class_docstr: Continuous Gaussian actions sampler.
  
  Supports in nested action_spec with 1d continuous actions or 1d
  discrete actions.
  
  Given a batch of distribution params(including mean and var), sample and
  return [B, N, A] actions, where 'B' means batch_size, 'N' means num_samples,
  'A' means action_size.
class_docstr_tok: ["Continuous","Gaussian","actions","sampler",".","Supports","in","nested","action_spec","with","1d","continuous","actions","or","1d","discrete","actions",".","Given","a","batch","of","distribution","params","(","including","mean","and","var",")",",","sample","and","return","[","B",",","N",",","A","]","actions",",","where","'","B","'","means","batch_size",",","'N","'","means","num_samples",",","'","A","'","means","action_size","."]
code: def __init__(self, action_spec, sample_clippers=None):
      super(GaussianActionsSampler, self).__init__(
          action_spec, sample_clippers
      )
  
      for flat_action_spec in tf.nest.flatten(action_spec):
          if flat_action_spec.shape.rank > 1:
              raise ValueError(
                  "Only 1d action is supported by this sampler. "
                  "The action_spec: {} contains action whose rank > 1. "
                  "Consider coverting it into multiple 1d "
                  "actions.".format(action_spec)
              )
  
file_path: agents/tf_agents/policies/samplers/qtopt_cem_actions_sampler_hybrid.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gin
  import tensorflow
  import tensorflow_probability
  from tf_agents.policies.samplers import qtopt_cem_actions_sampler
  from tf_agents.utils import common
label_desc: Use image node_id 1 to create a new GaussianActionsSampler object from inherited base classes: qtopt_cem_actions_sampler with example: obj = GaussianActionsSampler(action_spec, sample_clippers)