class_docstr: Base class from SparseCoder and DictionaryLearning algorithms.
class_docstr_tok: ["Base","class","from","SparseCoder","and","DictionaryLearning","algorithms","."]
code: def __init__(
      self,
      transform_algorithm,
      transform_n_nonzero_coefs,
      transform_alpha,
      split_sign,
      n_jobs,
      positive_code,
      transform_max_iter,
  ):
      self.transform_algorithm = transform_algorithm
      self.transform_n_nonzero_coefs = transform_n_nonzero_coefs
      self.transform_alpha = transform_alpha
      self.transform_max_iter = transform_max_iter
      self.split_sign = split_sign
      self.n_jobs = n_jobs
      self.positive_code = positive_code
  
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 1 to create a new _BaseSparseCoding object from inherited base classes: ClassNamePrefixFeaturesOutMixin, TransformerMixin with example: obj = _BaseSparseCoding(transform_algorithm, transform_n_nonzero_coefs, transform_alpha, split_sign, n_jobs, positive_code, transform_max_iter)