class_docstr: A distributed Dask DataFrame where the backing dataframe is a
  :class:`cuDF DataFrame <cudf:cudf.DataFrame>`.
  
  Typically you would not construct this object directly, but rather
  use one of Dask-cuDF's IO routines.
  
  Most operations on :doc:`Dask DataFrames <dask:dataframe>` are
  supported, with many of the same caveats.
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code: def _assign_column(self, k, v):
      def assigner(df, k, v):
          out = df.copy()
          out[k] = v
          return out
  
      meta = assigner(self._meta, k, dask_make_meta(v))
      return self.map_partitions(assigner, k, v, meta=meta)
  
imports: import math
  import textwrap
  import warnings
  import numpy
  import pandas
  from tlz import partition_all
  from dask import dataframe
  from dask.base import normalize_token, tokenize
  from dask.dataframe.core import Scalar, handle_out, make_meta, map_partitions
  from dask.dataframe.utils import raise_on_meta_error
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import M, OperatorMethodMixin, apply, derived_from, funcname
  import cudf
  from cudf import _lib
  from cudf.utils.nvtx_annotation import _dask_cudf_nvtx_annotate
  from dask_cudf import sorting
  from dask_cudf.accessors import ListMethods, StructMethods
  from dask_cudf.sorting import _deprecate_shuffle_kwarg, _get_shuffle_method
label_desc: Use image node_id 1 for calling the DataFrame obj's underlying member method code with example usage: obj._assign_column(k, v) and returns: self, out