What is the translation of " UNDERLYING ARRAY " in Chinese?

• 底层数组

Examples of using Underlying array in English and their translations into Chinese

A nil slice has a length and capacity of 0 and has no underlying array.
Nil切片的长度和容量为0且没有底层数组。

Other slices that share the same underlying array will see those changes.
与它共享底层数组的切片都会观测到这些修改。

Slices support the[] operator to access elements of the underlying array.
切片支持[]操作符来访问底层数组的元素。

With each addition, the list needs to make sure its underlying array has enough free slots in it to accommodate the new item.
每次添加后，该列表还需确保其底层数组有足够的空槽能装下新项目。

In fact, ArrayList. Sort just calls Sort on the underlying array.
实际上，ArrayList.Sort仅对隐含的数组调用Sort。

Yes, the underlying array has 10 elements, but we need to explicitly expand our slice in order to access those elements.
没错，底层数组可以放10个元素，但是我们需要显式的扩展切片，才能访问到底层数组的元素。

A nil slice has a length and capacity of 0 with no existing underlying array.
Nil切片的长度和容量为0且没有底层数组。

The capacity of the slice is the number of elements in the underlying array starting from the index from which the slice is created.
切片的容量是从创建切片的索引开始的底层数组中元素的数量。

Any modifications done to the slice will be reflected in the underlying array.
对切片所做的任何修改都会反映在底层数组中。

With each addition, the list needs to make sure its underlying array has enough free slots in it to accommodate the new item.
随着每一次添加元素，list都需要确保其底层数组拥有足够的位置来容纳新的元素。

Any modifications done to the slice will be reflected in the underlying array.
对slice所做的任何修改都将反映在底层数组中。

When a number of slices share the same underlying array, the changes that each one makes will be reflected in the array..
当多个切片共享相同的底层数组时，每个元素所做的更改将在数组中反映出来。

The length is the size of the slice, the capacity is the size of the underlying array.
长度是切片的长度，容量是底层数组的长度。

Slices hold references to an underlying array, and if you assign one slice to another, both refer to the same array..
切片持有对底层数组的引用，如果你将一个切片赋值给另一个，二者都将引用同一个数组。

Initialcapacity: hashmap, which is the length of the underlying array.
InitialCapacity：HashMap的最大容量，即为底层数组的长度。

Parallel hash tables store their elements in an underlying array and place them in the position determined by the hash code of the respective element.
并行哈希表（Parallelhashtables）存储底层数组的元素，并将它们放置在由各自元素哈希码的位置。

A slice does not store any data, it just describes a section of an underlying array.
切片并不存储任何数据，它只是描述了底层数组中的一段。

Notice that this header still points to the same underlying array, stored in the buffer variable.
注：此header仍然指向相同的底层数组，存储在buffer变量中。

Changing the elements of a slice modifies the corresponding elements of its underlying array.
更改切片的元素会修改其底层数组中对应的元素。

If you could, the garbage collection system would need to keep the previous underlying array around until you had stopped using that pointer.
如果可以的话，垃圾收集系统需要保留以前的底层数组，直到你停止使用该指针。

This means that the elements can be accessed and updated efficiently by modifying the underlying array.
这意味着，通过修改底层数组，可以高效地访问和修改元素。

All standard Java collections, as well as most custom and extended implementations(such as Trove and Google's Guava), use underlying arrays(either primitive- or object-based).
所有标准的Java集合，包括定制和扩展的实现(比如Trove和Google的Guava),底层都使用了数组(原生数据类型或者基于对象的类型)。

A slices does not store any data, it just describes a section of an underlying array.
切片并不存储任何数据，只是描述了底层数组中的一段。

As mentioned earlier, re-slicing a slice doesn't make a copy of the underlying array.
正如前面所说，切片操作并不会复制底层的数组。

When a slice is passed to a function, even though it's passed by value, the pointer variable will refer to the same underlying array.
当切片传递给函数时，即使它通过值传递，指针变量也将引用相同的底层数组。

When a slice is passed to a function, even though it's passed by value, the pointer variable will refer to the same underlying array.
当把一个slice传递给函数之后，虽然它是值传递，但是指针变量将指向同一个底层数组。

This creates a new tall array variable with underlying data type double.
这将创建一个新的tall数组变量，其基础数据类型为double。

It is often used for passwords, digital fingerprints, or data with an underlying byte array..
它通常用于密码，数字指纹，或以字节数组为基础的数据。

But since the underlying implementation is an array, the array must be resized if you add a lot of elements.
但是由于底层实现是数组，因此如果添加很多元素，则必须调整数组大小。

The Torch Tensor and NumPy array will share their underlying memory locations, and changing one will change the other.
TorchTensor和NumPy数组将会共享它们的实际的内存位置,改变一个另一个也会跟着改变.