class_docstr: Tacotron 2 Model that is used to generate mel spectrograms from text
code: def generate_spectrogram(self, *, tokens):
      self.eval()
      self.calculate_loss = False
      token_len = torch.tensor([len(i) for i in tokens]).to(self.device)
      tensors = self(tokens=tokens, token_len=token_len)
      spectrogram_pred = tensors[1]
  
      if spectrogram_pred.shape[0] > 1:
          # Silence all frames past the predicted end
          mask = ~get_mask_from_lengths(tensors[-1])
          mask = mask.expand(
              spectrogram_pred.shape[1], mask.size(0), mask.size(1)
          )
          mask = mask.permute(1, 0, 2)
          spectrogram_pred.data.masked_fill_(mask, self.pad_value)
  
      return spectrogram_pred
  
imports: import contextlib
  from dataclasses import dataclass, field
  from typing import Any, Dict, List, Optional
  import torch
  from hydra.utils import instantiate
  from omegaconf import MISSING, DictConfig, OmegaConf, open_dict
  from omegaconf.errors import ConfigAttributeError
  from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger
  from torch import nn
  from nemo.collections.common.parts.preprocessing import parsers
  from nemo.collections.tts.losses.tacotron2loss import Tacotron2Loss
  from nemo.collections.tts.models.base import SpectrogramGenerator
  from nemo.collections.tts.parts.utils.helpers import g2p_backward_compatible_support, get_mask_from_lengths, tacotron2_log_to_tb_func, tacotron2_log_to_wandb_func
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types.elements import AudioSignal, EmbeddedTextType, LengthsType, LogitsType, MelSpectrogramType, SequenceToSequenceAlignmentType
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 7 for calling the Tacotron2Model obj's underlying member method code with example usage: obj.generate_spectrogram() and returns: spectrogram_pred