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 from_config(
      cls, config: Config, label_tensorizers: [Dict[str, Tensorizer]]
  ):
      modules = {
          name: ClassificationOutputLayer.from_config(
              config.outputs[idx], labels=tensorizer.vocab
          )
          for idx, (name, tensorizer) in enumerate(
              label_tensorizers.items()
          )
      }
  
      return cls(modules, config.label_set_weights)
  
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 1 for calling the MultiLabelClassificationLayer obj's underlying member method code with example usage: obj.from_config(cls, config, label_tensorizers) and returns: cls