code: def entropy_from_logits(logits: torch.Tensor):
      """Calculate entropy from logits."""
      pd = torch.nn.functional.softmax(logits, dim=-1)
      entropy = torch.logsumexp(logits, dim=-1) - torch.sum(
          pd * logits, dim=-1
      )
      return entropy
  
imports: import math
  import time
  import warnings
  from collections.abc import Mapping
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import torch
  import torch.nn.functional
  from datasets import Dataset
  from huggingface_hub import PyTorchModelHubMixin
  from torch.cuda.amp import autocast
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader
  from transformers import DataCollatorForLanguageModeling, PreTrainedTokenizer, PreTrainedTokenizerFast
  from llm_studio.src.utils.modeling_utils import unwrap_model
label_desc: Use image node_id 8 for calling a global function with example usage: entropy_from_logits(logits) and returns: entropy