class_docstr: Model-Parallel Multi-GPU Linear Regression Model.
docstr: Make predictions for X and returns a dask collection.
  
  Parameters
  ----------
  X : Dask cuDF dataframe  or CuPy backed Dask Array (n_rows, n_features)
      Distributed dense matrix (floats or doubles) of shape
      (n_samples, n_features).
  
  delayed : bool (default = True)
      Whether to do a lazy prediction (and return Delayed objects) or an
      eagerly executed one.
  
  Returns
  -------
  y : Dask cuDF dataframe  or CuPy backed Dask Array (n_rows, 1)
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code: def predict(self, X, delayed=True):
      """
      Make predictions for X and returns a dask collection.
  
      Parameters
      ----------
      X : Dask cuDF dataframe  or CuPy backed Dask Array (n_rows, n_features)
          Distributed dense matrix (floats or doubles) of shape
          (n_samples, n_features).
  
      delayed : bool (default = True)
          Whether to do a lazy prediction (and return Delayed objects) or an
          eagerly executed one.
  
      Returns
      -------
      y : Dask cuDF dataframe  or CuPy backed Dask Array (n_rows, 1)
      """
      return self._predict(X, delayed=delayed)
  
file_path: cuml/python/cuml/dask/solvers/cd.py
imports: from cuml.dask.common.base import BaseEstimator
  from cuml.dask.common.base import DelayedPredictionMixin
  from cuml.dask.common.base import mnmg_import
  from cuml.dask.common.base import SyncFitMixinLinearModel
  from raft_dask.common.comms import get_raft_comm_state
  from dask.distributed import get_worker
label_desc: Use image node_id 3 for calling the CD obj's underlying member method code with example usage: obj.predict(X, delayed) and returns: self