code: def hybrid_forward(
      self, F, x: Tensor
  ) -> Tuple[Tensor, Tensor, Tensor]:
      noise_std = (
          self.dense_noise_std(x)
          * (self.noise_std_bounds.upper - self.noise_std_bounds.lower)
          + self.noise_std_bounds.lower
      )
  
      innovation = (
          self.dense_innovation(x)
          * (
              self.innovation_bounds.upper
              - self.innovation_bounds.lower
          )
          + self.innovation_bounds.lower
      )
  
      residual = self.dense_residual(x)
  
      return noise_std, innovation, residual
  
imports: from typing import Optional, Tuple
  import mxnet
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.linalg_util import jitter_cholesky
  from gluonts.mx.util import _broadcast_param, make_nd_diag
  from .None import Distribution, Gaussian, MultivariateGaussian
  from .distribution import getF
label_desc: Use image node_id 2 for calling the LDSArgsProj obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: noise_std,  innovation,  residual