class_docstr: This class uses Expectation-Maximization to learn parameters of a single latent variable in a bayesian network.
  We do so by also allowing the user to CONSTRAINT the optimisation
  These are elements that help the algorithm find a local optimal point closer to the point we think
  
  The setting is:
  Input:
      - a StructureModel representing the whole network or any sub-graph containing the Markov Blanket of the LV
      - data as a dataframe. The LV must be in the dataframe, with missing values represented by `np.nan`s
      - constraints:
          - Box - A hard constraint; forbids the solution to be outside of certain boundaries
          - Priors - establishes Dirichlet priors to every parameter
  run:
      - using the method `run` or manually alternating over E and M steps)
  Result:
      - CPTs involving the latent variable, learnt by EM, found in the attribute `cpds`
      - CPTs not involving the LV not learned (They must be learned separately by MLE.
      This is faster and the result is the same)
  
  Example:
  >>> em = EMSingleLatentVariable(sm=sm, data=data, lv_name=lv_name, node_states=node_states)
  >>> em.run() # run EM until convergence
  >>> # or run E and M steps separately
  >>> for i in range(10): # Run EM 10 times
  >>>     em.e_step()
  >>>     em.m_step()
docstr: Checks if the priors are passed in the right format and if they are valid
  
  Args:
      priors: Prior distribution to check
  
  Returns:
      Verified priors
  
  Raises:
      ValueError: when prior distributions are invalid
docstr_tok: ["Checks","if","the","priors","are","passed","in","the","right","format","and","if","they","are","valid","Args",":","priors",":","Prior","distribution","to","check","Returns",":","Verified","priors","Raises",":","ValueError",":","when","prior","distributions","are","invalid"]
code: def _check_priors(
      priors: Dict[str, pd.DataFrame]
  ) -> Dict[str, pd.DataFrame]:
      """
      Checks if the priors are passed in the right format and if they are valid
  
      Args:
          priors: Prior distribution to check
  
      Returns:
          Verified priors
  
      Raises:
          ValueError: when prior distributions are invalid
      """
      if priors is None or isinstance(priors, dict):
          return priors
  
      raise ValueError(f"Invalid priors {priors}")
  
imports: import logging
  import os
  from time import time
  from typing import Dict, List, Tuple, Union
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from causalnex.structure import StructureModel
  from causalnex.utils.data_utils import chunk_data, count_unique_rows
  from causalnex.utils.pgmpy_utils import cpd_multiplication
label_desc: Use image node_id 19 for calling the EMSingleLatentVariable obj's underlying member method code with example usage: obj._check_priors(priors) and returns: priors