class_docstr: Returns a convolution dense block for the decoding (up) part of a layer of the network.
  This will upsample data with an unpool block before the forward.
  It uses the indices from corresponding encoder on it's level.
  Its output is used as input to the next layer up.
  
  Args:
      in_channels: number of input channels.
      un_pool: predefined unpool block.
      se_layer: predefined SELayer. Defaults to None.
      dropout: predefined dropout block. Defaults to None.
      kernel_size: Kernel size of convolution layers. Defaults to 5*5.
      num_filters: number of input channels to each convolution layer. Defaults to 64.
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code: def forward(self, input, indices):
      out_block, _ = super().forward(input, None)
      out_block = self.un_pool(out_block, indices)
      return out_block, None
  
imports: from __future__ import annotations
  from typing import Optional, Sequence, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from monai.networks.blocks import ConvDenseBlock, Convolution
  from monai.networks.blocks import squeeze_and_excitation
  from monai.networks.layers.factories import Act, Norm
  from monai.networks.layers.simplelayers import SkipConnection
  from monai.networks.layers.utils import get_dropout_layer, get_pool_layer
  from monai.utils import optional_import
label_desc: Use image node_id 2 for calling the Decoder obj's underlying member method code with example usage: obj.forward(input, indices) and returns: out_block,  None