code: def __init__(
      self,
      q=None,
      interpolation=None,
      axis=None,
      numeric_only=None,
      dtype=None,
      gpu=None,
      output_types=None,
      **kw,
  ):
      super().__init__(
          _q=q,
          _interpolation=interpolation,
          _axis=axis,
          _numeric_only=numeric_only,
          _dtype=dtype,
          _output_types=output_types,
          gpu=gpu,
          **kw,
      )
  
imports: from collections import OrderedDict
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import ENTITY_TYPE, recursive_tile
  from ...serialization.serializables import KeyField, AnyField, StringField, DataTypeField, BoolField, Int32Field
  from ...tensor.core import TENSOR_TYPE, TENSOR_CHUNK_TYPE
  from ...tensor.datasource import empty, tensor, from_series, from_dataframe
  from ...tensor.statistics.quantile import quantile
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..core import DATAFRAME_TYPE
  from ..datasource.from_tensor import series_from_tensor, dataframe_from_tensor
  from ..initializer import DataFrame
  from ..utils import parse_index, build_empty_df, find_common_type, validate_axis
label_desc: Use image node_id 1 to create a new DataFrameQuantile object from inherited base classes: DataFrameOperand, DataFrameOperandMixin with example: obj = DataFrameQuantile(q, interpolation, axis, numeric_only, dtype, gpu, output_types)