class_docstr: Dense layer with spectral normalization applied to weights, as in.
  
  [BJC19]_.
docstr: Parameters
  ----------
  x
      Input Tensor of SNDense layer
  
  Returns
  -------
  Tensor
      Output Tensor of SNDense layer
docstr_tok: ["Parameters","--","--","--","--","--","x","Input","Tensor","of","SNDense","layer","Returns","--","--","--","-","Tensor","Output","Tensor","of","SNDense","layer"]
code: def hybrid_forward(self, F, x, _weight, _u, _bias=None):
      """
  
      Parameters
      ----------
      x
          Input Tensor of SNDense layer
  
      Returns
      -------
      Tensor
          Output Tensor of SNDense layer
  
      """
      act = nd.FullyConnected(
          data=x,
          weight=self._spectral_norm(_weight, _u),
          bias=_bias,
          no_bias=_bias is None,
          num_hidden=self._units,
          flatten=self._flatten,
          name="fwd",
      )
  
      if self._act is not None:
          act = self._act(act)
  
      return act
  
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 3 for calling the SNDense obj's underlying member method code with example usage: obj.hybrid_forward(F, x, _weight, _u, _bias) and returns: act