class_docstr: Dense layer with spectral normalization applied to weights, as in.
  
  [BJC19]_.
code: def __repr__(self):
      s = "{name}({layout}, {act})"
      shape = self._weight.shape
      return s.format(
          name=self.__class__.__name__,
          act=self._act if self._act else "linear",
          layout="{} -> {}".format(
              shape[1] if shape[1] else None, shape[0]
          ),
      )
  
file_path: gluonts/src/gluonts/mx/block/sndense.py
imports: from typing import Optional
  import mxnet
  from mxnet import nd
  from mxnet import autograd
  from mxnet import init
  from gluonts.core.component import validated
  from gluonts.mx.context import get_mxnet_context
  from gluonts.mx import Tensor
  from gluonts.mx.activation import get_activation
label_desc: Use image node_id 4 for calling the SNDense obj's underlying member method code with example usage: obj.__repr__() and returns: s