code: def __call__(self, x, copy=True):
      x = check_array(
          x,
          accept_sparse=True,
          estimator="the normalize function",
          dtype=(np.float64, np.float32, np.float16),
      )
  
      normed = None
      if not self._return_norm:
          res = self.new_tensor([x], shape=x.shape, order=x.order)
      else:
          kws = [
              {"shape": x.shape, "order": x.order},
              {
                  "shape": (
                      x.shape[0] if self._axis == 1 else x.shape[1],
                  ),
                  "order": TensorOrder.C_ORDER,
              },
          ]
          res, normed = self.new_tensors([x], kws=kws, output_limit=2)
  
      if not copy and self._axis == 1:
          # follow the behaviour of sklearn
          x.data = res.data
  
      if normed is None:
          return res
      return ExecutableTuple([res, normed])
  
imports: import numpy
  from ...None import opcodes
  from ...core import ExecutableTuple, recursive_tile
  from ...serialization.serializables import KeyField, StringField, Int32Field, BoolField
  from ...tensor.operands import TensorOperand, TensorOperandMixin
  from ...tensor.core import TensorOrder
  from ...tensor.array_utils import as_same_device, device, sparse, issparse
  from ...None import tensor
  from ..utils import check_array
label_desc: Use image node_id 9 for calling the TensorNormalize obj's underlying member method code with example usage: obj.__call__(x, copy) and returns: ExecutableTuple, res