code: def _omp_path_residues(
      X_train,
      y_train,
      X_test,
      y_test,
      copy=True,
      fit_intercept=True,
      max_iter=100,
  ):
      """Compute the residues on left-out data for a full LARS path.
  
      Parameters
      ----------
      X_train : ndarray of shape (n_samples, n_features)
          The data to fit the LARS on.
  
      y_train : ndarray of shape (n_samples)
          The target variable to fit LARS on.
  
      X_test : ndarray of shape (n_samples, n_features)
          The data to compute the residues on.
  
      y_test : ndarray of shape (n_samples)
          The target variable to compute the residues on.
  
      copy : bool, default=True
          Whether X_train, X_test, y_train and y_test should be copied.  If
          False, they may be overwritten.
  
      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).
  
      max_iter : int, default=100
          Maximum numbers of iterations to perform, therefore maximum features
          to include. 100 by default.
  
      Returns
      -------
      residues : ndarray of shape (n_samples, max_features)
          Residues of the prediction on the test data.
      """
  
      if copy:
          X_train = X_train.copy()
          y_train = y_train.copy()
          X_test = X_test.copy()
          y_test = y_test.copy()
  
      if fit_intercept:
          X_mean = X_train.mean(axis=0)
          X_train -= X_mean
          X_test -= X_mean
          y_mean = y_train.mean(axis=0)
          y_train = as_float_array(y_train, copy=False)
          y_train -= y_mean
          y_test = as_float_array(y_test, copy=False)
          y_test -= y_mean
  
      coefs = orthogonal_mp(
          X_train,
          y_train,
          n_nonzero_coefs=max_iter,
          tol=None,
          precompute=False,
          copy_X=False,
          return_path=True,
      )
      if coefs.ndim == 1:
          coefs = coefs[:, np.newaxis]
  
      return np.dot(coefs.T, X_test.T) - y_test
  
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 Bunch, as_float_array, check_array
  from ..utils._param_validation import Interval, StrOptions, validate_params
  from ..utils.metadata_routing import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing
  from ..utils.parallel import Parallel, delayed
  from ._base import LinearModel, _pre_fit
label_desc: Use image node_id 5 for calling a global function with example usage: _omp_path_residues(X_train, y_train, X_test, y_test, copy, fit_intercept, max_iter) and returns: unknown