code: def _validate_seuclidean_kwargs(X, m, n, **kwargs):
      V = kwargs.pop("V", None)
      if V is None:
          if isinstance(X, tuple):
              X = np.vstack(X)
          V = np.var(X.astype(np.float64, copy=False), axis=0, ddof=1)
      else:
          V = np.asarray(V, order="c")
          if len(V.shape) != 1:
              raise ValueError(
                  "Variance vector V must " "be one-dimensional."
              )
          if V.shape[0] != n:
              raise ValueError(
                  "Variance vector V must be of the same "
                  "dimension as the vectors on which the distances "
                  "are computed."
              )
      kwargs["V"] = _convert_to_double(V)
      return kwargs
  
imports: import math
  import warnings
  import numpy
  import dataclasses
  from typing import Optional, Callable
  from functools import partial
  from scipy._lib._util import _asarray_validated
  from .None import _distance_wrap
  from .None import _hausdorff
  from ..linalg import norm
  from ..special import rel_entr
  from .None import _distance_pybind
label_desc: Use image node_id 13 for calling a global function with example usage: _validate_seuclidean_kwargs(X, m, n) and returns: kwargs