class_docstr: Builds a list of convolutional operators and performs summation on the outputs.
  
  ::
  
                          Conv3d, Conv3d, ...,  Conv3d
                                         ↓
                                        Sum
class_docstr_tok: ["Builds","a","list","of","convolutional","operators","and","performs","summation","on","the","outputs",".",":",":","Conv3d",",","Conv3d",",","...",",","Conv3d","\u2193","Sum"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      output = []
      for ind in range(len(self.convs)):
          output.append(self.convs[ind](x))
      if self.reduction_method == "sum":
          output = torch.stack(output, dim=0).sum(dim=0, keepdim=False)
      elif self.reduction_method == "cat":
          output = torch.cat(output, dim=1)
      return output
  
file_path: pytorchvideo/pytorchvideo/layers/convolutions.py
imports: from typing import Callable, Optional, Tuple
  import torch
  import torch.nn
  from pytorchvideo.layers.utils import set_attributes
  from torch.nn.common_types import _size_3_t
label_desc: Use image node_id 2 for calling the ConvReduce3D obj's underlying member method code with example usage: obj.forward(x) and returns: output