class_docstr: Trident Convolution Module.
  
  Args:
      in_channels (int): Number of channels in input.
      out_channels (int): Number of channels in output.
      kernel_size (int): Size of convolution kernel.
      stride (int, optional): Convolution stride. Default: 1.
      trident_dilations (tuple[int, int, int], optional): Dilations of
          different trident branch. Default: (1, 2, 3).
      test_branch_idx (int, optional): In inference, all 3 branches will
          be used if `test_branch_idx==-1`, otherwise only branch with
          index `test_branch_idx` will be used. Default: 1.
      bias (bool, optional): Whether to use bias in convolution or not.
          Default: False.
      init_cfg (dict or list[dict], optional): Initialization config dict.
          Default: None
code: def forward(self, inputs):
      if self.training or self.test_branch_idx == -1:
          outputs = [
              F.conv2d(
                  input,
                  self.weight,
                  self.bias,
                  self.stride,
                  padding,
                  dilation,
              )
              for input, dilation, padding in zip(
                  inputs, self.dilations, self.paddings
              )
          ]
      else:
          assert len(inputs) == 1
          outputs = [
              F.conv2d(
                  inputs[0],
                  self.weight,
                  self.bias,
                  self.stride,
                  self.paddings[self.test_branch_idx],
                  self.dilations[self.test_branch_idx],
              )
          ]
  
      return outputs
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from mmengine.model import BaseModule
  from torch.nn.modules.utils import _pair
  from mmdet.models.backbones.resnet import Bottleneck, ResNet
  from mmdet.registry import MODELS
label_desc: Use image node_id 3 for calling the TridentConv obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs