code: def __call__(self, a):
      a = astensor(a)
  
      if a.ndim != 2:
          raise LinAlgError(
              f"{a.ndim}-dimensional tensor given. Tensor must be two-dimensional"
          )
  
      tiny_U, tiny_s, tiny_V = np.linalg.svd(
          np.ones((1, 1), dtype=a.dtype)
      )
  
      # if a's shape is (6, 18), U's shape is (6, 6), s's shape is (6,), V's shape is (6, 18)
      # if a's shape is (18, 6), U's shape is (18, 6), s's shape is (6,), V's shape is (6, 6)
      U_shape, s_shape, V_shape = calc_svd_shapes(a)
      U, s, V = self.new_tensors(
          [a],
          order=TensorOrder.C_ORDER,
          kws=[
              {"side": "U", "dtype": tiny_U.dtype, "shape": U_shape},
              {"side": "s", "dtype": tiny_s.dtype, "shape": s_shape},
              {"side": "V", "dtype": tiny_V.dtype, "shape": V_shape},
          ],
      )
      return ExecutableTuple([U, s, V])
  
imports: import numpy
  from numpy.linalg import LinAlgError
  from ...None import opcodes
  from ...serialization.serializables import KeyField, StringField
  from ...core import ExecutableTuple
  from ..array_utils import as_same_device, device
  from ..datasource import tensor
  from ..operands import TensorHasInput, TensorOperandMixin
  from ..core import TensorOrder
  from .core import TSQR
  from .utils import calc_svd_shapes
label_desc: Use image node_id 6 for calling the TensorSVD obj's underlying member method code with example usage: obj.__call__(a) and returns: ExecutableTuple