return fmt.Sprintf("%s:TIMESTAMP", x)
	case string:
		return fmt.Sprintf("\"%s\":%s", x, v.GetTypeString())
	case []byte:
		return fmt.Sprintf("\"%s\":BYTES", string(x))
	case []Value:
		var s strings.Builder
		s.WriteByte('[')
		for i, v := range x {
			s.WriteString(v.Repr())
			if i < len(x)-1 {
				s.WriteByte(',')
			}
		}
		s.WriteString("]:ARRAY")
		return s.String()
	case Missing:

            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).

   * per call to submit() or submitAsync(), and each instance supports only one call to execute().
   *
   * <p>This class would certainly be simpler and easier to reason about if it were built with
   * ThreadLocal; however, ThreadLocal is not well optimized for the case where the ThreadLocal is
   * non-static, and is initialized/removed frequently - this causes churn in the Thread specific

	isReadLock := true
	return dm.lockBlocking(ctx, cancel, id, source, isReadLock, opts)
}

// lockBlocking will try to acquire either a read or a write lock
//
// The function will loop using a built-in timing randomized back-off
// algorithm until either the lock is acquired successfully or more
// time has elapsed than the timeout value.

		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)
	}
}

            modelBuilder = createModelBuilder();
        }
        return modelBuilder;
    }

    protected ModelBuilder createModelBuilder() {
        // from maven-model-builder
        DefaultModelProcessor modelProcessor = new DefaultModelProcessor(new DefaultModelXmlFactory(), List.of());
        return new DefaultModelBuilder(
                modelProcessor,

			testWrite{"\x00\x00", 2, nil},
			testWrite{"abc", 3, nil},
			testWrite{"\x00\x00", 2, errUnrefData},
		},
	}}

	for i, v := range vectors {
		var wantStr string
		bb := new(strings.Builder)
		w := testNonEmptyWriter{bb}
		var fw fileWriter
		switch maker := v.maker.(type) {
		case makeReg:
			fw = &regFileWriter{w, maker.size}
			wantStr = maker.wantStr
		case makeSparse:

* Add /bin/mkfifo symlink to bazel build for busybox, so that CI builds have /bin/tee ([#59268](https://github.com/kubernetes/kubernetes/pull/59268), [@justinsb](https://github.com/justinsb))

license-AGPL%20V3-blue)](https://github.com/minio/minio/blob/master/LICENSE) MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads. For more detailed documentation please visit [here](https://docs.minio.io/) Introduction ---------- This chart bootstraps MinIO Cluster on [Kubernetes]...