code: def __init__(
      self,
      data=None,
      index=None,
      dtype=None,
      name=None,
      copy=False,
      chunk_size=None,
      gpu=None,
      sparse=None,
      num_partitions=None,
  ):
      if dtype is not None:
          dtype = pandas_dtype(dtype)
      need_repart = False
      if isinstance(data, (TENSOR_TYPE, INDEX_TYPE)):
          if chunk_size is not None:
              data = data.rechunk(chunk_size)
          name = name or getattr(data, "name", None)
          series = series_from_tensor(
              data, index=index, name=name, gpu=gpu, sparse=sparse
          )
          need_repart = num_partitions is not None
      elif isinstance(index, INDEX_TYPE):
          if data is not None:
              data = astensor(data, chunk_size=chunk_size)
          series = series_from_tensor(
              data,
              index=index,
              name=name,
              dtype=dtype,
              gpu=gpu,
              sparse=sparse,
          )
          need_repart = num_partitions is not None
      elif isinstance(data, SERIES_TYPE):
          if not hasattr(data, "data"):
              # SeriesData
              series = _Series(data)
          else:
              series = data
          need_repart = num_partitions is not None
      else:
          if is_cudf(data) or is_cupy(data):  # pragma: no cover
              pd_series = cudf.Series(
                  data, index=index, dtype=dtype, name=name
              )
              if copy:
                  pd_series = pd_series.copy()
          else:
              pd_series = pd.Series(
                  data, index=index, dtype=dtype, name=name, copy=copy
              )
          if num_partitions is not None:
              chunk_size = ceildiv(len(pd_series), num_partitions)
          series = from_pandas_series(
              pd_series, chunk_size=chunk_size, gpu=gpu, sparse=sparse
          )
  
      if need_repart:
          series = series.rebalance(num_partitions=num_partitions)
      super().__init__(series.data)
  
imports: import pandas
  from pandas.core.dtypes.common import pandas_dtype
  from ..core import ENTITY_TYPE
  from ..serialization.serializables import SerializableMeta
  from ..tensor import tensor, stack
  from ..tensor.array_utils import is_cupy
  from ..tensor.core import TENSOR_TYPE
  from ..utils import ceildiv, lazy_import
  from .core import DATAFRAME_TYPE, SERIES_TYPE, INDEX_TYPE, DataFrame, Series, Index
  from .datasource.dataframe import from_pandas
  from .datasource.series import from_pandas
  from .datasource.index import from_pandas, from_tileable
  from .datasource.from_tensor import dataframe_from_tensor, series_from_tensor, dataframe_from_1d_tileables
  from .utils import is_index, is_cudf
label_desc: Use image node_id 1 to create a new Series object from inherited base classes: _Series with example: obj = Series(data, index, dtype, name, copy, chunk_size, gpu, sparse, num_partitions)