code: def generate_z(self, num_samples: int) -> Tensor:
      return torch.randn(
          num_samples, self.latent_dim, device=get_device(self)
      )
  
imports: import torch
  import random
  from abc import abstractmethod
  from abc import ABCMeta
  from torch import Tensor
  from typing import Any
  from typing import Optional
  from cftool.misc import safe_execute
  from cftool.misc import shallow_copy_dict
  from cftool.types import tensor_dict_type
  from ...schema import _forward
  from ...constants import INPUT_KEY
  from ...constants import LABEL_KEY
  from ...constants import PREDICTIONS_KEY
  from ...misc.toolkit import slerp
  from ...misc.toolkit import get_device
label_desc: Use image node_id 2 for calling the GaussianGeneratorMixin obj's underlying member method code with example usage: obj.generate_z(num_samples) and returns: torch