class_docstr: Base Estimator class for both Stan and Pyro Estimator
  
  Parameters
  ----------
  seed : int
      seed number for initial random values
  verbose : bool
      If True (default), output all diagnostics messages from estimators
class_docstr_tok: ["Base","Estimator","class","for","both","Stan","and","Pyro","Estimator","Parameters","--","--","--","--","--","seed",":","int","seed","number","for","initial","random","values","verbose",":","bool","If","True","(","default",")",",","output","all","diagnostics","messages","from","estimators"]
docstr: Parameters
  ----------
  model_name : str
      name of model - used in mapping the right sampling file (stan/pyro/...)
  model_param_names : list
      list of strings of model parameters names to extract
  data_input : dict
      key-value pairs of data input as required by definition in samplers (stan/pyro/...)
  fitter :
      model object used for fitting; this will be used instead of model_name if supplied to search for
      model object
  init_values : float or np.array
      initial sampler value. If None, 'random' is used
  
  Returns
  -------
  posteriors : dict
      key value pairs where key is the model parameter name and value is `num_sample` x posterior values
  training_metrics : dict
      metrics and meta data related to the training process
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code: def fit(
      self,
      model_name,
      model_param_names,
      data_input,
      fitter=None,
      init_values=None,
  ):
      """
  
      Parameters
      ----------
      model_name : str
          name of model - used in mapping the right sampling file (stan/pyro/...)
      model_param_names : list
          list of strings of model parameters names to extract
      data_input : dict
          key-value pairs of data input as required by definition in samplers (stan/pyro/...)
      fitter :
          model object used for fitting; this will be used instead of model_name if supplied to search for
          model object
      init_values : float or np.array
          initial sampler value. If None, 'random' is used
  
      Returns
      -------
      posteriors : dict
          key value pairs where key is the model parameter name and value is `num_sample` x posterior values
      training_metrics : dict
          metrics and meta data related to the training process
      """
      raise NotImplementedError(
          "Concrete fit() method must be implemented"
      )
  
imports: from abc import abstractmethod
  import numpy
label_desc: Use image node_id 2 for calling the BaseEstimator obj's underlying member method code with example usage: obj.fit(model_name, model_param_names, data_input, fitter, init_values) without return types