class_docstr: Most of ImageNet 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``.
  
      force_global_pooling (bool):
          Regardless the input image size, the final average pooling before classification layer will be automatically transformed to a global average pooling. The default is ``False``.
  
      check_global_pooling (bool):
          If ``True``, and if the stride configuration of the final average pooling is not for global pooling, it raises an exception. The default is ``True``. Use ``False`` when user want to do the pooling with the trained stride ``(7, 7)`` regardless the input spatial size.
  
      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.
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code: def __call__(
      self,
      input_var=None,
      use_from=None,
      use_up_to="classifier",
      training=False,
      force_global_pooling=False,
      check_global_pooling=True,
      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``.
  
          force_global_pooling (bool):
              Regardless the input image size, the final average pooling before classification layer will be automatically transformed to a global average pooling. The default is ``False``.
  
          check_global_pooling (bool):
              If ``True``, and if the stride configuration of the final average pooling is not for global pooling, it raises an exception. The default is ``True``. Use ``False`` when user want to do the pooling with the trained stride ``(7, 7)`` regardless the input spatial size.
  
          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/imagenet/base.py
imports: from __future__ import absolute_import
  import nnabla
  from nnabla.utils.nnp_graph import NnpLoader
  from ..utils import *
label_desc: Use image node_id 8 for calling the ImageNetBase obj's underlying member method code with example usage: obj.__call__(input_var, use_from, use_up_to, training, force_global_pooling, check_global_pooling, returns_net, verbose) without return types