I am not understanding how exactly pytorch will implement a set of data points to a given model.
Suppose I have a csv file: ‘train.csv’ with two attributes: ‘x’ and ‘y’ (where we supposse ‘x’ is independent, ‘y’ is independent) and there are 700 samples.

train = pd.read_csv(‘C:\Users\hgstr\Jupyter_Files\Data_Sets\linear_regression\train.csv’)

Then using pandas I extract the data into a dataframe. Suppose afterwards I put both attributes into their own separate Tensors.

x = torch.Tensor(train[‘x’])
y = torch.Tensor(train[‘y’])

How do I ‘reshape’ or ‘view’ x and y so that I don’t get any size mismatch or get unexpected data-type?