code: def __init__(self, parameters, deterministic=False):
      self.parameters = parameters
      self.mean, self.logvar = paddle.chunk(parameters, 2, axis=1)
      self.logvar = paddle.clip(self.logvar, -30.0, 20.0)
      self.deterministic = deterministic
      self.std = paddle.exp(0.5 * self.logvar)
      self.var = paddle.exp(self.logvar)
      if self.deterministic:
          self.var = self.std = paddle.zeros_like(self.mean)
  
imports: import numpy
  import paddle
  import paddle.nn
  from ..configuration_utils import ConfigMixin
  from ..configuration_utils import register_to_config
  from .unet_blocks import get_down_block
  from .unet_blocks import get_up_block
  from .unet_blocks import UNetMidBlock2D
label_desc: Use image node_id 1 to create a new DiagonalGaussianDistribution object from inherited base classes: object with example: obj = DiagonalGaussianDistribution(parameters, deterministic)