code: def _critical_success_index_update(
      preds: Tensor,
      target: Tensor,
      threshold: float,
      keep_sequence_dim: Optional[int] = None,
  ) -> Tuple[Tensor, Tensor, Tensor]:
      """Update and return variables required to compute Critical Success Index. Checks for same shape of tensors.
  
      Args:
          preds: Predicted tensor
          target: Ground truth tensor
          threshold: Values above or equal to threshold are replaced with 1, below by 0
          keep_sequence_dim: Index of the sequence dimension if the inputs are sequences of images. If specified,
              the score will be calculated separately for each image in the sequence. If ``None``, the score will be
              calculated across all dimensions.
  
      """
      _check_same_shape(preds, target)
  
      if keep_sequence_dim is None:
          sum_dims = None
      elif not 0 <= keep_sequence_dim < preds.ndim:
          raise ValueError(
              f"Expected keep_sequence dim to be in range [0, {preds.ndim}] but got {keep_sequence_dim}"
          )
      else:
          sum_dims = tuple(
              i for i in range(preds.ndim) if i != keep_sequence_dim
          )
  
      # binarize the tensors with the threshold
      preds_bin = (preds >= threshold).bool()
      target_bin = (target >= threshold).bool()
  
      if keep_sequence_dim is None:
          hits = torch.sum(preds_bin & target_bin).int()
          misses = torch.sum(
              (preds_bin ^ target_bin) & target_bin
          ).int()
          false_alarms = torch.sum(
              (preds_bin ^ target_bin) & preds_bin
          ).int()
      else:
          hits = torch.sum(preds_bin & target_bin, dim=sum_dims).int()
          misses = torch.sum(
              (preds_bin ^ target_bin) & target_bin, dim=sum_dims
          ).int()
          false_alarms = torch.sum(
              (preds_bin ^ target_bin) & preds_bin, dim=sum_dims
          ).int()
      return hits, misses, false_alarms
  
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.compute import _safe_divide
label_desc: Use image node_id 1 for calling a global function with example usage: _critical_success_index_update(preds, target, threshold, keep_sequence_dim) and returns: hits,  misses,  false_alarms