old_contents: """
  RandomSampler
  -------------
  
  A random sampler that can be used for unit testing and debugging.
  """
  import numpy as np
  
  from dimod.core.sampler import Sampler
  from dimod.response import Response, SampleView
  
  __all__ = ['RandomSampler']
  
  
  class RandomSampler(Sampler):
      """Gives random samples.
  
      Note that this sampler is intended for testing.
  
      """
      def __init__(self):
          Sampler.__init__(self)
          self.sample_kwargs = {'num_reads': []}
  
      def sample(self, bqm, num_reads=10):
          """Gives random samples.
  
          Args:
              todo
  
          Returns:
              :obj:`.Response`: The vartype will match the given binary quadratic model.
  
          Notes:
              For each variable in each sample, the value is chosen by a coin flip.
  
          """
          values = np.asarray(list(bqm.vartype.value), dtype='int8')
          samples = np.random.choice(values, (num_reads, len(bqm)))
          variable_labels = list(bqm.linear)
          label_to_idx = {v: idx for idx, v in enumerate(variable_labels)}
  
          energies = [bqm.energy(SampleView(idx, samples, label_to_idx)) for idx in range(num_reads)]
  
          return Response.from_matrix(samples, {'energy': energies},
                                      vartype=bqm.vartype, variable_labels=variable_labels)
  
new_contents: """
  RandomSampler
  -------------
  
  A random sampler that can be used for unit testing and debugging.
  """
  import numpy as np
  
  from dimod.core.sampler import Sampler
  from dimod.response import Response, SampleView
  
  __all__ = ['RandomSampler']
  
  
  class RandomSampler(Sampler):
      """Gives random samples.
  
      Note that this sampler is intended for testing.
  
      """
      properties = None
      parameters = None
  
      def __init__(self):
          self.parameters = {'num_reads': []}
          self.properties = {}
  
      def sample(self, bqm, num_reads=10):
          """Gives random samples.
  
          Args:
              todo
  
          Returns:
              :obj:`.Response`: The vartype will match the given binary quadratic model.
  
          Notes:
              For each variable in each sample, the value is chosen by a coin flip.
  
          """
          values = np.asarray(list(bqm.vartype.value), dtype='int8')
          samples = np.random.choice(values, (num_reads, len(bqm)))
          variable_labels = list(bqm.linear)
          label_to_idx = {v: idx for idx, v in enumerate(variable_labels)}
  
          energies = [bqm.energy(SampleView(idx, samples, label_to_idx)) for idx in range(num_reads)]
  
          return Response.from_matrix(samples, {'energy': energies},
                                      vartype=bqm.vartype, variable_labels=variable_labels)
  
subject: Update RandomSampler to use the new Sampler abc