class_docstr: Basic interface for a generator model.
  
  Users can inherit from this class and implement their own generator model. The requirements are that the ``sample``
  method is implemented and that the ``num_classes`` attribute is present when ``conditional=True`` metric.
docstr: Return the number of classes for conditional generation.
docstr_tok: ["Return","the","number","of","classes","for","conditional","generation","."]
code: def num_classes(self) -> int:
      """Return the number of classes for conditional generation."""
      raise NotImplementedError
  
file_path: torchmetrics/src/torchmetrics/functional/image/perceptual_path_length.py
imports: import math
  from typing import Literal, Optional, Tuple, Union
  import torch
  from torch import Tensor, nn
  from torchmetrics.functional.image.lpips import _LPIPS
  from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE
label_desc: Use image node_id 1 for calling the GeneratorType obj's underlying member method code with example usage: obj.num_classes() without return types