docstr: Transform input `X`.
  
  Note: all imputations should have a `fit_transform` method,
  but only some (like IterativeImputer in sklearn) also support inductive
  mode using `fit` or `fit_transform` on `X_train` and then `transform`
  on new `X_test`.
docstr_tok: ["Transform","input","`","X","`",".","Note",":","all","imputations","should","have","a","`","fit_transform","`","method",",","but","only","some","(","like","IterativeImputer","in","sklearn",")","also","support","inductive","mode","using","`","fit","`","or","`","fit_transform","`","on","`","X_train","`","and","then","`","transform","`","on","new","`","X_test","`","."]
code: def transform(self, X, y=None):
      """
      Transform input `X`.
  
      Note: all imputations should have a `fit_transform` method,
      but only some (like IterativeImputer in sklearn) also support inductive
      mode using `fit` or `fit_transform` on `X_train` and then `transform`
      on new `X_test`.
      """
      raise ValueError(
          "%s.transform not implemented! This imputation algorithm likely "
          "doesn't support inductive mode. Only %s.fit_transform is "
          "supported at this time."
          % (self.__class__.__name__, self.__class__.__name__)
      )
  
file_path: fancyimpute/fancyimpute/solver.py
imports: import warnings
  import numpy
  from sklearn.utils import check_array
  from .common import generate_random_column_samples
label_desc: Use image node_id 14 for calling the Solver obj's underlying member method code with example usage: obj.transform(X, y) without return types