code: def _verify_names(sampler, var_names, arg_names, slices):
      """Make sure var_names and arg_names are assigned reasonably.
  
      This is meant to run before loading emcee objects into InferenceData.
      In case var_names or arg_names is None, will provide defaults. If they are
      not None, it verifies there are the right number of them.
  
      Throws a ValueError in case validation fails.
  
      Parameters
      ----------
      sampler : emcee.EnsembleSampler
          Fitted emcee sampler
      var_names : list[str] or None
          Names for the emcee parameters
      arg_names : list[str] or None
          Names for the args/observations provided to emcee
      slices : list[seq] or None
          slices to select the variables (used for multidimensional variables)
  
      Returns
      -------
      list[str], list[str], list[seq]
          Defaults for var_names, arg_names and slices
      """
      # There are 3 possible cases: emcee2, emcee3 and sampler read from h5 file (emcee3 only)
      if hasattr(sampler, "args"):
          ndim = sampler.chain.shape[-1]
          num_args = len(sampler.args)
      elif hasattr(sampler, "log_prob_fn"):
          ndim = sampler.get_chain().shape[-1]
          num_args = len(sampler.log_prob_fn.args)
      else:
          ndim = sampler.get_chain().shape[-1]
          num_args = 0  # emcee only stores the posterior samples
  
      if slices is None:
          slices = utils.arange(ndim)
          num_vars = ndim
      else:
          num_vars = len(slices)
      indices = utils.arange(ndim)
      slicing_try = np.concatenate(
          [utils.one_de(indices[idx]) for idx in slices]
      )
      if len(set(slicing_try)) != ndim:
          warnings.warn(
              "Check slices: Not all parameters in chain captured. "
              f"{ndim} are present, and {len(slicing_try)} have been captured.",
              UserWarning,
          )
      if len(slicing_try) != len(set(slicing_try)):
          warnings.warn(
              f"Overlapping slices. Check the index present: {slicing_try}",
              UserWarning,
          )
  
      if var_names is None:
          var_names = [f"var_{idx}" for idx in range(num_vars)]
      if arg_names is None:
          arg_names = [f"arg_{idx}" for idx in range(num_args)]
  
      if len(var_names) != num_vars:
          raise ValueError(
              f"The sampler has {num_vars} variables, "
              f"but only {len(var_names)} var_names were provided!"
          )
  
      if len(arg_names) != num_args:
          raise ValueError(
              f"The sampler has {num_args} args, "
              f"but only {len(arg_names)} arg_names were provided!"
          )
      return var_names, arg_names, slices
  
file_path: arviz/arviz/data/io_emcee.py
imports: import warnings
  from collections import OrderedDict
  import numpy
  import xarray
  from ..None import utils
  from .base import dict_to_dataset, generate_dims_coords, make_attrs
  from .inference_data import InferenceData
label_desc: Use image node_id 1 for calling a global function with example usage: _verify_names(sampler, var_names, arg_names, slices) and returns: var_names,  arg_names,  slices