code: def pad_and_concat(
      self, states: List[BottleneckState]
  ) -> BottleneckState:
      return BottleneckState(
          torch.cat([state.hidden_states for state in states], dim=0),
          torch.cat([state.input_tensor for state in states], dim=0),
          torch.cat(
              [state.adapter_residual for state in states], dim=0
          ),
          states[0].layer_norm,
          torch.cat([state.bottleneck_up for state in states], dim=0)
          if states[0].bottleneck_up is not None
          else None,
      )
  
imports: from typing import Dict, List, Mapping, NamedTuple, Optional, Union
  import torch
  from torch import nn
  from ..composition import AdapterCompositionBlock, Average, BatchSplit, Fuse, Parallel, Split, Stack, adjust_tensors_for_parallel
  from ..configuration import BnConfig
  from ..context import ForwardContext
  from .adapter_layer_base import ComposableAdapterLayerBase
  from .modeling import Adapter, BertFusion, ParallelAdapter
label_desc: Use image node_id 13 for calling the BottleneckLayer obj's underlying member method code with example usage: obj.pad_and_concat(states) and returns: BottleneckState