code: def params():
      for values in itertools.product(
          radial1, radial2, radial3, layer_sizes, learning_rates, epochs
      ):
          d = {
              "frag1_num_atoms": 140,
              "frag2_num_atoms": 821,
              "complex_num_atoms": 908,
              "radial": [values[0], values[1], values[2]],
              "layer_sizes": values[3],
              "learning_rate": values[4],
              "epochs": values[5],
          }
          yield d
  
file_path: deepchem/contrib/atomicconv/acnn/core/tensor_graph_hyper_param_search.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import os
  import sys
  import deepchem
  import json
  import numpy
  import tensorflow
  from deepchem.models.atomic_conv import atomic_conv_model
  from deepchem.models.tensorgraph.layers import Layer, Feature, Label, L2Loss, AtomicConvolution, Transpose, Dense
  from deepchem.models import TensorGraph
  import numpy
  import tensorflow
  import itertools
  import time
label_desc: Use image node_id 1 for calling a global function with example usage: params() without return types