code: def interpolate(
      self,
      num_samples: int,
      *,
      class_idx: Optional[int] = None,
      use_slerp: bool = False,
      **kwargs: Any,
  ) -> Tensor:
      z1 = self.generate_z(1)
      z2 = self.generate_z(1)
      shape = z1.shape
      z1 = z1.view(1, -1)
      z2 = z2.view(1, -1)
      ratio = torch.linspace(
          0.0, 1.0, num_samples, device=get_device(self)
      )[:, None]
      z = (
          slerp(z1, z2, ratio)
          if use_slerp
          else ratio * z1 + (1.0 - ratio) * z2
      )
      z = z.view(num_samples, *shape[1:])
      if class_idx is None and self.num_classes is not None:
          class_idx = random.randint(0, self.num_classes - 1)
      kw = shallow_copy_dict(kwargs)
      kw["z"] = z
      kw["labels"] = self.get_sample_labels(num_samples, class_idx)
      return safe_execute(self.decode, kw)
  
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 4 for calling the GaussianGeneratorMixin obj's underlying member method code with example usage: obj.interpolate(num_samples) and returns: safe_execute