class_docstr: Res2Net, see https://arxiv.org/abs/1904.01169
  Args:
      depth (int): Res2Net depth, should be 50, 101, 152, 200.
      width (int): Res2Net width
      scales (int): Res2Net scale
      variant (str): Res2Net variant, supports 'a', 'b', 'c', 'd' currently
      lr_mult_list (list): learning rate ratio of different resnet stages(2,3,4,5),
                           lower learning rate ratio is need for pretrained model
                           got using distillation(default as [1.0, 1.0, 1.0, 1.0]).
      groups (int): The groups number of the Conv Layer.
      norm_type (str): normalization type, 'bn' or 'sync_bn'
      norm_decay (float): weight decay for normalization layer weights
      freeze_norm (bool): freeze normalization layers
      freeze_at (int): freeze the backbone at which stage
      return_idx (list): index of stages whose feature maps are returned,
                         index 0 stands for res2
      dcn_v2_stages (list): index of stages who select deformable conv v2
      num_stages (int): number of stages created
code: def forward(self, inputs):
      x = inputs["image"]
      res1 = self.res1(x)
      x = F.max_pool2d(res1, kernel_size=3, stride=2, padding=1)
      outs = []
      for idx, stage in enumerate(self.res_layers):
          x = stage(x)
          if idx == self.freeze_at:
              x.stop_gradient = True
          if idx in self.return_idx:
              outs.append(x)
      return outs
  
imports: from numbers import Integral
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ..shape_spec import ShapeSpec
  from .resnet import ConvNormLayer
label_desc: Use image node_id 3 for calling the Res2Net obj's underlying member method code with example usage: obj.forward(inputs) and returns: outs