class_docstr: This class is a featurizer for the Molecule Attention Transformer [1]_.
  The returned value is a numpy array which consists of molecular graph descriptions:
      - Node Features
      - Adjacency Matrix
      - Distance Matrix
  
  References
  ---------
  .. [1] Lukasz Maziarka et al. "Molecule Attention Transformer`<https://arxiv.org/abs/2002.08264>`"
  
  Examples
  --------
  >>> import deepchem as dc
  >>> feat = dc.feat.MATFeaturizer()
  >>> out = feat.featurize("CCC")
  
  Note
  ----
  This class requires RDKit to be installed.
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docstr: This function constructs a matrix of atom features for all atoms in a given molecule using the atom_features function.
  
  Parameters
  ----------
  mol: RDKitMol
      RDKit Mol object.
  
  Returns
  ----------
  Atom_features: ndarray
      Numpy array containing atom features.
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code: def construct_node_features_matrix(self, mol: RDKitMol) -> np.ndarray:
      """This function constructs a matrix of atom features for all atoms in a given molecule using the atom_features function.
  
      Parameters
      ----------
      mol: RDKitMol
          RDKit Mol object.
  
      Returns
      ----------
      Atom_features: ndarray
          Numpy array containing atom features.
  
      """
      return np.array(
          [self.atom_features(atom) for atom in mol.GetAtoms()]
      )
  
file_path: deepchem/deepchem/feat/molecule_featurizers/mat_featurizer.py
imports: from deepchem.feat.base_classes import MolecularFeaturizer
  from deepchem.utils.molecule_feature_utils import one_hot_encode
  from deepchem.utils.typing import RDKitMol, RDKitAtom
  import numpy
  from typing import Tuple, Any
  from dataclasses import dataclass
label_desc: Use image node_id 4 for calling the MATFeaturizer obj's underlying member method code with example usage: obj.construct_node_features_matrix(mol) and returns: np