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 estimation of trainable parameters.
  
  All model estimations with scikit-learn, keras, pytorch models as well as some preprocessing
  techniques (normalization) estimate parameters based on data (training data).
  Those parameters are trained during fit execution and are persisted for the future.
  Only the estimation logic, nothing else.
  
  Args:
      args: positional arguments (can be anything)
      kwargs: keyword arguments (can be anything)
  
  Returns:
      BaseTransformer: self object
docstr_tok: ["Performs","estimation","of","trainable","parameters",".","All","model","estimations","with","scikit-learn",",","keras",",","pytorch","models","as","well","as","some","preprocessing","techniques","(","normalization",")","estimate","parameters","based","on","data","(","training","data",")",".","Those","parameters","are","trained","during","fit","execution","and","are","persisted","for","the","future",".","Only","the","estimation","logic",",","nothing","else",".","Args",":","args",":","positional","arguments","(","can","be","anything",")","kwargs",":","keyword","arguments","(","can","be","anything",")","Returns",":","BaseTransformer",":","self","object"]
code: def fit(self, *args, **kwargs):
      """Performs estimation of trainable parameters.
  
      All model estimations with scikit-learn, keras, pytorch models as well as some preprocessing
      techniques (normalization) estimate parameters based on data (training data).
      Those parameters are trained during fit execution and are persisted for the future.
      Only the estimation logic, nothing else.
  
      Args:
          args: positional arguments (can be anything)
          kwargs: keyword arguments (can be anything)
  
      Returns:
          BaseTransformer: self object
      """
      return self
  
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 2 for calling the BaseTransformer obj's underlying member method code with example usage: obj.fit() and returns: self