code: def Hamming_loss(
      y_pred: Tensor,
      y_true: Tensor,
      sigmoid: bool = True,
      thresh: float = CLASSIFICATION_THRESHOLD,
      sample_weight=None,
      **kwargs,
  ):
      if sigmoid:
          y_pred = y_pred.sigmoid()
      y_pred = (y_pred > thresh).float()
      return hamming_loss(y_true, y_pred, sample_weight=sample_weight)
  
file_path: fast-bert/fast_bert/metrics.py
imports: import numpy
  from torch import Tensor
  from sklearn.metrics import roc_curve, auc, hamming_loss, accuracy_score, confusion_matrix
  import pdb
  import logging
label_desc: Use image node_id 6 for calling a global function with example usage: Hamming_loss(y_pred, y_true, sigmoid, thresh, sample_weight) and returns: hamming_loss