class_docstr: Abstraction on ``fit`` and ``transform`` execution.
  
  Base transformer is an abstraction strongly inspired by the ``sklearn.Transformer`` and
  ``sklearn.Estimator``. Two main concepts are:
  
      1. Every action that can be performed on data (transformation, model training) can be
      performed in two steps: fitting (where trainable parameters are estimated) and transforming
      (where previously estimated parameters are used to transform the data into desired state).
  
      2. Every transformer knows how it should be persisted and loaded (especially useful when
      working with Keras/Pytorch or scikit-learn) in one pipeline.
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docstr: Performs transformation of data.
  
  All data transformation including prediction with deep learning/machine learning models
  can be performed here. No parameters should be estimated in this method nor stored as
  class attributes. Only the transformation logic, nothing else.
  
  Args:
      args: positional arguments (can be anything)
      kwargs: keyword arguments (can be anything)
  
  Returns:
      dict: output
docstr_tok: ["Performs","transformation","of","data",".","All","data","transformation","including","prediction","with","deep","learning\/machine","learning","models","can","be","performed","here",".","No","parameters","should","be","estimated","in","this","method","nor","stored","as","class","attributes",".","Only","the","transformation","logic",",","nothing","else",".","Args",":","args",":","positional","arguments","(","can","be","anything",")","kwargs",":","keyword","arguments","(","can","be","anything",")","Returns",":","dict",":","output"]
code: def transform(self, *args, **kwargs):
      """Performs transformation of data.
  
      All data transformation including prediction with deep learning/machine learning models
      can be performed here. No parameters should be estimated in this method nor stored as
      class attributes. Only the transformation logic, nothing else.
  
      Args:
          args: positional arguments (can be anything)
          kwargs: keyword arguments (can be anything)
  
      Returns:
          dict: output
      """
      raise NotImplementedError
  
imports: import os
  import pprint
  from collections import defaultdict
  from sklearn.externals import joblib
  from steppy.adapter import Adapter, AdapterError
  from steppy.utils import display_upstream_structure, persist_as_png, get_logger, initialize_logger
label_desc: Use image node_id 3 for calling the BaseTransformer obj's underlying member method code with example usage: obj.transform() without return types