class_docstr: Inherits time series methods from TSM class.
  
  **** NON-GAUSSIAN LOCAL LEVEL MODEL ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  integ : int (default : 0)
      Specifies how many times to difference the time series.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
  
  family : 
      e.g. pf.Normal(0,1)
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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 .llm import *
label_desc: Use image node_id 17 for calling the NLLEV obj's underlying member method code with example usage: obj.fit(optimizer, iterations, print_progress, start_diffuse) and returns: self