code: def confusion_matrix(
      pred: torch.Tensor,
      target: torch.Tensor,
      num_classes: int,
      normalized: bool = False,
  ) -> torch.Tensor:
      r"""Compute confusion matrix to evaluate the accuracy of a classification.
  
      Args:
          pred: tensor with estimated targets returned by a
            classifier. The shape can be :math:`(B, *)` and must contain integer
            values between 0 and K-1.
          target: tensor with ground truth (correct) target
            values. The shape can be :math:`(B, *)` and must contain integer
            values between 0 and K-1, where targets are assumed to be provided as
            one-hot vectors.
          num_classes: total possible number of classes in target.
          normalized: whether to return the confusion matrix normalized.
  
      Returns:
          a tensor containing the confusion matrix with shape
          :math:`(B, K, K)` where K is the number of classes.
  
      Example:
          >>> logits = torch.tensor([[0, 1, 0]])
          >>> target = torch.tensor([[0, 1, 0]])
          >>> confusion_matrix(logits, target, num_classes=3)
          tensor([[[2., 0., 0.],
                   [0., 1., 0.],
                   [0., 0., 0.]]])
      """
      if not torch.is_tensor(pred) and pred.dtype is not torch.int64:
          raise TypeError(
              f"Input pred type is not a torch.Tensor with torch.int64 dtype. Got {type(pred)}"
          )
  
      if (
          not torch.is_tensor(target)
          and target.dtype is not torch.int64
      ):
          raise TypeError(
              f"Input target type is not a torch.Tensor with torch.int64 dtype. Got {type(target)}"
          )
      if not pred.shape == target.shape:
          raise ValueError(
              f"Inputs pred and target must have the same shape. Got: {pred.shape} and {target.shape}"
          )
      if not pred.device == target.device:
          raise ValueError(
              f"Inputs must be in the same device. Got: {pred.device} - {target.device}"
          )
  
      if not isinstance(num_classes, int) or num_classes < 2:
          raise ValueError(
              f"The number of classes must be an integer bigger than two. Got: {num_classes}"
          )
  
      batch_size: int = pred.shape[0]
  
      # hack for bitcounting 2 arrays together
      # NOTE: torch.bincount does not implement batched version
      pre_bincount: torch.Tensor = pred + target * num_classes
      pre_bincount_vec: torch.Tensor = pre_bincount.view(batch_size, -1)
  
      confusion_list = []
      for iter_id in range(batch_size):
          pb: torch.Tensor = pre_bincount_vec[iter_id]
          bin_count: torch.Tensor = torch.bincount(
              pb, minlength=num_classes**2
          )
          confusion_list.append(bin_count)
  
      confusion_vec: torch.Tensor = torch.stack(confusion_list)
      confusion_mat: torch.Tensor = confusion_vec.view(
          batch_size, num_classes, num_classes
      ).to(
          torch.float32
      )  # BxKxK
  
      if normalized:
          norm_val: torch.Tensor = torch.sum(
              confusion_mat, dim=1, keepdim=True
          )
          confusion_mat = confusion_mat / (norm_val + 1e-6)
  
      return confusion_mat
  
label_desc: Use image node_id 1 for calling a global function with example usage: confusion_matrix(pred, target, num_classes, normalized) and returns: confusion_mat