docstr: Generate (idx, name, trans) tuples from self.steps.
  
  When `filter_passthrough=True`, 'passthrough' and None
  transformers are filtered out. When `filter_train_only=True`,
  the RemoveOutliers and FixImbalancer classes are only used for
  fitting. They are skipped for all prediction methods since we
  want to keep the original distribution of target classes.
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code: def _iter(
      self,
      with_final=True,
      filter_passthrough=True,
      filter_train_only=True,
      reverse=False,
  ):
      """Generate (idx, name, trans) tuples from self.steps.
  
      When `filter_passthrough=True`, 'passthrough' and None
      transformers are filtered out. When `filter_train_only=True`,
      the RemoveOutliers and FixImbalancer classes are only used for
      fitting. They are skipped for all prediction methods since we
      want to keep the original distribution of target classes.
  
      """
      it = super()._iter(with_final, filter_passthrough)
      if reverse:
          it = reversed(list(it))
      if filter_train_only:
          return filter(
              lambda x: not getattr(x[-1], "_train_only", False), it
          )
      else:
          return it
  
imports: import platform
  import warnings
  from copy import deepcopy
  from inspect import signature
  import imblearn.pipeline
  import sklearn.pipeline
  from sklearn.base import clone
  from sklearn.utils import _print_elapsed_time
  from sklearn.utils.metaestimators import available_if
  from sklearn.utils.validation import check_memory
  from pycaret.utils._show_versions import _get_deps_info
  from pycaret.utils.generic import get_all_object_vars_and_properties, variable_return
label_desc: Use image node_id 10 for calling the Pipeline obj's underlying member method code with example usage: obj._iter(with_final, filter_passthrough, filter_train_only, reverse) and returns: filter, it