class_docstr: MobileNetV2 backbone.
  
  Args:
      widen_factor (float): Width multiplier, multiply number of
          channels in each layer by this amount. Default: 1.0.
      out_indices (Sequence[int], optional): Output from which stages.
          Default: (1, 2, 4, 7).
      frozen_stages (int): Stages to be frozen (all param fixed).
          Default: -1, which means not freezing any parameters.
      conv_cfg (dict, optional): Config dict for convolution layer.
          Default: None, which means using conv2d.
      norm_cfg (dict): Config dict for normalization layer.
          Default: dict(type='BN').
      act_cfg (dict): Config dict for activation layer.
          Default: dict(type='ReLU6').
      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. Default: False.
      with_cp (bool): Use checkpoint or not. Using checkpoint will save some
          memory while slowing down the training speed. Default: False.
      pretrained (str, optional): model pretrained path. Default: None
      init_cfg (dict or list[dict], optional): Initialization config dict.
          Default: None
class_docstr_tok: ["MobileNetV2","backbone",".","Args",":","widen_factor","(","float",")",":","Width","multiplier",",","multiply","number","of","channels","in","each","layer","by","this","amount",".","Default",":","1.0.","out_indices","(","Sequence","[","int","]",",","optional",")",":","Output","from","which","stages",".","Default",":","(","1",",","2",",","4",",","7",")",".","frozen_stages","(","int",")",":","Stages","to","be","frozen","(","all","param","fixed",")",".","Default",":","-1",",","which","means","not","freezing","any","parameters",".","conv_cfg","(","dict",",","optional",")",":","Config","dict","for","convolution","layer",".","Default",":","None",",","which","means","using","conv2d",".","norm_cfg","(","dict",")",":","Config","dict","for","normalization","layer",".","Default",":","dict","(","type='BN","'",")",".","act_cfg","(","dict",")",":","Config","dict","for","activation","layer",".","Default",":","dict","(","type='ReLU6","'",")",".","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",".","Default",":","False",".","with_cp","(","bool",")",":","Use","checkpoint","or","not",".","Using","checkpoint","will","save","some","memory","while","slowing","down","the","training","speed",".","Default",":","False",".","pretrained","(","str",",","optional",")",":","model","pretrained","path",".","Default",":","None","init_cfg","(","dict","or","list","[","dict","]",",","optional",")",":","Initialization","config","dict",".","Default",":","None"]
docstr: Convert the model into training mode while keep normalization layer
  frozen.
docstr_tok: ["Convert","the","model","into","training","mode","while","keep","normalization","layer","frozen","."]
code: def train(self, mode=True):
      """Convert the model into training mode while keep normalization layer
      frozen."""
      super(MobileNetV2, self).train(mode)
      self._freeze_stages()
      if mode and self.norm_eval:
          for m in self.modules():
              # trick: eval have effect on BatchNorm only
              if isinstance(m, _BatchNorm):
                  m.eval()
  
file_path: mmdetection/mmdet/models/backbones/mobilenet_v2.py
imports: import warnings
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmengine.model import BaseModule
  from torch.nn.modules.batchnorm import _BatchNorm
  from mmdet.registry import MODELS
  from ..layers import InvertedResidual
  from ..utils import make_divisible
label_desc: Use image node_id 5 for calling the MobileNetV2 obj's underlying member method code with example usage: obj.train(mode) without return types