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
code: def __init__(
      self,
      outputs: Dict[str, ClassificationOutputLayer],
      label_set_weights: Optional[Dict[str, float]] = None,
  ) -> None:
      super().__init__()
      self.outputs = outputs
      self.num_label_sets = len(outputs)
      self.label_set_weights = label_set_weights
      log_class_usage(__class__)
  
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 2 to create a new MultiLabelClassificationLayer object from inherited base classes: OutputLayerBase with example: obj = MultiLabelClassificationLayer(outputs, label_set_weights)