class_docstr: Model-Parallel Multi-GPU Linear Regression Model.
docstr: Fit the model with X and y.
  
  Parameters
  ----------
  X : Dask cuDF dataframe  or CuPy backed Dask Array (n_rows, n_features)
      Features for regression
  y : Dask cuDF dataframe  or CuPy backed Dask Array (n_rows, 1)
      Labels (outcome values)
docstr_tok: ["Fit","the","model","with","X","and","y.","Parameters","--","--","--","--","--","X",":","Dask","cuDF","dataframe","or","CuPy","backed","Dask","Array","(","n_rows",",","n_features",")","Features","for","regression","y",":","Dask","cuDF","dataframe","or","CuPy","backed","Dask","Array","(","n_rows",",","1",")","Labels","(","outcome","values",")"]
code: def fit(self, X, y):
      """
      Fit the model with X and y.
  
      Parameters
      ----------
      X : Dask cuDF dataframe  or CuPy backed Dask Array (n_rows, n_features)
          Features for regression
      y : Dask cuDF dataframe  or CuPy backed Dask Array (n_rows, 1)
          Labels (outcome values)
      """
  
      models = self._fit(model_func=CD._create_model, data=(X, y))
  
      self._set_internal_model(list(models.values())[0])
  
      return self
  
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 2 for calling the CD obj's underlying member method code with example usage: obj.fit(X, y) and returns: self