[BUG] qml.data.load() fails when using 'full' parameter value

[DSGuala]

Expected behavior

qml.data.load('qspin',sysname='Heisenberg',periodicity='open',lattice='full',layout='2x2')
Should return the Heisenberg dataset corresponding to the open periodicity, rectangular lattice, and 2x2 layout.

Actual behavior

qml.data.load('qspin',sysname='Heisenberg',periodicity='open',lattice='full',layout='2x2')

Raises ValueError: layout value of '2x2' is not available. Available values are ['1x16', '1x4', '1x8']

Additional information

No response

Source code

qml.data.load('qspin',sysname='Heisenberg',periodicity='open',lattice='full',layout='1x4') # works

qml.data.load('qspin',sysname='Heisenberg',periodicity='open',lattice='full',layout='2x2') # fails

Tracebacks

No response

System information

Name: PennyLane
Version: 0.31.0.dev0
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/XanaduAI/pennylane
Author: 
Author-email: 
License: Apache License 2.0
Location: .venv/lib/python3.8/site-packages
Editable project location: 
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml
Required-by: PennyLane-Lightning

Platform info:           Linux-5.15.0-73-generic-x86_64-with-glibc2.29
Python version:          3.8.10
Numpy version:           1.23.5
Scipy version:           1.10.1
Installed devices:
- default.gaussian (PennyLane-0.31.0.dev0)
- default.mixed (PennyLane-0.31.0.dev0)
- default.qubit (PennyLane-0.31.0.dev0)
- default.qubit.autograd (PennyLane-0.31.0.dev0)
- default.qubit.jax (PennyLane-0.31.0.dev0)
- default.qubit.tf (PennyLane-0.31.0.dev0)
- default.qubit.torch (PennyLane-0.31.0.dev0)
- default.qutrit (PennyLane-0.31.0.dev0)
- null.qubit (PennyLane-0.31.0.dev0)
- lightning.qubit (PennyLane-Lightning-0.30.0)

Existing GitHub issues

• I have searched existing GitHub issues to make sure the issue does not already exist.

[timmysilv]

this can be addressed as part of the new HDF5 datasets coming soon (#4097)