解：
（1）Create a tensor a from list(range(9)). Predict and then check the size, offset, and stride.
创建列表a
将其转化为张量
a.size：The size (or shape, in NumPy parlance) is a tuple indicating how many elements across each dimension the tensor represents.
a.offset：The storage offset is the index in the storage corresponding to the first element in the tensor.
a.stride：The stride is the number of elements in the storage that need to be skipped over to obtain the next element along each dimension.
回顾：
（2）Create a new tensor using b = a.view(3, 3). What does view do? Check that a and b share the same storage.
A PyTorch Tensor instance is a view of such a Storage instance that is capable of indexing into that storage using an offset and per-dimension strides.
a.storage()与b.storage()均为
id(a.storage())==id(b.storage())
因此对b的修改也会影响a
例如：
（下文中a和b的值不修改，仍为0~8）
回顾：
（3）Create a tensor c = b[1:,1:]. Predict and then check the size, offset, and stride.
c取第1行及以后得行，和第1列及以后得列
c.size
c.storage()
c.offset
c的第一个元素4存储在内存地址4的位置
c.stride
因此从4到7需要3
从4到5需要1
注：
切片可以看成是指向内存地址的指针，对c的修改也会影响b
2.
解：
若出现报错可尝试将a整型改为浮点型，再进行数学运算
