}
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  absl::Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      src.flat<tstring>()(i) = data[i];
    }

                   int64_t value) {
  desc->node_builder.Attr(attr_name, static_cast<int64_t>(value));
}

void TF_SetAttrIntList(TF_OperationDescription* desc, const char* attr_name,
                       const int64_t* values, int num_values) {
  desc->node_builder.Attr(
      attr_name, ArraySlice<const int64_t>(
                     reinterpret_cast<const int64_t*>(values), num_values));
}

                                 const int64_t* dims, int num_dims) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];

TF_CAPI_EXPORT extern void TF_SetAttrInt(TF_OperationDescription* desc,
                                         const char* attr_name, int64_t value);
TF_CAPI_EXPORT extern void TF_SetAttrIntList(TF_OperationDescription* desc,
                                             const char* attr_name,
                                             const int64_t* values,
                                             int num_values);

pkg math/rand/v2, func Int() int #61716
pkg math/rand/v2, func Int32() int32 #61716
pkg math/rand/v2, func Int32N(int32) int32 #61716
pkg math/rand/v2, func Int64() int64 #61716
pkg math/rand/v2, func Int64N(int64) int64 #61716
pkg math/rand/v2, func IntN(int) int #61716
pkg math/rand/v2, func N[$0 intType]($0) $0 #61716
pkg math/rand/v2, func New(Source) *Rand #61716

		{Name: "company_count_group_c"},
	})

	var count1 int64
	if err := DB.Model(&Company{}).Where("name = ?", "company_count_group_a").Group("name").Count(&count1).Error; err != nil {
		t.Errorf(fmt.Sprintf("Count should work, but got err %v", err))
	}
	if count1 != 1 {
		t.Errorf("Count with group should be 1, but got count: %v", count1)
	}

	var count2 int64

		12: {bucketName: bucket, objName: object, inputData: []byte("ijkl"), inputMeta: map[string]string{"etag": "09a0877d04abf8759f99adec02baf579"}, inputDataSize: int64(len("ijkl"))},
		13: {bucketName: bucket, objName: object, inputData: []byte("mnop"), inputMeta: map[string]string{"etag": "e132e96a5ddad6da8b07bba6f6131fef"}, inputDataSize: int64(len("mnop"))},

		// With no metadata

	"time"

	"github.com/minio/madmin-go/v3"
	"github.com/minio/minio-go/v7"
)

//go:generate msgp -file $GOFILE

// RStat has replication error stats
type RStat struct {
	Count int64 `json:"count"`
	Bytes int64 `json:"bytes"`
}

// RTimedMetrics has replication error stats for various time windows
type RTimedMetrics struct {
	LastHour    ReplicationLastHour `json:"lastHour"`

	// in Queue stats for bucket - from qCache
	QStat InQueueMetric `json:"queued"`
	// Deprecated fields
	// Pending size in bytes
	PendingSize int64 `json:"pendingReplicationSize"`
	// Failed size in bytes
	FailedSize int64 `json:"failedReplicationSize"`
	// Total number of pending operations including metadata updates
	PendingCount int64 `json:"pendingReplicationCount"`

		// Simple useful utilities
		repeat = func(k int64, n int) []int64 {
			a := []int64{}
			for range n {
				a = append(a, k)
			}
			return a
		}
		lsum = func(s []int64) int64 {
			sum := int64(0)
			for _, i := range s {
				if i < 0 {
					return -1
				}
				sum += i
			}
			return sum
		}
		esum = func(oi ObjectInfo) int64 {
			sum := int64(0)
			for _, i := range oi.Parts {