class_docstr: A numpy version of the :class:`~neuraxle.steps.column_transformer.ColumnSelector2D`.
class_docstr_tok: ["A","numpy","version","of","the",":","class",":","`","~neuraxle.steps.column_transformer.ColumnSelector2D","`","."]
code: def transform(self, data_inputs):
      if isinstance(self.column_selection, range):
          self.column_selection = slice(
              self.column_selection.start,
              self.column_selection.stop,
              self.column_selection.step,
          )
  
      if isinstance(self.column_selection, int):
          return np.expand_dims(
              np.array(data_inputs)[:, self.column_selection], axis=-1
          )
  
      if isinstance(self.column_selection, slice):
          return np.array(data_inputs)[:, self.column_selection]
  
      if isinstance(self.column_selection, list):
          columns = [
              np.expand_dims(np.array(data_inputs)[:, i], axis=-1)
              for i in self.column_selection
          ]
          return np.concatenate(columns, axis=-1)
  
      if self.column_selection is None:
          return data_inputs
  
      raise ValueError(
          "column selection type not supported : {0}\nSupported types".format(
              self.column_selection, repr(ColumnSelectionType)
          )
      )
  
imports: from operator import itemgetter
  from typing import List, Tuple, Union, Iterable
  import numpy
  from neuraxle.base import MetaStep, BaseTransformer
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.loop import ForEach
  from neuraxle.union import FeatureUnion
label_desc: Use image node_id 2 for calling the NumpyColumnSelector2D obj's underlying member method code with example usage: obj.transform(data_inputs) and returns: np, np, np, data_inputs