code: def wronskian(functions, var, method="bareiss"):
      """
      Compute Wronskian for [] of functions
  
      ::
  
                           | f1       f2        ...   fn      |
                           | f1'      f2'       ...   fn'     |
                           |  .        .        .      .      |
          W(f1, ..., fn) = |  .        .         .     .      |
                           |  .        .          .    .      |
                           |  (n)      (n)            (n)     |
                           | D   (f1) D   (f2)  ...  D   (fn) |
  
      see: https://en.wikipedia.org/wiki/Wronskian
  
      See Also
      ========
  
      sympy.matrices.matrixbase.MatrixBase.jacobian
      hessian
      """
  
      functions = [sympify(f) for f in functions]
      n = len(functions)
      if n == 0:
          return S.One
      W = Matrix(n, n, lambda i, j: functions[i].diff(var, j))
      return W.det(method)
  
imports: import random
  from sympy.core.basic import Basic
  from sympy.core.singleton import S
  from sympy.core.symbol import Symbol
  from sympy.core.sympify import sympify
  from sympy.functions.elementary.trigonometric import cos, sin
  from sympy.utilities.decorator import doctest_depends_on
  from sympy.utilities.exceptions import sympy_deprecation_warning
  from sympy.utilities.iterables import is_sequence
  from .exceptions import ShapeError
  from .decompositions import _cholesky, _LDLdecomposition
  from .matrixbase import MatrixBase
  from .repmatrix import MutableRepMatrix, RepMatrix
  from .solvers import _lower_triangular_solve, _upper_triangular_solve
label_desc: Use image node_id 22 for calling a global function with example usage: wronskian(functions, var, method) and returns: W, S