class_docstr: Implementation of a Network In Network model
  
  :param num_classes (int): Number of classes for classification
  :param in_channels (int): Number of channels in input specimens
class_docstr_tok: ["Implementation","of","a","Network","In","Network","model",":","param","num_classes","(","int",")",":","Number","of","classes","for","classification",":","param","in_channels","(","int",")",":","Number","of","channels","in","input","specimens"]
code: def _initialize_weights(self):
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              m.weight.data.normal_(0, 0.05)
              if m.bias is not None:
                  m.bias.data.zero_()
  
imports: from torch import nn
label_desc: Use image node_id 3 for calling the NetworkInNetwork obj's underlying member method code with example usage: obj._initialize_weights() without return types