class_docstr: Implements an Atomic Convolution Model.
  
  Implements the atomic convolutional networks as introduced in
  
  Gomes, Joseph, et al. "Atomic convolutional networks for predicting protein-ligand binding affinity." arXiv preprint arXiv:1703.10603 (2017).
  
  The atomic convolutional networks function as a variant of
  graph convolutions. The difference is that the "graph" here is
  the nearest neighbors graph in 3D space. The AtomicConvModel
  leverages these connections in 3D space to train models that
  learn to predict energetic state starting from the spatial
  geometry of the model.
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docstr: Loads model from joblib file on disk.
docstr_tok: ["Loads","model","from","joblib","file","on","disk","."]
code: def reload(self):
      """Loads model from joblib file on disk."""
      self.model = load_from_disk(
          self.get_model_filename(self.model_dir)
      )
  
file_path: deepchem/deepchem/models/atomic_conv.py
imports: import logging
  from deepchem.models import KerasModel
  from deepchem.models.layers import AtomicConvolution
  from deepchem.models.losses import L2Loss
  from tensorflow.keras.layers import Input, Dense, Reshape, Dropout, Activation, Lambda, Flatten, Concatenate
  import numpy
  import tensorflow
  import itertools
  from collections.abc import Sequence
  from typing import Sequence
  from deepchem.utils.typing import ActivationFn, OneOrMany
  from deepchem.utils.data_utils import load_from_disk, save_to_disk
label_desc: Use image node_id 4 for calling the AtomicConvModel obj's underlying member method code with example usage: obj.reload() without return types