docstr: Allow us to catch known and valid exceptions for all evaluate
  scripts.
docstr_tok: ["Allow","us","to","catch","known","and","valid","exceptions","for","all","evaluate","scripts","."]
code: def __fit(self, function_handle):
      """Allow us to catch known and valid exceptions for all evaluate
      scripts."""
      try:
          function_handle()
          return True
      except KeyError as e:
          if (
              "Floating-point under-/overflow occurred at epoch"
              in e.args[0]
              or "removed all features" in e.args[0]
              or "failed to create intent" in e.args[0]
          ):
              pass
          else:
              traceback.print_exc()
              raise e
      except ValueError as e:
          if (
              "Floating-point under-/overflow occurred at epoch"
              in e.args[0]
              or "removed all features" in e.args[0]
              or "failed to create intent" in e.args[0]
          ):
              pass
          else:
              raise e
      except LinAlgError as e:
          if "not positive definite, even with jitter" in e.args[0]:
              pass
          else:
              raise e
      except RuntimeWarning as e:
          if "invalid value encountered in sqrt" in e.args[0]:
              pass
          elif "divide by zero encountered in divide" in e.args[0]:
              pass
          else:
              raise e
      except UserWarning as e:
          if "FastICA did not converge" in e.args[0]:
              pass
          else:
              raise e
  
imports: import functools
  import tempfile
  import traceback
  import numpy
  import sklearn.datasets
  import sklearn.model_selection
  from numpy.linalg import LinAlgError
  from sklearn import preprocessing
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.constants import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, REGRESSION
  from autosklearn.data.xy_data_manager import XYDataManager
  from autosklearn.metrics import accuracy, balanced_accuracy, f1_macro, f1_micro, f1_weighted, log_loss, precision_macro, precision_micro, precision_weighted, recall_macro, recall_micro, recall_weighted
  from autosklearn.pipeline.util import get_dataset
  from autosklearn.util.data import convert_to_bin
  import unittest
label_desc: Use image node_id 4 for calling the BaseEvaluatorTest obj's underlying member method code with example usage: obj.__fit(function_handle) and returns: True