code: def entropy_from_logits(logits: torch.Tensor) -> torch.Tensor:
      """Calculate entropy from logits."""
      pd = torch.nn.functional.softmax(logits, dim=-1)
      entropy = torch.logsumexp(logits, axis=-1) - torch.sum(
          pd * logits, axis=-1
      )
      return entropy
  
imports: import gc
  import random
  import warnings
  from contextlib import contextmanager
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pad_sequence
  from transformers import top_k_top_p_filtering
  from .import_utils import is_npu_available, is_xpu_available
label_desc: Use image node_id 12 for calling a global function with example usage: entropy_from_logits(logits) and returns: entropy