class_docstr: Base class from SparseCoder and DictionaryLearning algorithms.
docstr: Private method allowing to accommodate both DictionaryLearning and
  SparseCoder.
docstr_tok: ["Private","method","allowing","to","accommodate","both","DictionaryLearning","and","SparseCoder","."]
code: def _transform(self, X, dictionary):
      """Private method allowing to accommodate both DictionaryLearning and
      SparseCoder."""
      X = self._validate_data(X, reset=False)
  
      if hasattr(self, "alpha") and self.transform_alpha is None:
          transform_alpha = self.alpha
      else:
          transform_alpha = self.transform_alpha
  
      code = sparse_encode(
          X,
          dictionary,
          algorithm=self.transform_algorithm,
          n_nonzero_coefs=self.transform_n_nonzero_coefs,
          alpha=transform_alpha,
          max_iter=self.transform_max_iter,
          n_jobs=self.n_jobs,
          positive=self.positive_code,
      )
  
      if self.split_sign:
          # feature vector is split into a positive and negative side
          n_samples, n_features = code.shape
          split_code = np.empty((n_samples, 2 * n_features))
          split_code[:, :n_features] = np.maximum(code, 0)
          split_code[:, n_features:] = -np.minimum(code, 0)
          code = split_code
  
      return code
  
imports: import itertools
  import sys
  import time
  from numbers import Integral, Real
  from warnings import warn
  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 2 for calling the _BaseSparseCoding obj's underlying member method code with example usage: obj._transform(X, dictionary) and returns: code