docstr: Perform dimensionality reduction on X.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
       Data to be transformed.
  
  y : Ignored
  
  Returns
  -------
  X_new : array, shape (n_samples, n_components)
      Reduced version of X. This will always be a dense array, of the
      same type as the input array. If ``X`` was a ``dask.array``, then
      ``X_new`` will be a ``dask.array`` with the same chunks along the
      first dimension.
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code: def transform(self, X, y=None):
      """Perform dimensionality reduction on X.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
           Data to be transformed.
  
      y : Ignored
  
      Returns
      -------
      X_new : array, shape (n_samples, n_components)
          Reduced version of X. This will always be a dense array, of the
          same type as the input array. If ``X`` was a ``dask.array``, then
          ``X_new`` will be a ``dask.array`` with the same chunks along the
          first dimension.
      """
      return X.dot(self.components_.T)
  
file_path: dask-ml/dask_ml/decomposition/truncated_svd.py
imports: import dask.array
  from dask import compute
  from sklearn.base import BaseEstimator, TransformerMixin
  from .._compat import DASK_2_26_0
  from ..utils import svd_flip
label_desc: Use image node_id 5 for calling the TruncatedSVD obj's underlying member method code with example usage: obj.transform(X, y) and returns: X