class_docstr: Output layer for multilabel sequence classification models.
  
  Args:
      outputs (Dict[str, ClassificationOutputLayer]): Output for multilabels
      optional label_weights (Dict[str, int]): Dict of label_names along with the
      weight for label
docstr: Exports the multilabel output layer to Caffe2.
  See `OutputLayerBase.export_to_caffe2()` for details.
docstr_tok: ["Exports","the","multilabel","output","layer","to","Caffe2",".","See","`","OutputLayerBase.export_to_caffe2","(",")","`","for","details","."]
code: def export_to_caffe2(
      self,
      workspace: core.workspace,
      init_net: core.Net,
      predict_net: core.Net,
      model_out: List[torch.Tensor],
      out_name: str,
  ) -> List[core.BlobReference]:
      """
      Exports the multilabel output layer to Caffe2.
      See `OutputLayerBase.export_to_caffe2()` for details.
      """
  
      return functools.reduce(
          operator.add,
          [
              output_layer.export_to_caffe2(
                  workspace,
                  init_net,
                  predict_net,
                  single_output,
                  out_name,
              )
              for output_layer, single_output in zip(
                  self.outputs, model_out
              )
          ],
      )
  
imports: import functools
  import operator
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  import torch.nn
  from caffe2.python import core
  from pytext.data.tensorizers import Tensorizer
  from pytext.utils.usage import log_class_usage
  from torch import jit
  from .doc_classification_output_layer import ClassificationOutputLayer
  from .output_layer_base import OutputLayerBase
label_desc: Use image node_id 5 for calling the MultiLabelClassificationLayer obj's underlying member method code with example usage: obj.export_to_caffe2(workspace, init_net, predict_net, model_out, out_name) and returns: functools