class_docstr: DiagGaussian distribution for continuous action spaces.
class_docstr_tok: ["DiagGaussian","distribution","for","continuous","action","spaces","."]
docstr: Returns:
      entropy: A float32 tensor with shape [BATCH_SIZE] of entropy of self policy distribution.
docstr_tok: ["Returns",":","entropy",":","A","float32","tensor","with","shape","[","BATCH_SIZE","]","of","entropy","of","self","policy","distribution","."]
code: def entropy(self):
      """
      Returns:
          entropy: A float32 tensor with shape [BATCH_SIZE] of entropy of self policy distribution.
      """
      entropy = paddle.sum(
          self.logstd + 0.5 * np.log(2.0 * np.pi * np.e), axis=1
      )
      return entropy
  
imports: import paddle
  import paddle.nn.functional
  import numpy
label_desc: Use image node_id 3 for calling the DiagGaussianDistribution obj's underlying member method code with example usage: obj.entropy() and returns: entropy