code: def __init__(
      self,
      num_components,  # the number of switching states K
      hidden_dim,  # the dimension of the continuous latent space
      obs_dim,  # the dimension of the continuous outputs
      fine_transition_matrix=True,  # controls whether the transition matrix depends on s_t
      fine_transition_noise=False,  # controls whether the transition noise depends on s_t
      fine_observation_matrix=False,  # controls whether the observation matrix depends on s_t
      fine_observation_noise=False,  # controls whether the observation noise depends on s_t
      moment_matching_lag=1,
  ):  # controls the expense of the moment matching approximation
      self.num_components = num_components
      self.hidden_dim = hidden_dim
      self.obs_dim = obs_dim
      self.moment_matching_lag = moment_matching_lag
      self.fine_transition_noise = fine_transition_noise
      self.fine_observation_matrix = fine_observation_matrix
      self.fine_observation_noise = fine_observation_noise
      self.fine_transition_matrix = fine_transition_matrix
  
      assert moment_matching_lag > 0
      assert (
          fine_transition_noise
          or fine_observation_matrix
          or fine_observation_noise
          or fine_transition_matrix
      ), (
          "The continuous dynamics need to be coupled to the discrete dynamics in at least one way [use at "
          + "least one of the arguments --ftn --ftm --fon --fom]"
      )
  
      super(SLDS, self).__init__()
  
      # initialize the various parameters of the model
      self.transition_logits = nn.Parameter(
          0.1 * torch.randn(num_components, num_components)
      )
      if fine_transition_matrix:
          transition_matrix = torch.eye(
              hidden_dim
          ) + 0.05 * torch.randn(num_components, hidden_dim, hidden_dim)
      else:
          transition_matrix = torch.eye(
              hidden_dim
          ) + 0.05 * torch.randn(hidden_dim, hidden_dim)
      self.transition_matrix = nn.Parameter(transition_matrix)
      if fine_transition_noise:
          self.log_transition_noise = nn.Parameter(
              0.1 * torch.randn(num_components, hidden_dim)
          )
      else:
          self.log_transition_noise = nn.Parameter(
              0.1 * torch.randn(hidden_dim)
          )
      if fine_observation_matrix:
          self.observation_matrix = nn.Parameter(
              0.3 * torch.randn(num_components, hidden_dim, obs_dim)
          )
      else:
          self.observation_matrix = nn.Parameter(
              0.3 * torch.randn(hidden_dim, obs_dim)
          )
      if fine_observation_noise:
          self.log_obs_noise = nn.Parameter(
              0.1 * torch.randn(num_components, obs_dim)
          )
      else:
          self.log_obs_noise = nn.Parameter(0.1 * torch.randn(obs_dim))
  
      # define the prior distribution p(x_0) over the continuous latent at the initial time step t=0
      x_init_mvn = pyro.distributions.MultivariateNormal(
          torch.zeros(self.hidden_dim), torch.eye(self.hidden_dim)
      )
      self.x_init_mvn = mvn_to_funsor(
          x_init_mvn,
          real_inputs=OrderedDict(
              [("x_0", funsor.Reals[self.hidden_dim])]
          ),
      )
  
imports: import argparse
  import time
  from collections import OrderedDict
  from os.path import exists
  from urllib.request import urlopen
  import numpy
  import pyro
  import torch
  import torch.nn
  import funsor
  import funsor.ops
  import funsor.torch.distributions
  from funsor.pyro.convert import funsor_to_cat_and_mvn, funsor_to_mvn, matrix_and_mvn_to_funsor, mvn_to_funsor
label_desc: Use image node_id 1 to create a new SLDS object from inherited base classes: nn with example: obj = SLDS(num_components, hidden_dim, obs_dim, fine_transition_matrix, fine_transition_noise, fine_observation_matrix, fine_observation_noise, moment_matching_lag)