class_docstr: A graph object which is read from nnp file.
  
  
  An instance of NnpNetwork is usually created by an NnpLoader instance.
  See an example usage described in :obj:`NnpLoader`.
  
  
  Attributes:
      variables (dict): A dict of all variables in a created graph
          with a variable name as a key, and a nnabla.Variable as a value.
  
      inputs (dict): All input variables.
  
      outputs (dict): All output variables.
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code: def __init__(self, proto_network, batch_size, callback):
      proto_network = proto_network.expand_loop_control()
      self.proto_network = proto_network.promote(callback)
      self.proto_network(batch_size=batch_size)
      for k, v in itertools.chain(
          self.proto_network.variables.items(),
          self.proto_network.parameters.items(),
      ):
          if v.variable_instance is not None:
              v.variable_instance.name = k
      self._inputs = {
          i: self.proto_network.variables[i].variable_instance
          for i in self.proto_network.inputs
      }
      self._outputs = {
          i: self.proto_network.variables[i].variable_instance
          for i in self.proto_network.outputs
      }
      self._variables = {
          k: v.variable_instance
          for k, v in itertools.chain(
              self.proto_network.variables.items(),
              self.proto_network.parameters.items(),
          )
      }
  
      # publish network's parameters to current parameter scope
      # like original implementation.
      with nn.parameter_scope("", nn.get_current_parameter_scope()):
          for k, v in self.proto_network.parameters.items():
              nn.parameter.set_parameter(k, v.variable_instance)
  
imports: from __future__ import print_function
  import itertools
  from collections import OrderedDict
  import nnabla
  import numpy
label_desc: Use image node_id 1 to create a new NnpNetwork object from inherited base classes: object with example: obj = NnpNetwork(proto_network, batch_size, callback)