class_docstr: This class is the entry-point to build neural network formulations.
  
  This class iterates over all nodes in the neural network and for
  each one them, generates the constraints to represent the layer
  and its activation function.
  
  Parameters
  ----------
  network_structure : NetworkDefinition
      the neural network definition
  layer_constraints : dict-like or None
      overrides the constraints generated for the specified layer types
  activation_constraints : dict-like or None
      overrides the constraints generated for the specified activation functions
class_docstr_tok: ["This","class","is","the","entry-point","to","build","neural","network","formulations",".","This","class","iterates","over","all","nodes","in","the","neural","network","and","for","each","one","them",",","generates","the","constraints","to","represent","the","layer","and","its","activation","function",".","Parameters","--","--","--","--","--","network_structure",":","NetworkDefinition","the","neural","network","definition","layer_constraints",":","dict-like","or","None","overrides","the","constraints","generated","for","the","specified","layer","types","activation_constraints",":","dict-like","or","None","overrides","the","constraints","generated","for","the","specified","activation","functions"]
docstr: The indexes of the formulation inputs.
docstr_tok: ["The","indexes","of","the","formulation","inputs","."]
code: def input_indexes(self):
      """The indexes of the formulation inputs."""
      network_inputs = list(self.__network_definition.input_nodes)
      if len(network_inputs) != 1:
          raise ValueError(
              "Multiple input layers are not currently supported."
          )
      return network_inputs[0].input_indexes
  
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 5 for calling the FullSpaceNNFormulation obj's underlying member method code with example usage: obj.input_indexes() and returns: network_inputs