class_docstr: GroupLasso Regularizer:
  The first dimension represents the input layer and the second dimension represents the output layer.
  The groups are defined by the column in the matrix W
class_docstr_tok: ["GroupLasso","Regularizer",":","The","first","dimension","represents","the","input","layer","and","the","second","dimension","represents","the","output","layer",".","The","groups","are","defined","by","the","column","in","the","matrix","W"]
code: def regularized_all_param(self, reg_loss_function):
      for (
          model_param_name,
          model_param_value,
      ) in self.model.named_parameters():
          if model_param_name.endswith("weight"):
              reg_loss_function += (
                  self.lambda_reg
                  * GroupLassoRegularizer.__inputs_groups_reg(
                      layer_weights=model_param_value
                  )
              )
          if model_param_name.endswith("bias"):
              reg_loss_function += (
                  self.lambda_reg
                  * GroupLassoRegularizer.__bias_groups_reg(
                      bias_weights=model_param_value
                  )
              )
      return reg_loss_function
  
imports: from enum import Enum
label_desc: Use image node_id 3 for calling the GroupLassoRegularizer obj's underlying member method code with example usage: obj.regularized_all_param(reg_loss_function) and returns: reg_loss_function