The most common is the implicit flow.

The most secure is the code flow, but it's more complex to implement as it requires more steps. As it is more complex, many providers end up suggesting the implicit flow.

/// note

It's common that each authentication provider names their flows in a different way, to make it part of their brand.

# Important: "tfrun foo | bar" is "( tfrun foo ) | bar", not "tfrun (foo | bar)".
# Therefore, "tfrun" commands cannot include pipes -- which is
# probably for the better. If a pipe is necessary for something, it is probably
# complex. Write a well-documented script under utilities/ to encapsulate the
# functionality instead.
tfrun() { "$@"; }

if [[ $(uname -s) = MSYS_NT* ]]; then
  source ./ci/official/utilities/windows.sh

  }

  @Override
  public final void clear() {
    backingMap.clear();
    size = 0;
  }

  /**
   * Skeleton of per-entry iterators. We could push this down and win a few bytes, but it's complex
   * enough it's not especially worth it.
   */
  abstract class Itr<T extends @Nullable Object> implements Iterator<T> {
    int entryIndex = backingMap.firstIndex();
    int toRemove = -1;

do exactly this.
</p>

<p>
A read of an array, struct, or complex number
may be implemented as a read of each individual sub-value
(array element, struct field, or real/imaginary component),
in any order.
Similarly, a write of an array, struct, or complex number
may be implemented as a write of each individual sub-value,
in any order.
</p>

<p>

    // TLD with only 1 (wildcard) rule.
    checkPublicSuffix("mm", null)
    checkPublicSuffix("c.mm", null)
    checkPublicSuffix("b.c.mm", "b.c.mm")
    checkPublicSuffix("a.b.c.mm", "b.c.mm")
    // More complex TLD.
    checkPublicSuffix("jp", null)
    checkPublicSuffix("test.jp", "test.jp")
    checkPublicSuffix("www.test.jp", "test.jp")
    checkPublicSuffix("ac.jp", null)
    checkPublicSuffix("test.ac.jp", "test.ac.jp")

    "C4",  # flake8-comprehensions
    "UP",  # pyupgrade
]
ignore = [
    "E501",  # line too long, handled by black
    "B008",  # do not perform function calls in argument defaults
    "C901",  # too complex
    "W191",  # indentation contains tabs
]

[tool.ruff.lint.per-file-ignores]
"__init__.py" = ["F401"]
"docs_src/dependencies/tutorial007_py39.py" = ["F821"]

But before that, handling asynchronous code was quite more complex and difficult.

In previous versions of Python, you could have used threads or <a href="https://www.gevent.org/" class="external-link" target="_blank">Gevent</a>. But the code is way more complex to understand, debug, and think about.

> What’s the history of this project? It seems to have come from nowhere to awesome in a few weeks [...]

Here's a little bit of that history.

## Alternatives { #alternatives }

I have been creating APIs with complex requirements for several years (Machine Learning, distributed systems, asynchronous jobs, NoSQL databases, etc), leading several teams of developers.

As part of that, I needed to investigate, test and use many alternatives.

    assertFalse(set.equals(newHashSet(4, 5, 6)));
    assertFalse(newHashSet(4, 5, 6).equals(set));

    Set<String> complex = Sets.newTreeSet(STRING_LENGTH);
    Collections.addAll(complex, "in", "the", "a");
    assertEquals(set, complex);
  }

  public void testEquals_bothDefaultOrdering_stringVsInt() {
    SortedSet<String> set = of("a", "b", "c");

* Data conversion
* Data validation
* Automatic documentation

## Special types and validation { #special-types-and-validation }

Apart from normal singular types like `str`, `int`, `float`, etc. you can use more complex singular types that inherit from `str`.