#### Eigen equivalent to arithmetic with Numpy Brodcasting (3D)

In numpy i have the following

``````X1 = np.arange(1, 6).reshape(-1, 1)
X1 = np.hstack([X1, X1])

# Output: X1
# array([ [1, 1],
#         [2, 2],
#         [3, 3],
#         [4, 4],
#         [5, 5] ])

X2 = X1[:, np.newaxis, :]
X3 = X1[np.newaxis, :, :]

# Output: Shapes
# X2.shape = (5, 1, 2)
# X3.shape = (1, 5, 2)

X4 = (X2 - X3) ** 2

# Output: X4
# X4.shape = (5, 5, 2)
# X4[:, :, 0] =
# array([ [ 0,  1,  4,  9, 16],
#         [ 1,  0,  1,  4,  9],
#         [ 4,  1,  0,  1,  4],
#         [ 9,  4,  1,  0,  1],
#         [16,  9,  4,  1,  0] ], dtype=int32)
# X4[:, :, 1] =
# array([ [ 0,  1,  4,  9, 16],
#         [ 1,  0,  1,  4,  9],
#         [ 4,  1,  0,  1,  4],
#         [ 9,  4,  1,  0,  1],
#         [16,  9,  4,  1,  0] ], dtype=int32)
``````

Is this type of operations do-able in Eigen C++, if so how?

I would need this because I will later have to divide `X4` by another (Row) vector with the same number of columns as `X1`. I understand Eigen has its broadcasting operations (https://eigen.tuxfamily.org/dox/group__TutorialReductionsVisitorsBroadcasting.html) however my numpy implementation works with 3D matrices.

Source: Windows Questions C++