class_docstr: Wraps a model that returns dict or list and returns only a specific element.
  
  Usage example:
      >>> model = MyAwesomeSegmentationModel() # Returns dict {"OUTPUT_MASK": Tensor, ...}
      >>> net  = nn.Sequential(PickModelOutput(model, "OUTPUT_MASK")), nn.Sigmoid())
code: def __init__(self, model: nn.Module, key: Union[str, int]):
      super().__init__()
      self.model = model
      self.target_key = key
  
imports: import torch
  from torch import nn, Tensor
  from typing import List, Union, Iterable, Optional, Dict, Tuple
  from pytorch_toolbelt.inference.tta import _deaugment_averaging
label_desc: Use image node_id 1 to create a new PickModelOutput object from inherited base classes: nn with example: obj = PickModelOutput(model, key)