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 fit followed by transform.
  
  This method simply combines fit and transform.
  
  Args:
      args: positional arguments (can be anything)
      kwargs: keyword arguments (can be anything)
  
  Returns:
      dict: output
docstr_tok: ["Performs","fit","followed","by","transform",".","This","method","simply","combines","fit","and","transform",".","Args",":","args",":","positional","arguments","(","can","be","anything",")","kwargs",":","keyword","arguments","(","can","be","anything",")","Returns",":","dict",":","output"]
code: def fit_transform(self, *args, **kwargs):
      """Performs fit followed by transform.
  
      This method simply combines fit and transform.
  
      Args:
          args: positional arguments (can be anything)
          kwargs: keyword arguments (can be anything)
  
      Returns:
          dict: output
      """
      self.fit(*args, **kwargs)
      return self.transform(*args, **kwargs)
  
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 4 for calling the BaseTransformer obj's underlying member method code with example usage: obj.fit_transform() and returns: self