Understanding PyTorch Tensor Shape
I have a simple question regarding the shape of tensor we define in PyTorch. Let's say if I say:

input = torch.randn(32, 35)


This will create a matrix with 32 row and 35 columns.
Now when I define:

input2 = torch.randn(1,2,32, 35)


What can I say about the dimension of the new matrix input2?
How can I define the rows and columns here? I mean do I have two matrices with shapes 32*35 packed by the tensor? 

I want to better understand the geometry behind this. Thanks. 

Consider tensor shapes as the number of lists that a dimension holds. For instance, a tensor shaped (4, 4, 2) will have four elements, which will all contain 4 elements, which in turn have 2 elements.

The first holds 4 elements.
The second holds 4 elements.
The third dimension holds 2 elements.


Here's what the data would look like:
[[[0.86471446, 0.26302726],
  [0.04137454, 0.00349315],   
  [0.06559607, 0.45617865],
  [0.0219786, 0.27513594]],

 [[0.60555118, 0.10853228],
  [0.07059685, 0.32746256],
  [0.99684617, 0.07496456],
  [0.55169005, 0.39024103]],

 [[0.55891377, 0.41151245],
  [0.3434965, 0.12956237],
  [0.74908291, 0.69889266],
  [0.98600141, 0.8570597]],

 [[0.7903229, 0.93017741],
  [0.54663242, 0.72318166],
  [0.6099451, 0.96090241],
  [0.63772238, 0.78605599]]]

In other words, four elements of four elements of two elements.
