code: def entropy(x):
      """Calculate entropy of a pre-softmax logit Tensor"""
      exp_x = torch.exp(x)
      A = torch.sum(exp_x, dim=1)  # sum of exp(x_i)
      B = torch.sum(x * exp_x, dim=1)  # sum of x_i * exp(x_i)
      return torch.log(A) - B / A
  
imports: import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from transformers.models.bert.modeling_bert import BERT_INPUTS_DOCSTRING, BERT_START_DOCSTRING, BertEmbeddings, BertLayer, BertPooler, BertPreTrainedModel
label_desc: Use image node_id 1 for calling a global function with example usage: entropy(x) and returns: unknown