class_docstr: Partial Least Squares (PLS)
  
  This class implements the generic PLS algorithm.
  
  Main ref: Wegelin, a survey of Partial Least Squares (PLS) methods,
  with emphasis on the two-block case
  https://stat.uw.edu/sites/default/files/files/reports/2000/tr371.pdf
class_docstr_tok: ["Partial","Least","Squares","(","PLS",")","This","class","implements","the","generic","PLS","algorithm",".","Main","ref",":","Wegelin",",","a","survey","of","Partial","Least","Squares","(","PLS",")","methods",",","with","emphasis","on","the","two-block","case","https",":","\/\/stat.uw.edu\/sites\/default\/files\/files\/reports\/2000\/tr371.pdf"]
code: def _more_tags(self):
      return {"poor_score": True, "requires_y": False}
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  import numpy
  from scipy.linalg import svd
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, MultiOutputMixin, RegressorMixin, TransformerMixin, _fit_context
  from ..exceptions import ConvergenceWarning
  from ..utils import check_array, check_consistent_length
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.extmath import svd_flip
  from ..utils.fixes import parse_version, sp_version
  from ..utils.validation import FLOAT_DTYPES, check_is_fitted
label_desc: Use image node_id 7 for calling the _PLS obj's underlying member method code with example usage: obj._more_tags() and returns: dict