Entry<RoundingMode, Double> entry : expectedValues.entrySet()) {
        RoundingMode mode = entry.getKey();
        Double expectation = entry.getValue();
        assertWithMessage("roundToDouble(%s, %s)", input, mode)
            .that(BigDecimalMath.roundToDouble(input, mode))
            .isEqualTo(expectation);
      }

      if (!expectedValues.containsKey(UNNECESSARY)) {

    for (RoundingMode mode : ALL_ROUNDING_MODES) {
      assertThrows(IllegalArgumentException.class, () -> BigIntegerMath.log2(ZERO, mode));
    }
  }

  public void testLog2NegativeAlwaysThrows() {
    for (RoundingMode mode : ALL_ROUNDING_MODES) {
      assertThrows(
          IllegalArgumentException.class, () -> BigIntegerMath.log2(BigInteger.valueOf(-1), mode));
    }
  }

*   Adds support for a new "distributed_epoch" processing mode. This processing
    mode distributes a dataset across all tf.data workers, instead of having
    each worker process the full dataset. See
    [the tf.data service docs](https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#understand_processing_mode)
    to learn more.
*   Adds optional `exclude_cols` parameter to CsvDataset. This parameter is the