code: def generate_spectrogram(
      self, tokens: "torch.tensor", speaker: int = 0, sigma: float = 1.0
  ) -> torch.tensor:
      self.eval()
      if self.training:
          logging.warning(
              "generate_spectrogram() is meant to be called in eval mode."
          )
      speaker = torch.tensor([speaker]).long().cuda().to(self.device)
      outputs = self.model.infer(speaker, tokens, sigma=sigma)
  
      spect = outputs["mel"]
      return spect
  
imports: import contextlib
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from pytorch_lightning.loggers import TensorBoardLogger
  from nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers import BaseTokenizer
  from nemo.collections.tts.losses.radttsloss import AttentionBinarizationLoss, RADTTSLoss
  from nemo.collections.tts.models.base import SpectrogramGenerator
  from nemo.collections.tts.parts.utils.helpers import batch_from_ragged, g2p_backward_compatible_support, plot_alignment_to_numpy, regulate_len, sample_tts_input
  from nemo.core.classes import Exportable
  from nemo.core.classes.common import typecheck
  from nemo.core.neural_types.elements import Index, MelSpectrogramType, RegressionValuesType, TokenDurationType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.core.optim.radam import RAdam
  from nemo.utils import logging
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 11 for calling the RadTTSModel obj's underlying member method code with example usage: obj.generate_spectrogram(tokens, speaker, sigma) and returns: spect