class_docstr: Base class for combinations.
docstr: Combine a sequence of individual representations.
  
  :param xs: shape: `(*batch_dims, *input_dims_i)`
      the individual representations
  
  :return: shape: `(*batch_dims, *output_dims)`
      a combined representation
docstr_tok: ["Combine","a","sequence","of","individual","representations",".",":","param","xs",":","shape",":","`","(","*","batch_dims",",","*","input_dims_i",")","`","the","individual","representations",":","return",":","shape",":","`","(","*","batch_dims",",","*","output_dims",")","`","a","combined","representation"]
code: def forward(
      self, xs: Sequence[torch.FloatTensor]
  ) -> torch.FloatTensor:
      """
      Combine a sequence of individual representations.
  
      :param xs: shape: `(*batch_dims, *input_dims_i)`
          the individual representations
  
      :return: shape: `(*batch_dims, *output_dims)`
          a combined representation
      """
      raise NotImplementedError
  
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 1 for calling the Combination obj's underlying member method code with example usage: obj.forward(xs) without return types