class_docstr: Sparse coding
  
  Finds a sparse representation of data against a fixed, precomputed
  dictionary.
  
  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
  ----------
  dictionary : array, [n_components, n_features]
      The dictionary atoms used for sparse coding. Lines are assumed to be
      normalized to unit norm.
  
  transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp',     'threshold'}
      Algorithm used to transform the data:
      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'``
  
  transform_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.
  
  transform_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`.
  
  split_sign : bool, False by default
      Whether to split the sparse feature vector into the concatenation of
      its negative part and its positive part. This can improve the
      performance of downstream classifiers.
  
  n_jobs : int,
      number of parallel jobs to run
  
  Attributes
  ----------
  components_ : array, [n_components, n_features]
      The unchanged dictionary atoms
  
  See also
  --------
  DictionaryLearning
  MiniBatchDictionaryLearning
  SparsePCA
  MiniBatchSparsePCA
  sparse_encode
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docstr: Do nothing and return the estimator unchanged
  
  This method is just there to implement the usual API and hence
  work in pipelines.
docstr_tok: ["Do","nothing","and","return","the","estimator","unchanged","This","method","is","just","there","to","implement","the","usual","API","and","hence","work","in","pipelines","."]
code: def fit(self, X, y=None):
      """Do nothing and return the estimator unchanged
  
      This method is just there to implement the usual API and hence
      work in pipelines.
      """
      return self
  
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 the SparseCoder obj's underlying member method code with example usage: obj.fit(X, y) and returns: self