class_docstr: Negative log likelihood loss.
class_docstr_tok: ["Negative","log","likelihood","loss","."]
docstr: Compute the loss.
  
  Args:
      logits (Tensor): [B, T, D]
  
  Returns:
      Tensor: [1]
docstr_tok: ["Compute","the","loss",".","Args",":","logits","(","Tensor",")",":","[","B",",","T",",","D","]","Returns",":","Tensor",":","[","1","]"]
code: def forward(
      self, log_prob: torch.Tensor
  ) -> dict:  # pylint: disable=no-self-use
      """Compute the loss.
  
      Args:
          logits (Tensor): [B, T, D]
  
      Returns:
          Tensor: [1]
  
      """
      return_dict = {}
      return_dict["loss"] = -log_prob.mean()
      return return_dict
  
imports: import os
  from typing import Dict, List, Union
  import torch
  from coqpit import Coqpit
  from torch import nn
  from trainer.logging.tensorboard_logger import TensorboardLogger
  from TTS.tts.layers.overflow.common_layers import Encoder, OverflowUtils
  from TTS.tts.layers.overflow.decoder import Decoder
  from TTS.tts.layers.overflow.neural_hmm import NeuralHMM
  from TTS.tts.layers.overflow.plotting_utils import get_spec_from_most_probable_state, plot_transition_probabilities_to_numpy
  from TTS.tts.models.base_tts import BaseTTS
  from TTS.tts.utils.speakers import SpeakerManager
  from TTS.tts.utils.text.tokenizer import TTSTokenizer
  from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
  from TTS.utils.generic_utils import format_aux_input
  from TTS.utils.io import load_fsspec
label_desc: Use image node_id 1 for calling the NLLLoss obj's underlying member method code with example usage: obj.forward(log_prob) and returns: return_dict