code: def safe_sparse_dot(a, b, *, dense_output=False):
      """Dot product that handle the sparse matrix case correctly.
  
      Parameters
      ----------
      a : {ndarray, sparse matrix}
      b : {ndarray, sparse matrix}
      dense_output : bool, default=False
          When False, ``a`` and ``b`` both being sparse will yield sparse output.
          When True, output will always be a dense array.
  
      Returns
      -------
      dot_product : {ndarray, sparse matrix}
          Sparse if ``a`` and ``b`` are sparse and ``dense_output=False``.
      """
      if a.ndim > 2 or b.ndim > 2:
          if sparse.issparse(a):
              # sparse is always 2D. Implies b is 3D+
              # [i, j] @ [k, ..., l, m, n] -> [i, k, ..., l, n]
              b_ = np.rollaxis(b, -2)
              b_2d = b_.reshape((b.shape[-2], -1))
              ret = a @ b_2d
              ret = ret.reshape(a.shape[0], *b_.shape[1:])
          elif sparse.issparse(b):
              # sparse is always 2D. Implies a is 3D+
              # [k, ..., l, m] @ [i, j] -> [k, ..., l, j]
              a_2d = a.reshape(-1, a.shape[-1])
              ret = a_2d @ b
              ret = ret.reshape(*a.shape[:-1], b.shape[1])
          else:
              ret = np.dot(a, b)
      else:
          ret = a @ b
  
      if (
          sparse.issparse(a)
          and sparse.issparse(b)
          and dense_output
          and hasattr(ret, "toarray")
      ):
          return ret.toarray()
      return ret
  
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 5 for calling a global function with example usage: safe_sparse_dot(a, b) and returns: ret, ret