original_docstring: Generate a function to evaluate the Jacobian of a function.
  
      The passed function should take as arguments and return symbolic array(s),
      and likewise the returned function will take symbolic array arguments.
  
      Args:
          func (Callable[..., Array]): Function which takes one or more arrays as
              arguments and returns an array.
          wrt (int): Index of argument to take derivatives with respect to.
          return_aux (bool): Whether the generated Jacobian function returns
              both the Jacobian and value of `func` as a 2-tuple (True) or just
              the Jacobian (False).
  
      Returns:
          Callable[..., Union[Array, Tuple[Array, Array]]]: Generated Jacobian
              function.
      
docstring: Generate a function to evaluate the Jacobian of a function.
  The passed function should take as arguments and return symbolic array(s),
  and likewise the returned function will take symbolic array arguments.
docstring_tokens: Generate a function to evaluate the Jacobian of a function . The passed function should take as arguments and return symbolic array ( s ) and likewise the returned function will take symbolic array arguments .
code: def jacobian(func, wrt=0, return_aux=False):
      """Generate a function to evaluate the Jacobian of a function.
  
      The passed function should take as arguments and return symbolic array(s),
      and likewise the returned function will take symbolic array arguments.
  
      Args:
          func (Callable[..., Array]): Function which takes one or more arrays as
              arguments and returns an array.
          wrt (int): Index of argument to take derivatives with respect to.
          return_aux (bool): Whether the generated Jacobian function returns
              both the Jacobian and value of `func` as a 2-tuple (True) or just
              the Jacobian (False).
  
      Returns:
          Callable[..., Union[Array, Tuple[Array, Array]]]: Generated Jacobian
              function.
      """
  
      @_wrap_derived(func, 'jacob', 'Jacobian')
      def jacob_func(*args):
          val = _get_sympy_func(func)(*args)
          jacob = _jacobian_transpose(
              sym.diff(val, args[wrt]), val.shape, args[wrt].shape)
          return (jacob, val) if return_aux else jacob
  
      return jacob_func
code_tokens: def jacobian ( func , wrt = 0 , return_aux = False ) : @ _wrap_derived ( func , 'jacob' , 'Jacobian' ) def jacob_func ( * args ) : val = _get_sympy_func ( func ) ( * args ) jacob = _jacobian_transpose ( sym . diff ( val , args [ wrt ] ) , val . shape , args [ wrt ] . shape ) return ( jacob , val ) if return_aux else jacob return jacob_func
short_docstring: Generate a function to evaluate the Jacobian of a function.
short_docstring_tokens: Generate a function to evaluate the Jacobian of a function .
comment: """Generate a function to evaluate the Jacobian of a function.
  
      The passed function should take as arguments and return symbolic array(s),
      and likewise the returned function will take symbolic array arguments.
  
      Args:
          func (Callable[..., Array]): Function which takes one or more arrays as
              arguments and returns an array.
          wrt (int): Index of argument to take derivatives with respect to.
          return_aux (bool): Whether the generated Jacobian function returns
              both the Jacobian and value of `func` as a 2-tuple (True) or just
              the Jacobian (False).
  
      Returns:
          Callable[..., Union[Array, Tuple[Array, Array]]]: Generated Jacobian
              function.
      """