class_docstr: Semantic Segmentation pretrained models are inherited from this class
  so that it provides some common interfaces.
docstr: Create a network (computation graph) from a loaded model.
  
  Args:
  
      input_var (Variable, optional):
          If given, input variable is replaced with the given variable and a network is constructed on top of the variable. Otherwise, a variable with batch size as 1 and a default shape from ``self.input_shape``.
  
      use_up_to (str):
          Network is constructed up to a variable specified by a string. A list of string-variable correspondences in a model is described in documentation for each model class.
  
      training (bool):
          This option enables additional training (fine-tuning, transfer learning etc.) for the constructed network. If True, the ``batch_stat`` option in batch normalization is turned ``True``, and ``need_grad`` attribute in trainable variables (conv weights and gamma and beta of bn etc.) is turned ``True``. The default is ``False``.
  
      returns_net (bool):
          When ``True``, it returns a :obj:`~nnabla.utils.nnp_graph.NnpNetwork` object. Otherwise, It only returns the last variable of the constructed network. The default is ``False``.
      verbose (bool, or int):
          Verbose level. With ``0``, it says nothing during network construction.
code: def __call__(
      self,
      input_var=None,
      use_from=None,
      use_up_to="segmentation",
      training=False,
      returns_net=False,
      verbose=0,
  ):
      """
      Create a network (computation graph) from a loaded model.
  
      Args:
  
          input_var (Variable, optional):
              If given, input variable is replaced with the given variable and a network is constructed on top of the variable. Otherwise, a variable with batch size as 1 and a default shape from ``self.input_shape``.
  
          use_up_to (str):
              Network is constructed up to a variable specified by a string. A list of string-variable correspondences in a model is described in documentation for each model class.
  
          training (bool):
              This option enables additional training (fine-tuning, transfer learning etc.) for the constructed network. If True, the ``batch_stat`` option in batch normalization is turned ``True``, and ``need_grad`` attribute in trainable variables (conv weights and gamma and beta of bn etc.) is turned ``True``. The default is ``False``.
  
          returns_net (bool):
              When ``True``, it returns a :obj:`~nnabla.utils.nnp_graph.NnpNetwork` object. Otherwise, It only returns the last variable of the constructed network. The default is ``False``.
          verbose (bool, or int):
              Verbose level. With ``0``, it says nothing during network construction.
      """
      raise NotImplementedError()
  
file_path: nnabla/python/src/nnabla/models/semantic_segmentation/base.py
imports: from __future__ import absolute_import
  from nnabla.utils.nnp_graph import NnpLoader
  from ..utils import *
label_desc: Use image node_id 7 for calling the SemanticSegmentation obj's underlying member method code with example usage: obj.__call__(input_var, use_from, use_up_to, training, returns_net, verbose) without return types