code: def __call__(self, target: DataFrame, value):
      raw_target = target
  
      inputs = [target]
      if np.isscalar(value):
          value_dtype = np.array(value).dtype
      elif self._is_scalar_tensor(value):
          inputs.append(value)
          value_dtype = value.dtype
      else:
          if isinstance(value, (pd.Series, SERIES_TYPE)):
              value = asseries(value)
              value_dtype = value.dtype
          elif isinstance(value, (pd.DataFrame, DATAFRAME_TYPE)):
              if (
                  len(self.indexes) != value.shape[1]
              ):  # pragma: no cover
                  raise ValueError("Columns must be same length as key")
  
              value = asframe(value)
              value_dtype = pd.Series(
                  list(value.dtypes), index=self._indexes
              )
          elif is_list_like(value) or isinstance(value, TENSOR_TYPE):
              # convert to numpy to get actual dim and shape
              if is_list_like(value):
                  value = np.array(value)
  
              if value.ndim == 1:
                  value = asseries(value, index=target.index)
                  value_dtype = value.dtype
              else:
                  if (
                      len(self.indexes) != value.shape[1]
                  ):  # pragma: no cover
                      raise ValueError(
                          "Columns must be same length as key"
                      )
  
                  value = asframe(value, index=target.index)
                  value_dtype = pd.Series(
                      list(value.dtypes), index=self._indexes
                  )
          else:  # pragma: no cover
              raise TypeError(
                  "Wrong value type, could be one of scalar, Series or tensor"
              )
  
          if target.shape[0] == 0:
              # target empty, reindex target first
              target = target.reindex(value.index)
              inputs[0] = target
          elif value.index_value.key != target.index_value.key:
              # need reindex when target df is not empty and index different
              value = value.reindex(target.index)
          inputs.append(value)
  
      index_value = target.index_value
      dtypes = target.dtypes.copy(deep=True)
  
      try:
          dtypes.loc[self._indexes] = value_dtype
      except KeyError:
          # when some index not exist, try update one by one
          if isinstance(value_dtype, pd.Series):
              for idx in self._indexes:
                  dtypes.loc[idx] = value_dtype.loc[idx]
          else:
              for idx in self._indexes:
                  dtypes.loc[idx] = value_dtype
  
      columns_value = parse_index(dtypes.index, store_data=True)
      ret = self.new_dataframe(
          inputs,
          shape=(target.shape[0], len(dtypes)),
          dtypes=dtypes,
          index_value=index_value,
          columns_value=columns_value,
      )
      raw_target.data = ret.data
  
imports: import collections
  import numpy
  import pandas
  from pandas.api.types import is_list_like
  from ...None import opcodes
  from ...core import OutputType, recursive_tile
  from ...serialization.serializables import KeyField, AnyField
  from ...tensor.core import TENSOR_TYPE
  from ...utils import pd_release_version
  from ..core import DATAFRAME_TYPE, SERIES_TYPE, DataFrame
  from ..initializer import DataFrame, Series
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index, is_index_value_identical
label_desc: Use image node_id 7 for calling the DataFrameSetitem obj's underlying member method code with example usage: obj.__call__(target, value) without return types