class_docstr: Combine representation by concatenation followed by an aggregation along the same axis.
code: def forward(
      self, xs: Sequence[torch.FloatTensor]
  ) -> torch.FloatTensor:  # noqa: D102
      return self.aggregation(super().forward(xs=xs), dim=self.dim)
  
imports: import logging
  from abc import ABC, abstractmethod
  from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Tuple
  import torch
  from class_resolver import ClassResolver, Hint, HintOrType, OptionalKwargs
  from class_resolver.contrib.torch import activation_resolver, aggregation_resolver
  from torch import nn
  from ..utils import ExtraReprMixin, combine_complex, split_complex
label_desc: Use image node_id 2 for calling the ConcatAggregationCombination obj's underlying member method code with example usage: obj.forward(xs) and returns: self