docstr: Get normalized probabilities (or log probs) from a net's output.
code: def get_normalized_probs(self, net_output, log_probs):
      """Get normalized probabilities (or log probs) from a net's output."""
      logits = net_output
  
      if log_probs:
          return utils.log_softmax(logits.float(), dim=-1)
      else:
          return utils.softmax(logits.float(), dim=-1)
  
imports: import contextlib
  import logging
  from argparse import Namespace
  from dataclasses import dataclass, field
  from typing import Any, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from omegaconf import II, MISSING, open_dict
  from fairseq import checkpoint_utils, tasks, utils
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
  from fairseq.models import BaseFairseqModel, FairseqEncoder, register_model
  from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES, Wav2Vec2Config
  from fairseq.models.wav2vec.wav2vec2_asr import Embedding, Linear, Wav2VecEncoder, Wav2Vec2AsrConfig
  from fairseq.tasks import FairseqTask
label_desc: Use image node_id 4 for calling the Wav2VecClassification obj's underlying member method code with example usage: obj.get_normalized_probs(net_output, log_probs) and returns: utils, utils