Is there any reason for using the word "column" in the context of one-dimensional tensor?
Consider the following statements from the chapter named Tensors: Multidimensional arrays from the textbook titled Deep Learning with PyTorch by Eli Stevens et al.

Let’s construct our first PyTorch tensor and see what it looks like.
It won’t be a particularly meaningful tensor for now, just three ones
in a column:
# In[4]:
import torch
a = torch.ones(3)
a


In general, the notion of the column is used in the case of at least two dimensions. The tensor initialized is of a single dimension. So, I am guessing that it is immaterial if we use either row or column.
Am I true? Else, Is there any reason behind using the word &quot;column&quot; in this context?

Often, in linear algebra theory, an n-dimensional vector is considered as a n x 1 matrix, called a column vector.
Indeed, the behavior of a tensor t with shape (n,) is very similar to that of a tensor u of shape (n, 1). In mathematical terms, you can think of a vector t in R^n and a vector u in R^{n x 1}.
In conclusion, the author, perhaps, is suggesting to treat the tensor as a mathematical column vector.
