} else if (values.hasAnyNegativeInfinity()) {
        assertWithMessage("mean of %s", values).that(mean).isNegativeInfinity();
      } else {
        assertWithMessage("mean of %s", values)
            .that(mean)
            .isWithin(ALLOWED_ERROR)
            .of(MANY_VALUES_MEAN);
      }
    }

        twoValuesAccumulator.xStats().mean(),
        twoValuesAccumulator.yStats().mean(),
        twoValuesAccumulator.xStats().populationVariance(),
        twoValuesAccumulator.populationCovariance());
    assertDiagonalLinearTransformation(
        twoValuesAccumulatorByAddAllPartitionedPairedStats.leastSquaresFit(),
        twoValuesAccumulatorByAddAllPartitionedPairedStats.xStats().mean(),

        accumulatorByAddAllStats.addAll(Stats.of(value));
      }
      double mean = accumulator.mean();
      double meanByAddAllStats = accumulatorByAddAllStats.mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of %s", values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of %s", values).that(meanByAddAllStats).isNaN();

            fit,
            stats.xStats().mean(),
            stats.yStats().mean(),
            stats.xStats().populationVariance(),
            stats.populationCovariance());
      }
    }
    assertHorizontalLinearTransformation(
        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(

            fit,
            stats.xStats().mean(),
            stats.yStats().mean(),
            stats.xStats().populationVariance(),
            stats.populationCovariance());
      }
    }
    assertHorizontalLinearTransformation(
        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(

        accumulatorByAddAllStats.addAll(Stats.of(value));
      }
      double mean = accumulator.mean();
      double meanByAddAllStats = accumulatorByAddAllStats.mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of %s", values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of %s", values).that(meanByAddAllStats).isNaN();

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all

# Deployment { #deployment }

Deploying a **FastAPI** application is relatively easy.

## What Does Deployment Mean { #what-does-deployment-mean }

To **deploy** an application means to perform the necessary steps to make it **available to the users**.

http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with...

If you use a `requirements.txt` file you could specify the version with:

```txt
fastapi[standard]==0.112.0
```

that would mean that you would use exactly the version `0.112.0`.

Or you could also pin it with:

```txt
fastapi[standard]>=0.112.0,<0.113.0
```

that would mean that you would use the versions `0.112.0` or above, but less than `0.113.0`, for example, a version `0.112.2` would still be accepted.