title: Changing graph dataset matrices from sparse format to dense
body: <p>I am trying to use the CoRA dataset to train a graph neural network on tensorflow for the first time. The features and adjacency matrices provided by the dataset comes in a sparse representation but I don't need it here. Thus, I want to use numpy's todense() but it turns out it doesn't exist. For your reference, here is the relevant code:</p>
  <pre><code>import tensorflow as tf
  import numpy as np
  from spektral.datasets import citation
  
  cora_dataset = spektral.datasets.citation.Citation(name='cora')
  test_mask = cora_dataset.mask_te
  train_mask = cora_dataset.mask_tr
  val_mask = cora_dataset.mask_va
  graph = cora_dataset.graphs[0]
  features = graph.x
  adj = graph.a
  labels = graph.y
  
  features = features.todense()
  </code></pre>
  <p>and the error is: &quot;AttributeError: 'numpy.ndarray' object has no attribute 'todense'&quot;</p>
  <p>I would like to know if there has been a replacement for todense() or any other ways to convert sparse representations to dense.</p>