class_docstr: Faster RCNN Network module
  
  Builds the Faster RCNN network architecture using different submodules.
  Calculates the total loss of the model based on the different losses by
  each of the submodules.
  
  It is also responsible for building the anchor reference which is used in
  graph for generating the dynamic anchors.
class_docstr_tok: ["Faster","RCNN","Network","module","Builds","the","Faster","RCNN","network","architecture","using","different","submodules",".","Calculates","the","total","loss","of","the","model","based","on","the","different","losses","by","each","of","the","submodules",".","It","is","also","responsible","for","building","the","anchor","reference","which","is","used","in","graph","for","generating","the","dynamic","anchors","."]
docstr: Get trainable vars included in the module.
          
docstr_tok: ["Get","trainable","vars","included","in","the","module","."]
code: def get_trainable_vars(self):
      """Get trainable vars included in the module."""
      trainable_vars = snt.get_variables_in_module(self)
      if self._config.model.base_network.trainable:
          pretrained_trainable_vars = (
              self.base_network.get_trainable_vars()
          )
          if len(pretrained_trainable_vars):
              tf.logging.info(
                  "Training {} vars from pretrained module; "
                  'from "{}" to "{}".'.format(
                      len(pretrained_trainable_vars),
                      pretrained_trainable_vars[0].name,
                      pretrained_trainable_vars[-1].name,
                  )
              )
          else:
              tf.logging.info(
                  "No vars from pretrained module to train."
              )
          trainable_vars += pretrained_trainable_vars
      else:
          tf.logging.info(
              "Not training variables from pretrained module"
          )
  
      return trainable_vars
  
file_path: luminoth/luminoth/models/fasterrcnn/fasterrcnn.py
imports: import numpy
  import sonnet
  import tensorflow
  from luminoth.models.fasterrcnn.rcnn import RCNN
  from luminoth.models.fasterrcnn.rpn import RPN
  from luminoth.models.base import TruncatedBaseNetwork
  from luminoth.utils.anchors import generate_anchors_reference
  from luminoth.utils.vars import VAR_LOG_LEVELS, variable_summaries
label_desc: Use image node_id 7 for calling the FasterRCNN obj's underlying member method code with example usage: obj.get_trainable_vars() and returns: trainable_vars