code: def tensorinv(a, ind=2):
      """Computes the inverse of a tensor.
  
      This function computes tensor ``a_inv`` from tensor ``a`` such that
      ``tensordot(a_inv, a, ind) == I``, where ``I`` denotes the identity tensor.
  
      Args:
          a (cupy.ndarray):
              The tensor such that
              ``prod(a.shape[:ind]) == prod(a.shape[ind:])``.
          ind (int):
              The positive number used in ``axes`` option of ``tensordot``.
  
      Returns:
          cupy.ndarray:
              The inverse of a tensor whose shape is equivalent to
              ``a.shape[ind:] + a.shape[:ind]``.
  
      .. warning::
          This function calls one or more cuSOLVER routine(s) which may yield
          invalid results if input conditions are not met.
          To detect these invalid results, you can set the `linalg`
          configuration to a value that is not `ignore` in
          :func:`cupyx.errstate` or :func:`cupyx.seterr`.
  
      .. seealso:: :func:`numpy.linalg.tensorinv`
      """
      _util._assert_cupy_array(a)
  
      if ind <= 0:
          raise ValueError("Invalid ind argument")
      oldshape = a.shape
      invshape = oldshape[ind:] + oldshape[:ind]
      prod = internal.prod(oldshape[ind:])
      a = a.reshape(prod, -1)
      a_inv = inv(a)
      return a_inv.reshape(*invshape)
  
file_path: cupy/cupy/linalg/_solve.py
imports: import warnings
  import numpy
  from numpy import linalg
  import cupy
  from cupy._core import internal
  from cupy.cuda import device
  from cupy.linalg import _decomposition
  from cupy.linalg import _util
  import cupyx
label_desc: Use image node_id 8 for calling a global function with example usage: tensorinv(a, ind) and returns: a_inv