class_docstr: Implements Linear Dynamical System (LDS) as a distribution.
  
  The LDS is given by
  
  .. math::
      z_t = A_t l_{t-1} + b_t + \epsilon_t \\
      l_t = C_t l_{t-1} + g_t \nu
  
  where
  
  .. math::
      \epsilon_t = N(0, S_v) \\
      \nu = N(0, 1)
  
  :math:`A_t`, :math:`C_t` and :math:`g_t` are the emission, transition and
  innovation coefficients respectively. The residual terms are denoted
  by :math:`b_t`.
  
  The target :math:`z_t` can be :math:`d`-dimensional in which case
  
  .. math::
      A_t \in R^{d \times h}, b_t \in R^{d}, C_t \in R^{h \times h}, g_t \in R^{h}
  
  where :math:`h` is dimension of the latent state.
  
  Parameters
  ----------
  emission_coeff
      Tensor of shape (batch_size, seq_length, obs_dim, latent_dim)
  transition_coeff
      Tensor of shape (batch_size, seq_length, latent_dim, latent_dim)
  innovation_coeff
      Tensor of shape (batch_size, seq_length, latent_dim)
  noise_std
      Tensor of shape (batch_size, seq_length, obs_dim)
  residuals
      Tensor of shape (batch_size, seq_length, obs_dim)
  prior_mean
      Tensor of shape (batch_size, latent_dim)
  prior_cov
      Tensor of shape (batch_size, latent_dim, latent_dim)
  latent_dim
      Dimension of the latent state
  output_dim
      Dimension of the output
  seq_length
      Sequence length
  F
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code: def event_shape(self) -> Tuple:
      return (self.output_dim,)
  
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 4 for calling the LDS obj's underlying member method code with example usage: obj.event_shape() and returns: self