class_docstr: .. math::
  
      L = \beta \| h_t-h_{t+1} \|_2^2,
  
  where :math:`h_t` is the output of the RNN at timestep t,
  :math:`h_{t+1}` is the output of the RNN at timestep t+1, :math:`\beta`
  is scaling coefficient.
  
  The implementation follows [MMS17]_.
  Computes Temporal Activation Regularization Loss. (alias: TAR)
  
  Parameters
  ----------
  beta
      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.
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code: def __repr__(self):
      s = "TemporalActivationRegularizationLoss (beta={beta})"
      return s.format(beta=self._beta)
  
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 TemporalActivationRegularizationLoss obj's underlying member method code with example usage: obj.__repr__() and returns: s