class_docstr: Metadata to be used for serializaing a net.
  
  parameters, inputs, outputs could be either BlobReference or blob's names
  
  predict_net can be either core.Net, NetDef, PlanDef or object
  
  Override the named tuple to provide optional name parameter.
  name will be used to identify multiple prediction nets.
  
  net_type is the type field in caffe2 NetDef - can be 'simple', 'dag', etc.
  
  num_workers specifies for net type 'dag' how many threads should run ops
  
  trainer_prefix specifies the type of trainer.
  
  extra_init_net gets appended to pred_init_net, useful for thread local init
  
  global_init_net gets appended to global_init_net, useful for global init
  on a shared across threads parameter workspace
  (in a case of multi-threaded inference)
code: def predict_net_name(self):
      return utils.get_comp_name(
          predictor_constants.PREDICT_NET_TYPE, self.name
      )
  
imports: from caffe2.proto import caffe2_pb2
  from caffe2.proto import metanet_pb2
  from caffe2.python import workspace, core, scope
  from caffe2.python.predictor_constants import predictor_constants
  import caffe2.python.predictor.serde
  import caffe2.python.predictor.predictor_py_utils
  import collections
label_desc: Use image node_id 7 for calling the PredictorExportMeta obj's underlying member method code with example usage: obj.predict_net_name() and returns: utils