class_docstr: ResNeSt backbone.
  
  Args:
      groups (int): Number of groups of Bottleneck. Default: 1
      base_width (int): Base width of Bottleneck. Default: 4
      radix (int): Radix of SplitAttentionConv2d. Default: 2
      reduction_factor (int): Reduction factor of inter_channels in
          SplitAttentionConv2d. Default: 4.
      avg_down_stride (bool): Whether to use average pool for stride in
          Bottleneck. Default: True.
      kwargs (dict): Keyword arguments for ResNet.
class_docstr_tok: ["ResNeSt","backbone",".","Args",":","groups","(","int",")",":","Number","of","groups","of","Bottleneck",".","Default",":","1","base_width","(","int",")",":","Base","width","of","Bottleneck",".","Default",":","4","radix","(","int",")",":","Radix","of","SplitAttentionConv2d",".","Default",":","2","reduction_factor","(","int",")",":","Reduction","factor","of","inter_channels","in","SplitAttentionConv2d",".","Default",":","4.","avg_down_stride","(","bool",")",":","Whether","to","use","average","pool","for","stride","in","Bottleneck",".","Default",":","True",".","kwargs","(","dict",")",":","Keyword","arguments","for","ResNet","."]
code: def __init__(
      self,
      groups=1,
      base_width=4,
      radix=2,
      reduction_factor=4,
      avg_down_stride=True,
      **kwargs,
  ):
      self.groups = groups
      self.base_width = base_width
      self.radix = radix
      self.reduction_factor = reduction_factor
      self.avg_down_stride = avg_down_stride
      super(ResNeSt, self).__init__(**kwargs)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from ..builder import BACKBONES
  from ..utils import ResLayer
  from .resnet import Bottleneck
  from .resnet import ResNetV1d
label_desc: Use image node_id 1 to create a new ResNeSt object from inherited base classes: ResNetV1d with example: obj = ResNeSt(groups, base_width, radix, reduction_factor, avg_down_stride)