class_docstr: Base class for actions sampler.
  
  Given a batch of distribution params(including mean and var), sample, clip and
  return [N, B, A] actions, where 'N' means num_samples, 'B' means batch_size,
  'A' means action_size.
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docstr: Creates an ActionsSampler.
  
  Args:
    action_spec: A nest of BoundedTensorSpec representing the actions.
    sample_clippers: A list of callables that are applied to the generated
      samples. These callables take in a nested structure matching the
      action_spec and must return a matching structure.
    sample_rejecters: A list of callables that will reject samples and return
      a mask tensor.
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code: def __init__(
      self, action_spec, sample_clippers=None, sample_rejecters=None
  ):
      """Creates an ActionsSampler.
  
      Args:
        action_spec: A nest of BoundedTensorSpec representing the actions.
        sample_clippers: A list of callables that are applied to the generated
          samples. These callables take in a nested structure matching the
          action_spec and must return a matching structure.
        sample_rejecters: A list of callables that will reject samples and return
          a mask tensor.
      """
      self._action_spec = action_spec
      self._sample_clippers = sample_clippers
      self._sample_rejecters = sample_rejecters
  
file_path: agents/tf_agents/policies/samplers/qtopt_cem_actions_sampler.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
label_desc: Use image node_id 1 to create a new ActionsSampler object from inherited base classes: object with example: obj = ActionsSampler(action_spec, sample_clippers, sample_rejecters)