code: def _sparse_encode(
      X,
      dictionary,
      gram,
      cov=None,
      algorithm="lasso_lars",
      regularization=None,
      copy_cov=True,
      init=None,
      max_iter=1000,
      check_input=True,
      verbose=0,
  ):
      """Generic sparse coding
  
      Each column of the result is the solution to a Lasso problem.
  
      Parameters
      ----------
      X: array of shape (n_samples, n_features)
          Data matrix.
  
      dictionary: array of shape (n_components, n_features)
          The dictionary matrix against which to solve the sparse coding of
          the data. Some of the algorithms assume normalized rows.
  
      gram: None | array, shape=(n_components, n_components)
          Precomputed Gram matrix, dictionary * dictionary'
          gram can be None if method is 'threshold'.
  
      cov: array, shape=(n_components, n_samples)
          Precomputed covariance, dictionary * X'
  
      algorithm: {'lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'}
          lars: uses the least angle regression method (linear_model.lars_path)
          lasso_lars: uses Lars to compute the Lasso solution
          lasso_cd: uses the coordinate descent method to compute the
          Lasso solution (linear_model.Lasso). lasso_lars will be faster if
          the estimated components are sparse.
          omp: uses orthogonal matching pursuit to estimate the sparse solution
          threshold: squashes to zero all coefficients less than regularization
          from the projection dictionary * data'
  
      regularization : int | float
          The regularization parameter. It corresponds to alpha when
          algorithm is 'lasso_lars', 'lasso_cd' or 'threshold'.
          Otherwise it corresponds to n_nonzero_coefs.
  
      init: array of shape (n_samples, n_components)
          Initialization value of the sparse code. Only used if
          `algorithm='lasso_cd'`.
  
      max_iter: int, 1000 by default
          Maximum number of iterations to perform if `algorithm='lasso_cd'`.
  
      copy_cov: boolean, optional
          Whether to copy the precomputed covariance matrix; if False, it may be
          overwritten.
  
      check_input: boolean, optional
          If False, the input arrays X and dictionary will not be checked.
  
      verbose: int
          Controls the verbosity; the higher, the more messages. Defaults to 0.
  
      Returns
      -------
      code: array of shape (n_components, n_features)
          The sparse codes
  
      See also
      --------
      sklearn.linear_model.lars_path
      sklearn.linear_model.orthogonal_mp
      sklearn.linear_model.Lasso
      SparseCoder
      """
      if X.ndim == 1:
          X = X[:, np.newaxis]
      n_samples, n_features = X.shape
      if cov is None and algorithm != "lasso_cd":
          # overwriting cov is safe
          copy_cov = False
          cov = np.dot(dictionary, X.T)
  
      if algorithm == "lasso_lars":
          alpha = (
              float(regularization) / n_features
          )  # account for scaling
          try:
              err_mgt = np.seterr(all="ignore")
  
              # Not passing in verbose=max(0, verbose-1) because Lars.fit already
              # corrects the verbosity level.
              lasso_lars = LassoLars(
                  alpha=alpha,
                  fit_intercept=False,
                  verbose=verbose,
                  normalize=False,
                  precompute=gram,
                  fit_path=False,
              )
              lasso_lars.fit(dictionary.T, X.T, Xy=cov)
              new_code = lasso_lars.coef_
          finally:
              np.seterr(**err_mgt)
  
      elif algorithm == "lasso_cd":
          alpha = (
              float(regularization) / n_features
          )  # account for scaling
  
          # TODO: Make verbosity argument for Lasso?
          # sklearn.linear_model.coordinate_descent.enet_path has a verbosity
          # argument that we could pass in from Lasso.
          clf = Lasso(
              alpha=alpha,
              fit_intercept=False,
              normalize=False,
              precompute=gram,
              max_iter=max_iter,
              warm_start=True,
          )
          clf.coef_ = init
          clf.fit(dictionary.T, X.T, check_input=check_input)
          new_code = clf.coef_
  
      elif algorithm == "lars":
          try:
              err_mgt = np.seterr(all="ignore")
  
              # Not passing in verbose=max(0, verbose-1) because Lars.fit already
              # corrects the verbosity level.
              lars = Lars(
                  fit_intercept=False,
                  verbose=verbose,
                  normalize=False,
                  precompute=gram,
                  n_nonzero_coefs=int(regularization),
                  fit_path=False,
              )
              lars.fit(dictionary.T, X.T, Xy=cov)
              new_code = lars.coef_
          finally:
              np.seterr(**err_mgt)
  
      elif algorithm == "threshold":
          new_code = (
              np.sign(cov) * np.maximum(np.abs(cov) - regularization, 0)
          ).T
  
      elif algorithm == "omp":
          # TODO: Should verbose argument be passed to this?
          new_code = orthogonal_mp_gram(
              Gram=gram,
              Xy=cov,
              n_nonzero_coefs=int(regularization),
              tol=None,
              norms_squared=row_norms(X, squared=True),
              copy_Xy=copy_cov,
          ).T
      else:
          raise ValueError(
              'Sparse coding method must be "lasso_lars" '
              '"lasso_cd",  "lasso", "threshold" or "omp", got %s.'
              % algorithm
          )
      return new_code
  
imports: from __future__ import print_function
  import time
  import sys
  import itertools
  from math import sqrt, ceil
  import numpy
  from scipy import linalg
  from numpy.lib.stride_tricks import as_strided
  from ..base import BaseEstimator, TransformerMixin
  from ..externals.joblib import Parallel, delayed, cpu_count
  from ..externals.six.moves import zip
  from ..utils import check_array, check_random_state, gen_even_slices, gen_batches, _get_n_jobs
  from ..utils.extmath import randomized_svd, row_norms
  from ..utils.validation import check_is_fitted
  from ..linear_model import Lasso, orthogonal_mp_gram, LassoLars, Lars
label_desc: Use image node_id 1 for calling a global function with example usage: _sparse_encode(X, dictionary, gram, cov, algorithm, regularization, copy_cov, init, max_iter, check_input, verbose) and returns: new_code