}

.progress-step.active .step-icon i {
    color: white;
}

.progress-step.completed .step-icon {
    background-color: #00875a;
}

.progress-step.completed .step-icon i {
    color: white;
}

.progress-step .step-label {
    font-size: 0.6875rem;
    color: #6b778c;
    text-align: center;
    white-space: nowrap;
}

.progress-step.active .step-label {

            ? EnumMultiset.create(AnEnum.class)
            : EnumMultiset.create(asList(elements));
      }
    };
  }

  private enum Color {
    BLUE,
    RED,
    YELLOW,
    GREEN,
    WHITE
  }

  private enum Gender {
    MALE,
    FEMALE
  }

  public void testClassCreate() {
    Multiset<Color> ms = EnumMultiset.create(Color.class);
    ms.add(Color.RED);
    ms.add(Color.YELLOW);

            ? EnumMultiset.create(AnEnum.class)
            : EnumMultiset.create(asList(elements));
      }
    };
  }

  private enum Color {
    BLUE,
    RED,
    YELLOW,
    GREEN,
    WHITE
  }

  private enum Gender {
    MALE,
    FEMALE
  }

  public void testClassCreate() {
    Multiset<Color> ms = EnumMultiset.create(Color.class);
    ms.add(Color.RED);
    ms.add(Color.YELLOW);

// Colors
const (
	Reset       = "\033[0m"
	Red         = "\033[31m"
	Green       = "\033[32m"
	Yellow      = "\033[33m"
	Blue        = "\033[34m"
	Magenta     = "\033[35m"
	Cyan        = "\033[36m"
	White       = "\033[37m"
	BlueBold    = "\033[34;1m"
	MagentaBold = "\033[35;1m"
	RedBold     = "\033[31;1m"
	YellowBold  = "\033[33;1m"
)

// LogLevel log level
type LogLevel int

const (

}

.termynal-comment {
  color: #4a968f;
  font-style: italic;
  display: block;
}

.termy {
  /* For right to left languages */
  direction: ltr;
}

.termy [data-termynal] {
  white-space: pre-wrap;
}

.termy .linenos {
  display: none;
}

/* External links: detected by JS comparing origin to site origin
   JS sets data-external-link on links pointing outside the site

*   [TensorFlow YouTube](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ)
*   [TensorFlow model optimization roadmap](https://www.tensorflow.org/model_optimization/guide/roadmap)
*   [TensorFlow White Papers](https://www.tensorflow.org/about/bib)
*   [TensorBoard Visualization Toolkit](https://github.com/tensorflow/tensorboard)
*   [TensorFlow Code Search](https://cs.opensource.google/tensorflow/tensorflow)

								<i class="fa fa-refresh me-1" aria-hidden="true"></i><la:message key="labels.chat_retry" />
							</button>
							<button type="button" class="error-banner-dismiss btn-close btn-close-white" aria-label="<la:message key="labels.chat_dismiss" />"></button>
						</div>
						<div class="input-wrapper">
							<div class="input-group">
								<textarea id="chatInput" class="form-control"

								<i class="fa fa-refresh me-1" aria-hidden="true"></i><la:message key="labels.chat_retry" />
							</button>
							<button type="button" class="error-banner-dismiss btn-close btn-close-white" aria-label="<la:message key="labels.chat_dismiss" />"></button>
						</div>
						<div class="input-wrapper">
							<div class="input-group">
								<textarea id="chatInput" class="form-control"

// It splits the slice s around each instance of one or more consecutive white space
// characters, as defined by [unicode.IsSpace], returning a slice of subslices of s or an
// empty slice if s contains only white space. Every element of the returned slice is
// non-empty. Unlike [Split], leading and trailing runs of white space characters
// are discarded.
func Fields(s []byte) [][]byte {
	// First count the fields.

  - search.suggest
  - toc.follow
  icon:
    repo: fontawesome/brands/github-alt
  logo: img/icon-white.svg
  favicon: img/favicon.png
  language: en
repo_name: fastapi/fastapi
repo_url: https://github.com/fastapi/fastapi
plugins:
  social:
    cards_layout_options:
      logo: ../en/docs/img/icon-white.svg
  typeset: null
  search: null
  macros:
    include_yaml: