class_docstr: PPHGNetV2
  Args:
      stem_channels: list. Number of channels for the stem block.
      stage_type: str. The stage configuration of PPHGNet. such as the number of channels, stride, etc.
      use_lab: boolean. Whether to use LearnableAffineBlock in network.
      lr_mult_list: list. Control the learning rate of different stages.
  Returns:
      model: nn.Layer. Specific PPHGNetV2 model depends on args.
class_docstr_tok: ["PPHGNetV2","Args",":","stem_channels",":","list",".","Number","of","channels","for","the","stem","block",".","stage_type",":","str",".","The","stage","configuration","of","PPHGNet",".","such","as","the","number","of","channels",",","stride",",","etc",".","use_lab",":","boolean",".","Whether","to","use","LearnableAffineBlock","in","network",".","lr_mult_list",":","list",".","Control","the","learning","rate","of","different","stages",".","Returns",":","model",":","nn.Layer",".","Specific","PPHGNetV2","model","depends","on","args","."]
code: def _freeze_parameters(self, m):
      for p in m.parameters():
          p.stop_gradient = True
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import KaimingNormal, Constant
  from paddle.nn import Conv2D, BatchNorm2D, ReLU, AdaptiveAvgPool2D, MaxPool2D
  from paddle.regularizer import L2Decay
  from paddle import ParamAttr
  import copy
  from ppdet.core.workspace import register, serializable
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 2 for calling the PPHGNetV2 obj's underlying member method code with example usage: obj._freeze_parameters(m) without return types