class_docstr: Base class for all estimators in scikit-learn.
  
  Notes
  -----
  All estimators should specify all the parameters that can be set
  at the class level in their ``__init__`` as explicit keyword
  arguments (no ``*args`` or ``**kwargs``).
class_docstr_tok: ["Base","class","for","all","estimators","in","scikit-learn",".","Notes","--","--","-","All","estimators","should","specify","all","the","parameters","that","can","be","set","at","the","class","level","in","their","``","__init__","``","as","explicit","keyword","arguments","(","no","``","*","args","``","or","``","*","*","kwargs","``",")","."]
docstr: Get parameters for this estimator.
  
  Parameters
  ----------
  deep : bool, optional
      If True, will return the parameters for this estimator and
      contained subobjects that are estimators.
  
  Returns
  -------
  params : dict
      Parameter names mapped to their values.
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code: def get_params(self, deep=True):
      """Get parameters for this estimator.
  
      Parameters
      ----------
      deep : bool, optional
          If True, will return the parameters for this estimator and
          contained subobjects that are estimators.
  
      Returns
      -------
      params : dict
          Parameter names mapped to their values.
      """
      out = dict()
      for key in self._get_param_names():
          # We need deprecation warnings to always be on in order to
          # catch deprecated param values.
          # This is set in utils/__init__.py but it gets overwritten
          # when running under python3 somehow.
          warnings.simplefilter("always", DeprecationWarning)
          try:
              with warnings.catch_warnings(record=True) as w:
                  value = getattr(self, key, None)
              if len(w) and w[0].category is DeprecationWarning:
                  # if the parameter is deprecated, don't show it
                  continue
          finally:
              warnings.filters.pop(0)
  
          # XXX: should we rather test if instance of estimator?
          if deep and hasattr(value, "get_params"):
              deep_items = value.get_params().items()
              out.update((key + "__" + k, val) for k, val in deep_items)
          out[key] = value
      return out
  
imports: import inspect
  import operator
  import os
  import warnings
  from contextlib import contextmanager
  from io import StringIO
  from math import log
  from pprint import pprint
  import numpy
label_desc: Use image node_id 2 for calling the BaseEstimator obj's underlying member method code with example usage: obj.get_params(deep) and returns: out