code: def __init__(
      self,
      input_dimensions: _typing.Iterable[int],
      output_dimension: int,
      dropout: float,
  ):
      super(_TopKPoolDecoder, self).__init__()
      k: int = min(len(list(input_dimensions)), 3)
      self.__pool: SortPooling = SortPooling(k)
      self.__linear_predictions: torch.nn.ModuleList = (
          torch.nn.ModuleList()
      )
      for layer, dimension in enumerate(input_dimensions):
          self.__linear_predictions.append(
              torch.nn.Linear(dimension * k, output_dimension)
          )
      self._dropout = torch.nn.Dropout(dropout)
  
imports: import torch.nn.functional
  import typing
  import dgl
  from dgl.nn.pytorch.glob import SumPooling, AvgPooling, MaxPooling, SortPooling
  from ..None import base_decoder, decoder_registry
  from ...encoders import base_encoder
  from ...None import _utils
label_desc: Use image node_id 1 to create a new _TopKPoolDecoder object from inherited base classes: torch.nn with example: obj = _TopKPoolDecoder(input_dimensions, output_dimension, dropout)