code: def sparse_encode(
      X,
      dictionary,
      gram=None,
      cov=None,
      algorithm="lasso_lars",
      n_nonzero_coefs=None,
      alpha=None,
      copy_cov=True,
      init=None,
      max_iter=1000,
      n_jobs=1,
      check_input=True,
      verbose=0,
  ):
      """Sparse coding
  
      Each row of the result is the solution to a sparse coding problem.
      The goal is to find a sparse array `code` such that::
  
          X ~= code * dictionary
  
      Read more in the :ref:`User Guide <SparseCoder>`.
  
      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 for meaningful
          output.
  
      gram: array, shape=(n_components, n_components)
          Precomputed Gram matrix, dictionary * dictionary'
  
      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 alpha from
          the projection dictionary * X'
  
      n_nonzero_coefs: int, 0.1 * n_features by default
          Number of nonzero coefficients to target in each column of the
          solution. This is only used by `algorithm='lars'` and `algorithm='omp'`
          and is overridden by `alpha` in the `omp` case.
  
      alpha: float, 1. by default
          If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the
          penalty applied to the L1 norm.
          If `algorithm='threshold'`, `alpha` is the absolute value of the
          threshold below which coefficients will be squashed to zero.
          If `algorithm='omp'`, `alpha` is the tolerance parameter: the value of
          the reconstruction error targeted. In this case, it overrides
          `n_nonzero_coefs`.
  
      init: array of shape (n_samples, n_components)
          Initialization value of the sparse codes. 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.
  
      n_jobs: int, optional
          Number of parallel jobs to run.
  
      check_input: boolean, optional
          If False, the input arrays X and dictionary will not be checked.
  
      verbose : int, optional
          Controls the verbosity; the higher, the more messages. Defaults to 0.
  
      Returns
      -------
      code: array of shape (n_samples, n_components)
          The sparse codes
  
      See also
      --------
      sklearn.linear_model.lars_path
      sklearn.linear_model.orthogonal_mp
      sklearn.linear_model.Lasso
      SparseCoder
      """
      if check_input:
          if algorithm == "lasso_cd":
              dictionary = check_array(
                  dictionary, order="C", dtype="float64"
              )
              X = check_array(X, order="C", dtype="float64")
          else:
              dictionary = check_array(dictionary)
              X = check_array(X)
  
      n_samples, n_features = X.shape
      n_components = dictionary.shape[0]
  
      if gram is None and algorithm != "threshold":
          gram = np.dot(dictionary, dictionary.T)
  
      if cov is None and algorithm != "lasso_cd":
          copy_cov = False
          cov = np.dot(dictionary, X.T)
  
      if algorithm in ("lars", "omp"):
          regularization = n_nonzero_coefs
          if regularization is None:
              regularization = min(
                  max(n_features / 10, 1), n_components
              )
      else:
          regularization = alpha
          if regularization is None:
              regularization = 1.0
  
      if n_jobs == 1 or algorithm == "threshold":
          code = _sparse_encode(
              X,
              dictionary,
              gram,
              cov=cov,
              algorithm=algorithm,
              regularization=regularization,
              copy_cov=copy_cov,
              init=init,
              max_iter=max_iter,
              check_input=False,
              verbose=verbose,
          )
          # This ensure that dimensionality of code is always 2,
          # consistant with the case n_jobs > 1
          if code.ndim == 1:
              code = code[np.newaxis, :]
          return code
  
      # Enter parallel code block
      code = np.empty((n_samples, n_components))
      slices = list(gen_even_slices(n_samples, _get_n_jobs(n_jobs)))
  
      code_views = Parallel(n_jobs=n_jobs, verbose=verbose)(
          delayed(_sparse_encode)(
              X[this_slice],
              dictionary,
              gram,
              cov[:, this_slice] if cov is not None else None,
              algorithm,
              regularization=regularization,
              copy_cov=copy_cov,
              init=init[this_slice] if init is not None else None,
              max_iter=max_iter,
              check_input=False,
          )
          for this_slice in slices
      )
      for this_slice, this_view in zip(slices, code_views):
          code[this_slice] = this_view
      return 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 2 for calling a global function with example usage: sparse_encode(X, dictionary, gram, cov, algorithm, n_nonzero_coefs, alpha, copy_cov, init, max_iter, n_jobs, check_input, verbose) and returns: code, code