class_docstr: Calculate symmetry function for each atom in the molecules
  
  This method is described in [1]_.
  
  Examples
  --------
  >>> import deepchem as dc
  >>> smiles = ['C1C=CC=CC=1']
  >>> featurizer = dc.feat.BPSymmetryFunctionInput(max_atoms=10)
  >>> features = featurizer.featurize(smiles)
  >>> type(features[0])
  <class 'numpy.ndarray'>
  >>> features[0].shape  # (max_atoms, 4)
  (10, 4)
  
  References
  ----------
  .. [1] Behler, Jörg, and Michele Parrinello. "Generalized neural-network
      representation of high-dimensional potential-energy surfaces." Physical
      review letters 98.14 (2007): 146401.
  
  Note
  ----
  This class requires RDKit to be installed.
docstr: Initialize this featurizer.
  
  Parameters
  ----------
  max_atoms: int
      The maximum number of atoms expected for molecules this featurizer will
      process.
docstr_tok: ["Initialize","this","featurizer",".","Parameters","--","--","--","--","--","max_atoms",":","int","The","maximum","number","of","atoms","expected","for","molecules","this","featurizer","will","process","."]
code: def __init__(self, max_atoms: int):
      """Initialize this featurizer.
  
      Parameters
      ----------
      max_atoms: int
          The maximum number of atoms expected for molecules this featurizer will
          process.
  
      """
      self.max_atoms = max_atoms
      self.coordfeat = AtomicCoordinates(use_bohr=True)
  
file_path: deepchem/deepchem/feat/molecule_featurizers/bp_symmetry_function_input.py
imports: import numpy
  from deepchem.utils.typing import RDKitMol
  from deepchem.utils.data_utils import pad_array
  from deepchem.feat.base_classes import MolecularFeaturizer
  from deepchem.feat.molecule_featurizers.atomic_coordinates import AtomicCoordinates
label_desc: Use image node_id 1 to create a new BPSymmetryFunctionInput object from inherited base classes: MolecularFeaturizer with example: obj = BPSymmetryFunctionInput(max_atoms)