class_docstr: Base class from SparseCoder and DictionaryLearning algorithms.
class_docstr_tok: ["Base","class","from","SparseCoder","and","DictionaryLearning","algorithms","."]
docstr: Encode the data as a sparse combination of the dictionary atoms.
  
  Coding method is determined by the object parameter
  `transform_algorithm`.
  
  Parameters
  ----------
  X : ndarray of shape (n_samples, n_features)
      Test data to be transformed, must have the same number of
      features as the data used to train the model.
  
  Returns
  -------
  X_new : ndarray of shape (n_samples, n_components)
      Transformed data.
code: def transform(self, X):
      """Encode the data as a sparse combination of the dictionary atoms.
  
      Coding method is determined by the object parameter
      `transform_algorithm`.
  
      Parameters
      ----------
      X : ndarray of shape (n_samples, n_features)
          Test data to be transformed, must have the same number of
          features as the data used to train the model.
  
      Returns
      -------
      X_new : ndarray of shape (n_samples, n_components)
          Transformed data.
      """
      check_is_fitted(self)
      return self._transform(X, self.components_)
  
imports: import itertools
  import sys
  import time
  import warnings
  from math import ceil
  from numbers import Integral, Real
  import numpy
  from joblib import effective_n_jobs
  from scipy import linalg
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
  from ..linear_model import Lars, Lasso, LassoLars, orthogonal_mp_gram
  from ..utils import check_array, check_random_state, gen_batches, gen_even_slices
  from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
  from ..utils.extmath import randomized_svd, row_norms, svd_flip
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 3 for calling the _BaseSparseCoding obj's underlying member method code with example usage: obj.transform(X) and returns: self