class_docstr: DiagGaussian distribution for continuous action spaces.
class_docstr_tok: ["DiagGaussian","distribution","for","continuous","action","spaces","."]
docstr: Args:
      other: object of DiagGaussianDistribution
  Returns:
      kl: A float32 tensor with shape [BATCH_SIZE]
docstr_tok: ["Args",":","other",":","object","of","DiagGaussianDistribution","Returns",":","kl",":","A","float32","tensor","with","shape","[","BATCH_SIZE","]"]
code: def kl(self, other):
      """
      Args:
          other: object of DiagGaussianDistribution
      Returns:
          kl: A float32 tensor with shape [BATCH_SIZE]
      """
      assert isinstance(other, DiagGaussianDistribution)
  
      temp = (
          torch.square(self.std) + torch.square(self.mean - other.mean)
      ) / (2.0 * torch.square(other.std))
      kl = torch.sum(other.logstd - self.logstd + temp - 0.5, axis=1)
      return kl
  
imports: import torch
  import torch.nn.functional
  import numpy
label_desc: Use image node_id 5 for calling the DiagGaussianDistribution obj's underlying member method code with example usage: obj.kl(other) and returns: kl