parameters: (self, net, features, iteration, params, version)
docstring: Args:
              params: the weight and bias, passed by either add_ops or
              add_train_ops function
  
              features: feature blobs to predict on. Can be the actual cur_layer
              or the bootstrapped_feature blobs.
  
              version: currently fp32 support only
docstring_summary: Args:
              params: the weight and bias, passed by either add_ops or
              add_train_ops function
docstring_tokens: Args : params : the weight and bias passed by either add_ops or add_train_ops function
function: def _add_ops(self, net, features, iteration, params, version):
          """
          Args:
              params: the weight and bias, passed by either add_ops or
              add_train_ops function
  
              features: feature blobs to predict on. Can be the actual cur_layer
              or the bootstrapped_feature blobs.
  
              version: currently fp32 support only
          """
  
          if self.clip_args is not None:
              clipped_params = [net.NextScopedBlob("clipped_%s" % str(p)) for p in params]
              for p, cp in zip(params, clipped_params):
                  net.Clip([p], [cp], **self.clip_args)
              params = clipped_params
  
          if self.output_dim_vec is None or len(self.output_dim_vec) == 1:
              self._insert_fc_ops(
                  net=net,
                  features=features,
                  params=params,
                  outputs=[self.output_schema.field_blobs()[(iteration * 2) + 1]],
                  version=version,
              )
function_tokens: def _add_ops ( self , net , features , iteration , params , version ) : if self . clip_args is not None : clipped_params = [ net . NextScopedBlob ( "clipped_%s" % str ( p ) ) for p in params ] for p , cp in zip ( params , clipped_params ) : net . Clip ( [ p ] , [ cp ] , * * self . clip_args ) params = clipped_params if self . output_dim_vec is None or len ( self . output_dim_vec ) == 1 : self . _insert_fc_ops ( net = net , features = features , params = params , outputs = [ self . output_schema . field_blobs ( ) [ ( iteration * 2 ) + 1 ] ] , version = version , )