code: def _incremental_mean_and_var(
      X, last_mean, last_variance, last_sample_count, sample_weight=None
  ):
      """Calculate mean update and a Youngs and Cramer variance update.
  
      If sample_weight is given, the weighted mean and variance is computed.
  
      Update a given mean and (possibly) variance according to new data given
      in X. last_mean is always required to compute the new mean.
      If last_variance is None, no variance is computed and None return for
      updated_variance.
  
      From the paper "Algorithms for computing the sample variance: analysis and
      recommendations", by Chan, Golub, and LeVeque.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Data to use for variance update.
  
      last_mean : array-like of shape (n_features,)
  
      last_variance : array-like of shape (n_features,)
  
      last_sample_count : array-like of shape (n_features,)
          The number of samples encountered until now if sample_weight is None.
          If sample_weight is not None, this is the sum of sample_weight
          encountered.
  
      sample_weight : array-like of shape (n_samples,) or None
          Sample weights. If None, compute the unweighted mean/variance.
  
      Returns
      -------
      updated_mean : ndarray of shape (n_features,)
  
      updated_variance : ndarray of shape (n_features,)
          None if last_variance was None.
  
      updated_sample_count : ndarray of shape (n_features,)
  
      Notes
      -----
      NaNs are ignored during the algorithm.
  
      References
      ----------
      T. Chan, G. Golub, R. LeVeque. Algorithms for computing the sample
          variance: recommendations, The American Statistician, Vol. 37, No. 3,
          pp. 242-247
  
      Also, see the sparse implementation of this in
      `utils.sparsefuncs.incr_mean_variance_axis` and
      `utils.sparsefuncs_fast.incr_mean_variance_axis0`
      """
      # old = stats until now
      # new = the current increment
      # updated = the aggregated stats
      last_sum = last_mean * last_sample_count
      X_nan_mask = np.isnan(X)
      if np.any(X_nan_mask):
          sum_op = np.nansum
      else:
          sum_op = np.sum
      if sample_weight is not None:
          # equivalent to np.nansum(X * sample_weight, axis=0)
          # safer because np.float64(X*W) != np.float64(X)*np.float64(W)
          new_sum = _safe_accumulator_op(
              np.matmul, sample_weight, np.where(X_nan_mask, 0, X)
          )
          new_sample_count = _safe_accumulator_op(
              np.sum, sample_weight[:, None] * (~X_nan_mask), axis=0
          )
      else:
          new_sum = _safe_accumulator_op(sum_op, X, axis=0)
          n_samples = X.shape[0]
          new_sample_count = n_samples - np.sum(X_nan_mask, axis=0)
  
      updated_sample_count = last_sample_count + new_sample_count
  
      updated_mean = (last_sum + new_sum) / updated_sample_count
  
      if last_variance is None:
          updated_variance = None
      else:
          T = new_sum / new_sample_count
          temp = X - T
          if sample_weight is not None:
              # equivalent to np.nansum((X-T)**2 * sample_weight, axis=0)
              # safer because np.float64(X*W) != np.float64(X)*np.float64(W)
              correction = _safe_accumulator_op(
                  np.matmul,
                  sample_weight,
                  np.where(X_nan_mask, 0, temp),
              )
              temp **= 2
              new_unnormalized_variance = _safe_accumulator_op(
                  np.matmul,
                  sample_weight,
                  np.where(X_nan_mask, 0, temp),
              )
          else:
              correction = _safe_accumulator_op(sum_op, temp, axis=0)
              temp **= 2
              new_unnormalized_variance = _safe_accumulator_op(
                  sum_op, temp, axis=0
              )
  
          # correction term of the corrected 2 pass algorithm.
          # See "Algorithms for computing the sample variance: analysis
          # and recommendations", by Chan, Golub, and LeVeque.
          new_unnormalized_variance -= (
              correction**2 / new_sample_count
          )
  
          last_unnormalized_variance = last_variance * last_sample_count
  
          with np.errstate(divide="ignore", invalid="ignore"):
              last_over_new_count = last_sample_count / new_sample_count
              updated_unnormalized_variance = (
                  last_unnormalized_variance
                  + new_unnormalized_variance
                  + last_over_new_count
                  / updated_sample_count
                  * (last_sum / last_over_new_count - new_sum) ** 2
              )
  
          zeros = last_sample_count == 0
          updated_unnormalized_variance[
              zeros
          ] = new_unnormalized_variance[zeros]
          updated_variance = (
              updated_unnormalized_variance / updated_sample_count
          )
  
      return updated_mean, updated_variance, updated_sample_count
  
imports: import warnings
  import numpy
  from scipy import linalg, sparse
  from .None import check_random_state
  from ._array_api import _is_numpy_namespace, get_namespace
  from ._logistic_sigmoid import _log_logistic_sigmoid
  from .sparsefuncs_fast import csr_row_norms
  from .validation import check_array
label_desc: Use image node_id 16 for calling a global function with example usage: _incremental_mean_and_var(X, last_mean, last_variance, last_sample_count, sample_weight) and returns: updated_mean,  updated_variance,  updated_sample_count