torch.Tensor.expand

Tensor.expand(*sizes) → Tensor

Returns a new view of the self tensor with singleton dimensions expanded to a larger size.
返回一个新的视图，其中self张量的单一维度扩展到更大的尺寸。

Passing -1 as the size for a dimension means not changing the size of that dimension.
将某个维度的大小设为-1 表示不改变该维度的大小。

Tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. For the new dimensions, the size cannot be set to -1.
张量也可以扩展到更多的维度，新维度将添加在前面。对于新维度，大小不能设为-1。

Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the stride to 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.
扩展张量不会分配新的内存，只会在现有张量上创建一个新的视图，其中大小为 1 的维度通过将stride设置为 0 扩展到更大的尺寸。任何大小为 1 的维度都可以扩展到任意值而无需分配新内存。

Parameters 参数

*sizes (torch.Size or int...) – the desired expanded size
*sizes (torch.Size 或 int...) – 所需的扩展尺寸

Warning 警告

More than one element of an expanded tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.
扩展张量的多个元素可能指向同一个内存位置。因此，就地操作（尤其是向量化操作）可能会导致不正确的行为。如果需要对张量进行写操作，请先克隆它们。

Example: 示例：

>>> x = torch.tensor([[1], [2], [3]])
>>> x.size()
torch.Size([3, 1])
>>> x.expand(3, 4)
tensor([[ 1,  1,  1,  1],
        [ 2,  2,  2,  2],
        [ 3,  3,  3,  3]])
>>> x.expand(-1, 4)   # -1 means not changing the size of that dimension
tensor([[ 1,  1,  1,  1],
        [ 2,  2,  2,  2],
        [ 3,  3,  3,  3]])