class_docstr: Class for Slice sampler.
  
  Parameters
  ----------
  model : Distribution from which sampling has done
  w     : Initial width for slice
  tune  : Flag for tuning
docstr: sample -- function which populates samples
  by calling function _sample iteratively
docstr_tok: ["sample","--","function","which","populates","samples","by","calling","function","_sample","iteratively"]
code: def sample(self, q0, num_samples=1, burn=100):
      """
      sample -- function which populates samples
      by calling function _sample iteratively
      """
      if num_samples is None:
          num_samples = 1
      if not hasattr(q0, "__len__"):
          q0 = np.array([q0])
  
      # Burn in samples
      for i in range(burn):
          q0 = self._sample(q0)
  
      samples = np.zeros([num_samples, len(q0)])
      for i in range(num_samples):
          samples[i] = self._sample(q0)
          q0 = samples[i]
  
      return samples
  
file_path: dragonfly/dragonfly/sampling/slice.py
imports: from __future__ import division
  import numpy
  import numpy.random
label_desc: Use image node_id 3 for calling the Slice obj's underlying member method code with example usage: obj.sample(q0, num_samples, burn) and returns: samples