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I have a query that I am using qiskit.aqua.algorithms.QGAN library and using the qgan function and I am having difficulty in loading data of dimension 1000 rows x 10 columns. I am following qiskit's github tutorial for qgan learning and loading, however I did tested out that the tutorial was with 1000 rows and 1 column and is working fine. It did works till 2 columns but for more than 2 columns the function returns error. I don't understand if the same dataset is of 1 or 2 column it works but as soon as the data is reshaped to 3 or more columns, it returns this error:

AttributeError                            Traceback (most recent call last)
<ipython-input-36-1df19589f511> in <module>
      6 
      7  # Initialize qGAN
----> 8 qgan = QGAN(real_data, bounds, num_qubits, batch_size, num_epochs, snapshot_dir=None)
      9 qgan.seed = 1
D:\Users\Bro\anaconda3\lib\site-packages\qiskit\aqua\algorithms\distribution_learners\qgan.py in __init__(self, data, bounds, num_qubits, batch_size, num_epochs, seed, discriminator, generator, tol_rel_ent, snapshot_dir, quantum_instance)
    126                 self._num_qubits = np.array([3])
    127         self._data, self._data_grid, self._grid_elements, self._prob_data = \
--> 128             discretize_and_truncate(self._data, self._bounds, self._num_qubits,
    129                                     return_data_grid_elements=True,
    130                                     return_prob=True, prob_non_zero=True)
D:\Users\Bro\anaconda3\lib\site-packages\qiskit\aqua\utils\dataset_helper.py in discretize_and_truncate(data, bounds, num_qubits, return_data_grid_elements, return_prob, prob_non_zero)
    214             for grid_element in grid_elements:
    215                 for element_current in elements_current_dim:
--> 216                     temp.append(deepcopy(grid_element).append(element_current))
    217             grid_elements = deepcopy(temp)
    218             data_grid.append(elements_current_dim)
AttributeError: 'NoneType' object has no attribute 'append'

Any solution for the same?? PS:- I am trying to load even bigger dimensional dataset so please help in this.

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