class_docstr: This class is used to build partition-based formulations
  of neural networks.
  
  Parameters
  ----------
  network_structure : NetworkDefinition
      the neural network definition
  split_func : callable
      the function used to compute the splits
class_docstr_tok: ["This","class","is","used","to","build","partition-based","formulations","of","neural","networks",".","Parameters","--","--","--","--","--","network_structure",":","NetworkDefinition","the","neural","network","definition","split_func",":","callable","the","function","used","to","compute","the","splits"]
code: def __init__(self, network_structure, split_func=None):
      super().__init__()
  
      self.__network_definition = network_structure
      self.__scaling_object = network_structure.scaling_object
      self.__scaled_input_bounds = network_structure.scaled_input_bounds
  
      if split_func is None:
          split_func = lambda w: default_partition_split_func(w, 2)
  
      self.__split_func = split_func
  
imports: import numpy
  import pyomo.environ
  from omlt.formulation import _PyomoFormulation, _setup_scaled_inputs_outputs
  from omlt.neuralnet.activations import ACTIVATION_FUNCTION_MAP
  from omlt.neuralnet.activations import ComplementarityReLUActivation, bigm_relu_activation_constraint, linear_activation_constraint, linear_activation_function, sigmoid_activation_constraint, sigmoid_activation_function, softplus_activation_constraint, softplus_activation_function, tanh_activation_constraint, tanh_activation_function
  from omlt.neuralnet.layer import ConvLayer2D, DenseLayer, InputLayer, PoolingLayer2D
  from omlt.neuralnet.layers.full_space import full_space_conv2d_layer, full_space_dense_layer, full_space_maxpool2d_layer
  from omlt.neuralnet.layers.partition_based import default_partition_split_func, partition_based_dense_relu_layer
  from omlt.neuralnet.layers.reduced_space import reduced_space_dense_layer
label_desc: Use image node_id 1 to create a new ReluPartitionFormulation object from inherited base classes: _PyomoFormulation with example: obj = ReluPartitionFormulation(network_structure, split_func)