#### Running doctest for testable docstring

There are two ways to test the code in the docstring locally:

1.  If you are only changing the docstring of a class/function/method, then you
    can test it by passing that file's path to
    [tf_doctest.py](https://www.tensorflow.org/code/tensorflow/tools/docs/tf_doctest.py).
    For example:

    ```bash
    python tf_doctest.py --file=<file_path>
    ```

{* ../../docs_src/openapi_callbacks/tutorial001_py310.py hl[33] *}

/// tip

Notice that you are not passing the router itself (`invoices_callback_router`) to `callback=`, but the attribute `.routes`, as in `invoices_callback_router.routes`.

///

### Check the docs { #check-the-docs }

   * <p>The returned object is serializable if {@code function} is serializable.
   *
   * @param function the function to be used for loading values; must never return {@code null}
   * @return a cache loader that loads values by passing each key to {@code function}
   */
  public static <K, V> CacheLoader<K, V> from(Function<K, V> function) {
    return new FunctionToCacheLoader<>(function);
  }

  /**

#       by Bash, Ksh, etc; in particular arrays are avoided.
#
#       The "traditional" practice of packing multiple parameters into a
#       space-separated string is a well documented source of bugs and security
#       problems, so this is (mostly) avoided, by progressively accumulating
#       options in "$@", and eventually passing that to Java.
#
#       Where the inherited environment variables (DEFAULT_JVM_OPTS, JAVA_OPTS,

with `yield`.

For example, instead of using the same database session, you would create a new database session inside of the background task, and you would obtain the objects from the database using this new session. And then instead of passing the object from the database as a parameter to the background task function, you would pass the ID of that object and then obtain the object again inside the background task function....

#   re-running tests. However, note that:
#
#    - New environments like new CUDA versions, changes to manylinux,
#      compilers, etc. can cause undefined behavior such as build failures
#      or tests passing incorrectly.
#    - Automatic LLVM updates are known to extend build time even with
#      the cache; this is unavoidable.
export TFCI=py311,linux_x86,public_cache,disk_cache

# Recommended: Configure Docker. (Linux only)

	}
	if g, e := h.ModTime, fi.ModTime(); !g.Equal(e) {
		t.Errorf("ModTime = %v; want %v", g, e)
	}
	// FileInfoHeader should error when passing nil FileInfo
	if _, err := FileInfoHeader(nil, ""); err == nil {
		t.Fatalf("Expected error when passing nil to FileInfoHeader")
	}
}

func TestFileInfoHeaderDir(t *testing.T) {
	fi, err := os.Stat("testdata")
	if err != nil {
		t.Fatal(err)
	}

#
# While the mount argument (`-v host_path:container_path`) still requires
# `container_path` to be a legitimate C:\ path, in this case, 'C:\drive_t',
# this symlink does allow for the convenience of passing unedited paths
# to `docker exec` commands, e.g., 'T:\path', instead of 'C:\path',
# without having to replace the drive letter with C:\ every time.
# Such a workaround is not required on Linux, since it

    /**
     * Returns true if (current * radix) + digit is a number too large to be represented by an
     * unsigned long. This is useful for detecting overflow while parsing a string representation of
     * a number. Does not verify whether supplied radix is valid, passing an invalid radix will give
     * undefined results or an ArrayIndexOutOfBoundsException.
     */
    static boolean overflowInParse(long current, int digit, int radix) {

    return collection.stream();
  }

  /**
   * Returns a sequential {@link Stream} of the remaining contents of {@code iterator}. Do not use
   * {@code iterator} directly after passing it to this method.
   */
  public static <T extends @Nullable Object> Stream<T> stream(Iterator<T> iterator) {
    return StreamSupport.stream(Spliterators.spliteratorUnknownSize(iterator, 0), false);
  }

  /**