class_docstr: .. math::
  
      L = \alpha \|h_t\|_2^2,
  
  where :math:`h_t` is the output of the RNN at timestep t.
  :math:`\alpha` is scaling coefficient.
  The implementation follows [MMS17]_.
  Computes Activation Regularization Loss. (alias: AR)
  
  Parameters
  ----------
  alpha
      The scaling coefficient of the regularization.
  weight
      Global scalar weight for loss.
  batch_axis
      The axis that represents mini-batch.
  time_axis
      The axis that represents time-step.
class_docstr_tok: ["..","math",":",":","L","=","\\alpha","\\|h_t\\|_2^2",",","where",":","math",":","`","h_t","`","is","the","output","of","the","RNN","at","timestep","t.",":","math",":","`","\\alpha","`","is","scaling","coefficient",".","The","implementation","follows","[","MMS17","]","_.","Computes","Activation","Regularization","Loss",".","(","alias",":","AR",")","Parameters","--","--","--","--","--","alpha","The","scaling","coefficient","of","the","regularization",".","weight","Global","scalar","weight","for","loss",".","batch_axis","The","axis","that","represents","mini-batch",".","time_axis","The","axis","that","represents","time-step","."]
code: def __repr__(self):
      s = "ActivationRegularizationLoss (alpha={alpha})"
      return s.format(alpha=self._alpha)
  
file_path: gluonts/src/gluonts/mx/block/regularization.py
imports: from typing import List, Optional
  from mxnet.gluon.loss import Loss
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the ActivationRegularizationLoss obj's underlying member method code with example usage: obj.__repr__() and returns: s