class_docstr: ResNeXt backbone.
  
  Args:
      depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
      in_channels (int): Number of input image channels. Default: 3.
      num_stages (int): Resnet stages. Default: 4.
      groups (int): Group of resnext.
      base_width (int): Base width of resnext.
      strides (Sequence[int]): Strides of the first block of each stage.
      dilations (Sequence[int]): Dilation of each stage.
      out_indices (Sequence[int]): Output from which stages.
      style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
          layer is the 3x3 conv layer, otherwise the stride-two layer is
          the first 1x1 conv layer.
      frozen_stages (int): Stages to be frozen (all param fixed). -1 means
          not freezing any parameters.
      norm_cfg (dict): dictionary to construct and config norm layer.
      norm_eval (bool): Whether to set norm layers to eval mode, namely,
          freeze running stats (mean and var). Note: Effect on Batch Norm
          and its variants only.
      with_cp (bool): Use checkpoint or not. Using checkpoint will save some
          memory while slowing down the training speed.
      zero_init_residual (bool): whether to use zero init for last norm layer
          in resblocks to let them behave as identity.
code: def __init__(self, groups=1, base_width=4, **kwargs):
      self.groups = groups
      self.base_width = base_width
      super(ResNeXt, self).__init__(**kwargs)
  
file_path: mmdetection/mmdet/models/backbones/resnext.py
imports: import math
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from mmdet.registry import MODELS
  from ..layers import ResLayer
  from .resnet import Bottleneck
  from .resnet import ResNet
label_desc: Use image node_id 1 to create a new ResNeXt object from inherited base classes: ResNet with example: obj = ResNeXt(groups, base_width)