class_docstr: Encapsulate emcee specific logic.
class_docstr_tok: ["Encapsulate","emcee","specific","logic","."]
code: def __init__(
      self,
      sampler,
      var_names=None,
      slices=None,
      arg_names=None,
      arg_groups=None,
      blob_names=None,
      blob_groups=None,
      index_origin=None,
      coords=None,
      dims=None,
  ):
      var_names, arg_names, slices = _verify_names(
          sampler, var_names, arg_names, slices
      )
      self.sampler = sampler
      self.var_names = var_names
      self.slices = slices
      self.arg_names = arg_names
      self.arg_groups = arg_groups
      self.blob_names = blob_names
      self.blob_groups = blob_groups
      self.index_origin = index_origin
      self.coords = coords
      self.dims = dims
      import emcee
  
      self.emcee = emcee
  
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 to create a new EmceeConverter object  with example: obj = EmceeConverter(sampler, var_names, slices, arg_names, arg_groups, blob_names, blob_groups, index_origin, coords, dims)