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: Pads an array to the desired shape.
  
  Parameters
  ----------
  array: np.ndarray
    Array to be padded.
  shape: int or Tuple
    Shape the array is padded to.
  
  Returns
  ----------
  array: np.ndarray
      Array padded to input shape.
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code: def _pad_array(self, array: np.ndarray, shape: Any) -> np.ndarray:
      """Pads an array to the desired shape.
  
      Parameters
      ----------
      array: np.ndarray
        Array to be padded.
      shape: int or Tuple
        Shape the array is padded to.
  
      Returns
      ----------
      array: np.ndarray
          Array padded to input shape.
  
      """
      result = np.zeros(shape=shape)
      slices = tuple(slice(s) for s in array.shape)
      result[slices] = array
      return result
  
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 6 for calling the MATFeaturizer obj's underlying member method code with example usage: obj._pad_array(array, shape) and returns: result