##### https://github.com/rballester/tntorch

Tip revision:

**3af563a42794ba169e7902198d1edd919617a958**authored by**Rafael Ballester**on**16 March 2023, 15:48:54 UTC****Updated doc (ranks_cp actually must be an integer, not a list)** Tip revision:

**3af563a**util.py

```
import numpy as np
import tntorch as tn
def random_format(shape):
"""
Generate a random tensor of random format (often hybrid) with the given shape
:param shape:
:return: a tensor
"""
N = len(shape)
if np.random.randint(4) == 0:
ranks_tucker = None
else:
ranks_tucker= [None]*N
for n in sorted(np.random.choice(N, np.random.randint(N+1), replace=False)):
ranks_tucker[n] = np.random.randint(1, 5)
if np.random.randint(4) == 0:
ranks_tt = None
ranks_cp = np.random.randint(1, 5)
elif np.random.randint(4) == 0:
ranks_cp = None
ranks_tt = np.random.randint(1, 5, N-1)
else:
ranks_tt = list(np.random.randint(1, 5, N-1))
ranks_cp = [None]*N
for n in sorted(np.random.choice(N, np.random.randint(N+1), replace=False)):
if n > 0 and ranks_cp[n-1] is not None:
r = ranks_cp[n-1]
else:
r = np.random.randint(1, 5)
ranks_cp[n] = r
if n > 0:
ranks_tt[n-1] = None
if n < N-1:
ranks_tt[n] = None
return tn.randn(shape, ranks_tt=ranks_tt, ranks_cp=ranks_cp, ranks_tucker=ranks_tucker)
```