class_docstr: Class for abstract distributions 
class_docstr_tok: ["Class","for","abstract","distributions"]
docstr: Returns samples drawn from distribution. 
docstr_tok: ["Returns","samples","drawn","from","distribution","."]
code: def draw_samples(self, method, size=None, init_sample=None, *args):
      """Returns samples drawn from distribution."""
      if method == "nuts":
          return self.draw_samples_nuts(size, init_sample, *args)
      elif method == "slice":
          return self.draw_samples_slice(size, init_sample, *args)
      elif method == "metropolis":
          return self.draw_samples_metropolis(size, init_sample, *args)
      elif method == "binarymetropolis":
          return self.draw_samples_binary_metropolis(
              size, init_sample, *args
          )
  
file_path: dragonfly/dragonfly/distributions/model.py
imports: from __future__ import absolute_import
  import numpy
  from .distribution import Distribution
  from ..sampling.slice import Slice
  from ..sampling.nuts import NoUTurnSamplerDA, LeapFrog
  from ..sampling.metropolis import Metropolis, BinaryMetropolis
label_desc: Use image node_id 5 for calling the Model obj's underlying member method code with example usage: obj.draw_samples(method, size, init_sample) and returns: self, self, self, self