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def ShortGI(dist, charge1,charge2,):
    #These are precomputed coefficients:
    a=np.array([[ 15.90600036,   3.9534831 ,  17.61453176],
                [  3.9534831 ,   5.21580206,   1.91045387],
                [ 17.61453176,   1.91045387, 238.75820253]])
    b=np.array([[-0.02495   , -0.04539319, -0.00247124],
                [-0.04539319, -0.2513    , -0.00258662],
                [-0.00247124, -0.00258662, -0.0013    ]])
    
    a_flat = a.flatten()

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from rdkit.Chem.Draw import rdMolDraw2D
from IPython.display import SVG
from rdkit import Chem
from rdkit.Chem import Draw
mol = Chem.MolFromSmiles(Chem.MolToSmiles(l))
beez = mol_to_bits(mol)

def mol_to_bits(mol):
    """Convert the atoms in a molecule into bit IDs. 
    This uses the morgan fingerprint with a depth of zero -

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import keras
import tensorflow as tf
class D2M(keras.layers.Layer):
    """
    Converts an EDM `D` to its Gram matrix representation `M`.
    """

    def call(self, inputs, **kwargs):
        batch_size = tf.shape(inputs)[0]
        n_atoms = tf.shape(inputs)[1]

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from rdkit import Chem
import numpy as np
from rdkit.Chem import AllChem
from rdkit.Geometry import Point3D

from scipy.spatial.distance import squareform, pdist

import copy

import py3Dmol