More descriptive message when random PARAFAC2 rank is infeasible given shape

[Mr-Niraj-Kulkarni]

Describe the bug

When Testing the random parafrac2 function fails throwing a error " ValueError: All the projection matrices of a PARAFAC2 tensor should have the same number of columns as the rank. However, rank=2 but projections[0].shape[1]=1" while validating when parafrac2Tensor object is being created by projection_matrices, weights and factors.

Steps or Code to Reproduce

For instance:
Arguments passed to random Tensor are
shapes = [(1, 1), (1, 1)]
rank =2

Expected behavior

projection shape should have the same rank as rank passed both 2

Actual result

But getting projection rank as 1 and actual rank is 2
Please paste or specifically describe the actual output or traceback.

for i, projection in enumerate(projections):
            current_mode_size, current_rank = ivy.shape(projection)
            if current_rank != rank:
>               raise ValueError(
                    "All the projection matrices of a PARAFAC2 tensor should have the"
                    f" same number of columns as the rank. However, rank={rank} but"
                    f" projections[{i}].shape[1]={ivy.shape(projection)[1]}"
                )
E               ValueError: All the projection matrices of a PARAFAC2 tensor should have the same number of columns as the rank. However, rank=2 but projections[0].shape[1]=1

ivy/data_classes/factorized_tensor/parafac2_tensor.py:166: ValueError

Versions

[aarmey]

Thanks for the issue, @Mr-Niraj-Kulkarni. Does this ever happen with datasets of larger sizes? The rank cannot be larger than max(X.shape). We can certainly add a more descriptive error message for this situation.