type ArrayOf[T RoundTripper] struct {
	aPool sync.Pool // Arrays
	ePool sync.Pool // Elements
}

// NewArrayOf returns a new ArrayOf.
// You must provide a function that returns a new instance of T.
func NewArrayOf[T RoundTripper](newFn func() T) *ArrayOf[T] {
	return &ArrayOf[T]{
		ePool: sync.Pool{New: func() any {
			return newFn()
		}},
	}
}

)

// To abstract a node over network.
type peerRESTServer struct{}

var (
	// Types & Wrappers
	aoBucketInfo           = grid.NewArrayOf[*BucketInfo](func() *BucketInfo { return &BucketInfo{} })
	aoMetricsGroup         = grid.NewArrayOf[*MetricV2](func() *MetricV2 { return &MetricV2{} })
	madminBgHealState      = grid.NewJSONPool[madmin.BgHealState]()
	madminCPUs             = grid.NewJSONPool[madmin.CPUs]()

    [`tf.experimental.numpy`](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy),
    which is a NumPy-compatible API for writing TF programs. This module
    provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
    wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
    (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is
    seamless in most cases. See

pkg image, func DecodeConfig(io.Reader) (Config, string, error)
pkg image, func NewAlpha(Rectangle) *Alpha
pkg image, func NewAlpha16(Rectangle) *Alpha16
pkg image, func NewGray(Rectangle) *Gray
pkg image, func NewGray16(Rectangle) *Gray16
pkg image, func NewNRGBA(Rectangle) *NRGBA
pkg image, func NewNRGBA64(Rectangle) *NRGBA64
pkg image, func NewPaletted(Rectangle, color.Palette) *Paletted