class_docstr: Returns the bottom or bottleneck layer at the bottom of a network linking encoder to decoder halves.
  It consists of a 5 * 5 convolutional layer and a batch normalization layer to separate
  the encoder and decoder part of the network, restricting information flow between the encoder and decoder.
  
  Args:
      in_channels: number of input channels.
      se_layer: predefined SELayer. Defaults to None.
      dropout: predefined dropout block. Defaults to None.
      un_pool: predefined unpool block.
      max_pool: predefined maxpool block.
      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, indices = self.max_pool(input)
      out_block, _ = super().forward(out_block, 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 Bottleneck obj's underlying member method code with example usage: obj.forward(input, indices) and returns: out_block,  None