class_docstr: MaxNorm weight constraint.
  
  Constrains the weights incident to each hidden unit
  to have a norm less than or equal to a desired value.
  
  Any hidden unit vector with a norm less than the max norm
  constaint will not be altered.
class_docstr_tok: ["MaxNorm","weight","constraint",".","Constrains","the","weights","incident","to","each","hidden","unit","to","have","a","norm","less","than","or","equal","to","a","desired","value",".","Any","hidden","unit","vector","with","a","norm","less","than","the","max","norm","constaint","will","not","be","altered","."]
code: def __call__(self, module):
      w = module.weight.data
      module.weight.data = th.renorm(w, 2, self.axis, self.value)
  
imports: from fnmatch import fnmatch
  import torch
  from .callbacks import Callback
label_desc: Use image node_id 2 for calling the MaxNorm obj's underlying member method code with example usage: obj.__call__(module) without return types