class_docstr: Inherits time series methods from TSM class.
  
  **** NON-GAUSSIAN DYNAMIC REGRESSION MODELS ****
  
  Parameters
  ----------
  
  formula : string
      patsy string describing the regression
  
  data : pd.DataFrame
      Field to specify the data that will be used
  
  family : 
      e.g. pf.Normal(0,1)
docstr: Fits the model
  
  Parameters
  ----------
  optimizer : string
      Stochastic optimizer: either RMSProp or ADAM.
  
  iterations: int
      How many iterations to run
  
  print_progress : bool
      Whether tp print the ELBO progress or not
  
  start_diffuse : bool
      Whether to start from diffuse values (if not: use approx Gaussian)
  
  Returns
  ----------
  BBVI fit object
docstr_tok: ["Fits","the","model","Parameters","--","--","--","--","--","optimizer",":","string","Stochastic","optimizer",":","either","RMSProp","or","ADAM",".","iterations",":","int","How","many","iterations","to","run","print_progress",":","bool","Whether","tp","print","the","ELBO","progress","or","not","start_diffuse",":","bool","Whether","to","start","from","diffuse","values","(","if","not",":","use","approx","Gaussian",")","Returns","--","--","--","--","--","BBVI","fit","object"]
code: def fit(
      self,
      optimizer="RMSProp",
      iterations=1000,
      print_progress=True,
      start_diffuse=False,
      **kwargs,
  ):
      """Fits the model
  
      Parameters
      ----------
      optimizer : string
          Stochastic optimizer: either RMSProp or ADAM.
  
      iterations: int
          How many iterations to run
  
      print_progress : bool
          Whether tp print the ELBO progress or not
  
      start_diffuse : bool
          Whether to start from diffuse values (if not: use approx Gaussian)
  
      Returns
      ----------
      BBVI fit object
      """
  
      return self._bbvi_fit(
          optimizer=optimizer,
          print_progress=print_progress,
          start_diffuse=start_diffuse,
          iterations=iterations,
          **kwargs,
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from scipy import optimize
  from ..None import inference
  from ..None import families
  from ..None import output
  from ..None import tsm
  from ..None import data_check
  from ..None import covariances
  from ..None import results
  from ..None import gas
  from .kalman import *
  from .dynlin import *
label_desc: Use image node_id 16 for calling the NDynReg obj's underlying member method code with example usage: obj.fit(optimizer, iterations, print_progress, start_diffuse) and returns: self