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_q, tiny_r = np.linalg.qr(np.ones((1, 1), dtype=a.dtype))
  
      x, y = a.shape
      q_shape, r_shape = (
          (a.shape, (y, y)) if x > y else ((x, x), a.shape)
      )
      q, r = self.new_tensors(
          [a],
          kws=[
              {
                  "side": "q",
                  "dtype": tiny_q.dtype,
                  "shape": q_shape,
                  "order": TensorOrder.C_ORDER,
              },
              {
                  "side": "r",
                  "dtype": tiny_r.dtype,
                  "shape": r_shape,
                  "order": TensorOrder.C_ORDER,
              },
          ],
      )
      return ExecutableTuple([q, r])
  
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 device, as_same_device
  from ..datasource import tensor
  from ..operands import TensorHasInput, TensorOperandMixin
  from ..core import TensorOrder
  from .core import SFQR, TSQR
label_desc: Use image node_id 5 for calling the TensorQR obj's underlying member method code with example usage: obj.__call__(a) and returns: ExecutableTuple