code: def compute_ecfp_features(mol, ecfp_degree=2, ecfp_power=11):
      """Computes ECFP features for provided rdkit molecule.
      Parameters
      ----------
      mol: rdkit molecule
          Molecule to featurize.
      ecfp_degree: int
          ECFP radius
      ecfp_power: int
          Number of bits to store ECFP features (2^ecfp_power will be length of
          ECFP array)
      Returns
      -------
      ecfp_array: np.ndarray
          Returns an array of size 2^ecfp_power where array at index i has a 1 if
          that ECFP fragment is found in the molecule and array at index j has a 0
          if ECFP fragment not in molecule.
      """
      from rdkit.Chem import AllChem
  
      bv = AllChem.GetMorganFingerprintAsBitVect(
          mol, ecfp_degree, nBits=2**ecfp_power
      )
      return np.array(bv)
  
imports: import os
  import logging
  import itertools
  import numpy
  from io import StringIO
  from deepchem.utils.pdbqt_utils import pdbqt_to_pdb
  from deepchem.utils.pdbqt_utils import convert_mol_to_pdbqt
  from deepchem.utils.pdbqt_utils import convert_protein_to_pdbqt
  from deepchem.utils.geometry_utils import compute_pairwise_distances
  from deepchem.utils.geometry_utils import compute_centroid
  from deepchem.utils.fragment_utils import MolecularFragment
  from deepchem.utils.fragment_utils import MoleculeLoadException
  from typing import Any, List, Sequence, Tuple, Set, Optional, Dict, Union
  from deepchem.utils.typing import OneOrMany, RDKitMol
label_desc: Use image node_id 11 for calling a global function with example usage: compute_ecfp_features(mol, ecfp_degree, ecfp_power) and returns: np