class_docstr: The baseModel class that provides the basic model structure for dlm. 
  
  Attributes:
      transition: the transition matrix G
      evaluation: the evaluation F
      noiseVar: the variance of the observation noise
      sysVar: the covariance of the underlying states
      innovation: the incremnent of the latent covariance W
      state: the latent states
      df: the degree of freedom (= number of data points)
      obs: the expectation of the observation
      obsVar: the variance of the observation
  
  Methods:
      initializeObservation: initialize the obs and obsVar
      validation: validate the matrix dimensions are consistent.
code: def __init__(
      self,
      transition=None,
      evaluation=None,
      noiseVar=None,
      sysVar=None,
      innovation=None,
      state=None,
      df=None,
  ):
      self.transition = transition
      self.evaluation = evaluation
      self.noiseVar = noiseVar
      self.sysVar = sysVar
      self.innovation = innovation
      self.state = state
      self.df = df
      self.obs = None
      self.obsVar = None
  
      # a hidden data field used only for model prediction
      self.prediction = __model__()
  
imports: import numpy
  import pydlm.base.tools
label_desc: Use image node_id 1 to create a new baseModel object  with example: obj = baseModel(transition, evaluation, noiseVar, sysVar, innovation, state, df)