class_docstr: Orthogonal Matching Pursuit model (OMP).
  
  Read more in the :ref:`User Guide <omp>`.
  
  Parameters
  ----------
  n_nonzero_coefs : int, default=None
      Desired number of non-zero entries in the solution. If None (by
      default) this value is set to 10% of n_features.
  
  tol : float, default=None
      Maximum squared norm of the residual. If not None, overrides n_nonzero_coefs.
  
  fit_intercept : bool, default=True
      Whether to calculate the intercept for this model. If set
      to false, no intercept will be used in calculations
      (i.e. data is expected to be centered).
  
  normalize : bool, default=False
      This parameter is ignored when ``fit_intercept`` is set to False.
      If True, the regressors X will be normalized before regression by
      subtracting the mean and dividing by the l2-norm.
      If you wish to standardize, please use
      :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``
      on an estimator with ``normalize=False``.
  
      .. versionchanged:: 1.2
         default changed from True to False in 1.2.
  
      .. deprecated:: 1.2
          ``normalize`` was deprecated in version 1.2 and will be removed in 1.4.
  
  precompute : 'auto' or bool, default='auto'
      Whether to use a precomputed Gram and Xy matrix to speed up
      calculations. Improves performance when :term:`n_targets` or
      :term:`n_samples` is very large. Note that if you already have such
      matrices, you can pass them directly to the fit method.
  
  Attributes
  ----------
  coef_ : ndarray of shape (n_features,) or (n_targets, n_features)
      Parameter vector (w in the formula).
  
  intercept_ : float or ndarray of shape (n_targets,)
      Independent term in decision function.
  
  n_iter_ : int or array-like
      Number of active features across every target.
  
  n_nonzero_coefs_ : int
      The number of non-zero coefficients in the solution. If
      `n_nonzero_coefs` is None and `tol` is None this value is either set
      to 10% of `n_features` or 1, whichever is greater.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems.
  orthogonal_mp_gram :  Solves n_targets Orthogonal Matching Pursuit
      problems using only the Gram matrix X.T * X and the product X.T * y.
  lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm.
  Lars : Least Angle Regression model a.k.a. LAR.
  LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
  sklearn.decomposition.sparse_encode : Generic sparse coding.
      Each column of the result is the solution to a Lasso problem.
  OrthogonalMatchingPursuitCV : Cross-validated
      Orthogonal Matching Pursuit model (OMP).
  
  Notes
  -----
  Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang,
  Matching pursuits with time-frequency dictionaries, IEEE Transactions on
  Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415.
  (https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf)
  
  This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad,
  M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
  Matching Pursuit Technical Report - CS Technion, April 2008.
  https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
  
  Examples
  --------
  >>> from sklearn.linear_model import OrthogonalMatchingPursuit
  >>> from sklearn.datasets import make_regression
  >>> X, y = make_regression(noise=4, random_state=0)
  >>> reg = OrthogonalMatchingPursuit().fit(X, y)
  >>> reg.score(X, y)
  0.9991...
  >>> reg.predict(X[:1,])
  array([-78.3854...])
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docstr: Fit the model using X, y as training data.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      Training data.
  
  y : array-like of shape (n_samples,) or (n_samples, n_targets)
      Target values. Will be cast to X's dtype if necessary.
  
  Returns
  -------
  self : object
      Returns an instance of self.
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code: def fit(self, X, y):
      """Fit the model using X, y as training data.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Training data.
  
      y : array-like of shape (n_samples,) or (n_samples, n_targets)
          Target values. Will be cast to X's dtype if necessary.
  
      Returns
      -------
      self : object
          Returns an instance of self.
      """
      _normalize = _deprecate_normalize(
          self.normalize, estimator_name=self.__class__.__name__
      )
  
      X, y = self._validate_data(
          X, y, multi_output=True, y_numeric=True
      )
      n_features = X.shape[1]
  
      X, y, X_offset, y_offset, X_scale, Gram, Xy = _pre_fit(
          X,
          y,
          None,
          self.precompute,
          _normalize,
          self.fit_intercept,
          copy=True,
      )
  
      if y.ndim == 1:
          y = y[:, np.newaxis]
  
      if self.n_nonzero_coefs is None and self.tol is None:
          # default for n_nonzero_coefs is 0.1 * n_features
          # but at least one.
          self.n_nonzero_coefs_ = max(int(0.1 * n_features), 1)
      else:
          self.n_nonzero_coefs_ = self.n_nonzero_coefs
  
      if Gram is False:
          coef_, self.n_iter_ = orthogonal_mp(
              X,
              y,
              n_nonzero_coefs=self.n_nonzero_coefs_,
              tol=self.tol,
              precompute=False,
              copy_X=True,
              return_n_iter=True,
          )
      else:
          norms_sq = (
              np.sum(y**2, axis=0) if self.tol is not None else None
          )
  
          coef_, self.n_iter_ = orthogonal_mp_gram(
              Gram,
              Xy=Xy,
              n_nonzero_coefs=self.n_nonzero_coefs_,
              tol=self.tol,
              norms_squared=norms_sq,
              copy_Gram=True,
              copy_Xy=True,
              return_n_iter=True,
          )
      self.coef_ = coef_.T
      self._set_intercept(X_offset, y_offset, X_scale)
      return self
  
imports: import warnings
  from math import sqrt
  from numbers import Integral, Real
  import numpy
  from scipy import linalg
  from scipy.linalg.lapack import get_lapack_funcs
  from ..base import MultiOutputMixin, RegressorMixin, _fit_context
  from ..model_selection import check_cv
  from ..utils import as_float_array, check_array
  from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
  from ..utils.parallel import Parallel, delayed
  from ._base import LinearModel, _deprecate_normalize, _pre_fit
label_desc: Use image node_id 2 for calling the OrthogonalMatchingPursuit obj's underlying member method code with example usage: obj.fit(X, y) and returns: self