code: def __call__(self):
      num_class = int(getattr(self.model, "n_classes_", 2))
      if self.proba:
          shape = (self.data.shape[0], num_class)
      else:
          shape = (self.data.shape[0],)
  
      if self._proba:
          dtype = np.dtype(np.float_)
      elif hasattr(self.model, "classes_"):
          dtype = np.array(self.model.classes_).dtype
      else:
          dtype = getattr(
              self.model, "out_dtype_", [np.dtype("float")]
          )[0]
  
      if self.output_types[0] == OutputType.tensor:
          # tensor
          return self.new_tileable(
              [self.data],
              shape=shape,
              dtype=dtype,
              order=TensorOrder.C_ORDER,
          )
      elif self.output_types[0] == OutputType.dataframe:
          # dataframe
          dtypes = pd.Series([dtype] * num_class)
          columns_value = parse_index(
              pd.Index(self.model.classes_), store_data=True
          )
          return self.new_tileable(
              [self.data],
              shape=shape,
              dtypes=dtypes,
              columns_value=columns_value,
              index_value=self.data.index_value,
          )
      else:
          return self.new_tileable(
              [self.data],
              shape=shape,
              index_value=self.data.index_value,
              name="predictions",
              dtype=dtype,
          )
  
imports: import pickle
  import numpy
  import pandas
  from ....None import opcodes
  from ....core import recursive_tile
  from ....dataframe.utils import parse_index
  from ....serialization.serializables import BoolField, BytesField, DictField, KeyField
  from ....tensor.core import TENSOR_TYPE, TensorOrder
  from ...operands import LearnOperand, LearnOperandMixin, OutputType
label_desc: Use image node_id 7 for calling the LGBMPredict obj's underlying member method code with example usage: obj.__call__() and returns: self, self, self