class_docstr: Class that performs the reading of the csv_input and select the lines
  according to the subject id if available
class_docstr_tok: ["Class","that","performs","the","reading","of","the","csv_input","and","select","the","lines","according","to","the","subject","id","if","available"]
docstr: Tile the csv_read to have the same value applied to the nsamples
  extracted from the volume
  :param data: csv data to tile for all samples extracted in the volume
  :return: tiled data
docstr_tok: ["Tile","the","csv_read","to","have","the","same","value","applied","to","the","nsamples","extracted","from","the","volume",":","param","data",":","csv","data","to","tile","for","all","samples","extracted","in","the","volume",":","return",":","tiled","data"]
code: def tile_nsamples(self, data):
      """
      Tile the csv_read to have the same value applied to the nsamples
      extracted from the volume
      :param data: csv data to tile for all samples extracted in the volume
      :return: tiled data
      """
      if self.n_samples_per_id > 1:
          print("preparing tiling")
          data = np.expand_dims(data, 1)
          data = np.tile(
              data,
              np.asarray(
                  np.concatenate(
                      (
                          [self.n_samples_per_id],
                          [
                              1,
                          ]
                          * (len(np.asarray(data.shape))),
                      )
                  ),
                  dtype=np.int,
              ),
          )
          print("tiling done", data.shape)
          return data
  
      else:
          return data
  
imports: import random
  import numpy
  import pandas
  import tensorflow
  from tensorflow.python.data.util import nest
  from niftynet.layer.base_layer import Layer
  from niftynet.io.image_reader import param_to_dict
label_desc: Use image node_id 7 for calling the CSVReader obj's underlying member method code with example usage: obj.tile_nsamples(data) and returns: data, data