code: def sample(
      self, generator: Optional[torch.Generator] = None
  ) -> torch.FloatTensor:
      # make sure sample is on the same device as the parameters and has same dtype
      sample = randn_tensor(
          self.mean.shape,
          generator=generator,
          device=self.parameters.device,
          dtype=self.parameters.dtype,
      )
      x = self.mean + self.std * sample
      return x
  
imports: from dataclasses import dataclass
  from typing import Optional
  import numpy
  import torch
  import torch.nn
  from ..utils import BaseOutput, is_torch_version
  from ..utils.torch_utils import randn_tensor
  from .activations import get_activation
  from .attention_processor import SpatialNorm
  from .unet_2d_blocks import AutoencoderTinyBlock, UNetMidBlock2D, get_down_block, get_up_block
label_desc: Use image node_id 2 for calling the DiagonalGaussianDistribution obj's underlying member method code with example usage: obj.sample(generator) and returns: x