class_docstr: Standardizes Root Sum of Squares along the input channel dimension.
  Used for Dense layer weight matrices only (ie. do not use on Convolution kernels).
  MXNet Dense layer weight matrix is of shape (out_ch, in_ch), so standardize across axis 1.
  Root Sum of Squares set to `rss`, which is 1.0 by default.
  Called `normalized_columns_initializer` in TensorFlow backend (but we work with rows instead of columns for MXNet).
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code: def __init__(self, rss=1.0):
      super(NormalizedRSSInitializer, self).__init__(rss=rss)
      self.rss = float(rss)
  
imports: from typing import Dict, List, Union, Tuple
  import mxnet
  from mxnet.initializer import Initializer, register
  from mxnet.gluon import nn, loss
  from mxnet.ndarray import NDArray
  from mxnet.symbol import Symbol
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.spaces import SpacesDefinition
label_desc: Use image node_id 1 to create a new NormalizedRSSInitializer object from inherited base classes: Initializer with example: obj = NormalizedRSSInitializer(rss)