* could perform a cast to LegacyConverter, but we can't because it's an internal-only class.
   *
   * TODO(cpovirk): So make it part of the open-source build, albeit package-private there?
   *
   * So we use uncheckedCastNullableTToT here. This is a weird usage of that method: The method is

        if (fieldLogMap != null) {
            assertEquals(fieldLogMap, map);
        }
        map.clear();
    }

    private QueryContext buildQuery(String query) {
        return queryHelper.build(SearchRequestType.SEARCH, query, context -> {
            context.skipRoleQuery();
        });
    }

        * [Build simple API service with Python FastAPI — Part 1](https://dev.to/cuongld2/build-simple-api-service-with-python-fastapi-part-1-581o) by [cuongld2](https://dev.to/cuongld2).
        * [FastAPI + Zeit.co = 🚀](https://paulsec.github.io/posts/fastapi_plus_zeit_serverless_fu/) by [Paul Sec](https://twitter.com/PaulWebSec).

---

<div style="text-align: left; direction: ltr;">"Honestly, what you've built looks super solid and polished. In many ways, it's what I wanted <strong>Hug</strong> to be - it's really inspiring to see someone build that."</div>

  }

  public void testPrecomputedOptimizations() {
    // These are testing behavior that's never promised by the API.
    // Some matchers are so efficient that it is a waste of effort to
    // build a precomputed version.
    CharMatcher m1 = is('x');
    assertSame(m1, m1.precomputed());
    assertEquals(m1.toString(), m1.precomputed().toString());

    CharMatcher m2 = anyOf("Az");

		iamGroupsMap:            map[string]GroupInfo{},
		iamUserGroupMemberships: map[string]set.StringSet{},
		iamGroupPolicyMap:       xsync.NewMapOf[string, MappedPolicy](),
	}
}

// buildUserGroupMemberships - builds the memberships map. IMPORTANT:
// Assumes that c.Lock is held by caller.
func (c *iamCache) buildUserGroupMemberships() {
	for group, gi := range c.iamGroupsMap {
		c.updateGroupMembershipsMap(group, &gi)
	}
}

		// import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
		// object SparkJSONRead {
		//   def main(args: Array[String]): Unit = {
		//     val spark:SparkSession = SparkSession.builder()
		//       .appName("SparkByExample")
		//       .master("local[1]").getOrCreate()
		//
		//     spark.sparkContext.setLogLevel("ERROR")

            if (this.endDate == null) return;

            this.calculateChosenLabel();
        },

        renderCalendar: function(side) {

            //
            // Build the matrix of dates that will populate the calendar
            //

            var calendar = side == 'left' ? this.leftCalendar : this.rightCalendar;
            var month = calendar.month.month();

* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

### Concurrency + Parallelism: Web + Machine Learning

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

unnecessary redirection from ci/latest.txt to ci-cross/latest.txt. Users should know exactly where the builds are stored on Google Cloud storage buckets from now on. For example for 1.9 and 1.10, users can specify ci/latest-1.9 and ci/latest-1.10 as the CI build jobs what build images correctly updates those. The CI jobs for master update the ci-cross/latest location, so if you are looking for latest master builds, then the correct parameter to use would be ci-cross/latest. ([#63504](https://github.com/ku...