You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: rand_test.go
    path of file: ./repos/cockroach/pkg/kv/kvserver/asim/tests
    
    the code of the file until where you have to start completion: // Copyright 2023 The Cockroach Authors.
//
// Use of this software is governed by the Business Source License
// included in the file licenses/BSL.txt.
//
// As of the Change Date specified in that file, in accordance with
// the Business Source License, use of this software will be governed
// by the Apache License, Version 2.0, included in the file
// licenses/APL.txt.

package tests

import (
	"fmt"
	"math/rand"
	"testing"
	"time"

	"github.com/cockroachdb/cockroach/pkg/testutils/datapathutils"
	"github.com/cockroachdb/datadriven"
)

const (
	defaultNumIterations = 3
	defaultSeed          = int64(42)
	defaultDuration      = 10 * time.Minute
	defaultVerbosity     = OutputResultOnly
)

// TestRandomized is a randomized data-driven testing framework that validates
// allocators by creating randomized configurations. It is designed for
// regression and exploratory testing.

// There are three modes for every aspect of randomized generation.
// - Static Mode:
//   1. If randomization options are disabled (e.g. no rand_ranges command is
//   used), the system uses the default configurations (defined in
//   default_settings.go) with no randomization.
// - Randomized: two scenarios occur:
//   2. Use default settings for randomized generation (e.g.rand_ranges)
//   3. Use settings specified with commands (e.g.rand_ranges
//   range_gen_type=zipf)

// The following commands are provided:
//	1. "rand_cluster" [cluster_gen_type=(single_region|multi_region|any_region)]
//	e.g. rand_cluster cluster_gen_type=(multi_region)
//	- rand_cluster: randomly picks a predefined cluster configuration
//    according to the specified type.
//	- cluster_gen_type (default value is multi_region): cluster configuration
//    type. On the next eval, the cluster is generated as the initial state of
//    the simulation.

//	2. "rand_ranges" [placement_type=(even|skewed|random|weighted_rand)]
// 	[replication_factor=<int>] [range_gen_type=(uniform|zipf)]
//	[keyspace_gen_type=(uniform|zipf)] [weighted_rand=(<[]float64>)]
//	e.g. rand_ranges placement_type=weighted_rand weighted_rand=(0.1,0.2,0.7)
//	e.g. rand_ranges placement_type=skewed replication_factor=1
//		 range_gen_type=zipf keyspace_gen_type=uniform
//	- rand_ranges: randomly generate a distribution of ranges across stores
//    based on the specified parameters. On the next call to eval, ranges and
//    their replica placement are generated and loaded to initial state.
//	- placement_type(default value is even): defines the type of range placement
// 	  distribution across stores. Once set, it remains constant across
// 	  iterations with no randomization involved.
//	- replication_factor(default value is 3): represents the replication factor
//	  of each range. Once set, it remains constant across iterations with no
//	  randomization involved.
//	- range_gen_type(default value is uniform): represents the type of
//	  distribution used to yield the range parameter as ranges are generated
//    across iterations (range ∈[1, 1000]).
//	- keyspace_gen_type: represents the typ
    
func TestRandomized(t *testing.T) {
	dir := datapathutils.TestDataPath(t, "rand")
	datadriven.Walk(t, dir, func(t *testing.T, path string) {
		randOptions := testRandOptions{}
		var rGenSettings rangeGenSettings
		var cGenSettings clusterGenSettings
		var eGenSettings eventGenSettings
		staticOptionSettings := getDefaultStaticOptionSettings()
		datadriven.RunTest(t, path, func(t *testing.T, d *datadriven.TestData) string {
			switch d.Cmd {
			case "clear":
				randOptions = testRandOptions{}
				rGenSettings = rangeGenSettings{}
				cGenSettings = clusterGenSettings{}
				eGenSettings = eventGenSettings{}
				staticOptionSettings = getDefaultStaticOptionSettings()
				return ""
			case "rand_cluster":
				randOptions.cluster = true
				clusterGenType := defaultClusterGenType
				scanIfExists(t, d, "cluster_gen_type", &clusterGenType)
				cGenSettings = clusterGenSettings{
					clusterGenType: clusterGenType,
				}
				return ""
			case "change_static_option":
				scanIfExists(t, d, "nodes", &staticOptionSettings.nodes)
				scanIfExists(t, d, "stores_per_node", &staticOptionSettings.storesPerNode)
				scanIfExists(t, d, "rw_ratio", &staticOptionSettings.rwRatio)
				scanIfExists(t, d, "rate", &staticOptionSettings.rate)
				scanIfExists(t, d, "min_block", &staticOptionSettings.minBlock)
				scanIfExists(t, d, "max_block", &staticOptionSettings.maxBlock)
				scanIfExists(t, d, "min_key", &staticOptionSettings.minKey)
				scanIfExists(t, d, "max_key", &staticOptionSettings.maxKey)
				scanIfExists(t, d, "skewed_access", &staticOptionSettings.skewedAccess)
				scanIfExists(t, d, "ranges", &staticOptionSettings.ranges)
				scanIfExists(t, d, "key_space", &staticOptionSettings.keySpace)
				scanIfExists(t, d, "placement_type", &staticOptionSettings.placementType)
				scanIfExists(t, d, "replication_factor", &staticOptionSettings.replicationFactor)
				scanIfExists(t, d, "bytes", &staticOptionSettings.bytes)
				scanIfExists(t, d, "stat", &staticOptionSettings.stat)
				scanIfExists(t, d, "height", &staticOptionSettings.height)
				scanIfExists(t, d, "width", &staticOptionSettings.width)
				return ""
			case "rand_ranges":
				randOptions.ranges = true
				placementType, replicationFactor, rangeGenType, keySpaceGenType := defaultPlacementType, defaultReplicationFactor, defaultRangeGenType, defaultKeySpaceGenType
				weightedRand := defaultWeightedRand
				scanIfExists(t, d, "placement_type", &placementType)
				scanIfExists(t, d, "replication_factor", &replicationFactor)
				scanIfExists(t, d, "range_gen_type", &rangeGenType)
				scanIfExists(t, d, "keyspace_gen_type", &keySpaceGenType)
				scanIfExists(t, d, "weighted_rand", &weightedRand)
				rGenSettings = rangeGenSettings{
					placementType:     placementType,
					replicationFactor: replicationFactor,
					rangeGenType:      rangeGenType,
					keySpaceGenType:   keySpaceGenType,
					weightedRand:      weightedRand,
				}
				return ""
			case "rand_load":
				return "unimplemented: randomized load"
			case "rand_events":
				randOptions.staticEvents = true
				seriesType, durationToAssertOnEvent := defaultEventsType, defaultDurationToAssertOnEvent
				scanIfExists(t, d, "type", &seriesType)
				scanIfExists(t, d, "duration_to_assert_on_event", &durationToAssertOnEvent)
				eGenSettings = eventGenSettings{
					durationToAssertOnEvent: durationToAssertOnEvent,
					eventsType:              seriesType,
				}
				return ""
			case "rand_settings":
				return "unimplemented: randomized settings"
			case "eval":
				seed := defaultSeed
				numIterations := defaultNumIterations
				duration := defaultDuration
				verbose := defaultVerbosity
				scanIfExists(t, d, "seed", &seed)
				scanIfExists(t, d, "num_iterations", &numIterations)
				scanIfExists(t, d, "duration", &duration)
				scanIfExists(t, d, "verbose", &verbose)
				s := testSettings{
					numIterations: numIterations,
					duration:      duration,
					randSource:    rand.New(rand.NewSource(seed)),
					assertions:    defaultAssertions(),
					verbose:       verbose,
					randOptions:   randOptions,
					rangeGen:      rGenSettings,
					clusterGen:    cGenSettings,
					eventGen:      eGenSettings,
				}
				f := newRandTestingFramework(s, staticOptionSettings)
				outputs := f.runRandTestRepeated()
				return outputs.String()
			default:
				return fmt.Sprintf("unknown command: %s", d.Cmd)
			}
		})
	})
}