code: def __init__(
      self,
      norm=None,
      axis=None,
      return_norm=None,
      use_sklearn=None,
      **kw,
  ):
      super().__init__(
          _norm=norm,
          _axis=axis,
          _return_norm=return_norm,
          _use_sklearn=use_sklearn,
          **kw,
      )
      if self._use_sklearn is None:
          # force to use sklearn if not specified
          self._use_sklearn = True
  
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 1 to create a new TensorNormalize object from inherited base classes: TensorOperand, TensorOperandMixin with example: obj = TensorNormalize(norm, axis, return_norm, use_sklearn)