class_docstr: DiagGaussian distribution for continuous action spaces.
class_docstr_tok: ["DiagGaussian","distribution","for","continuous","action","spaces","."]
docstr: Returns:
      sample_action: An float32 tensor with shape [BATCH_SIZE, NUM_ACTIOINS] of sample action,
                     with noise to keep the target close to the original action.
docstr_tok: ["Returns",":","sample_action",":","An","float32","tensor","with","shape","[","BATCH_SIZE",",","NUM_ACTIOINS","]","of","sample","action",",","with","noise","to","keep","the","target","close","to","the","original","action","."]
code: def sample(self):
      """
      Returns:
          sample_action: An float32 tensor with shape [BATCH_SIZE, NUM_ACTIOINS] of sample action,
                         with noise to keep the target close to the original action.
      """
      mean_shape = paddle.to_tensor(self.mean.shape, dtype="int64")
      random_normal = paddle.normal(shape=mean_shape)
      return self.mean + self.std * random_normal
  
imports: import paddle
  import paddle.nn.functional
  import numpy
label_desc: Use image node_id 2 for calling the DiagGaussianDistribution obj's underlying member method code with example usage: obj.sample() and returns: unknown