code: def __init__(
      self,
      prefix="./gkmSVM",
      word_length=11,
      mismatches=3,
      C=1,
      threads=1,
      cache_memory=100,
      verbosity=4,
  ):
      self.word_length = word_length
      self.mismatches = mismatches
      self.C = C
      self.threads = threads
      self.prefix = "_".join(
          map(str, (prefix, word_length, mismatches, C))
      )
      options_list = zip(
          ["-l", "-d", "-c", "-T", "-m", "-v"],
          map(
              str,
              (
                  word_length,
                  mismatches,
                  C,
                  threads,
                  cache_memory,
                  verbosity,
              ),
          ),
      )
      self.options = " ".join(
          [" ".join(option) for option in options_list]
      )
  
imports: from __future__ import absolute_import, division, print_function
  import matplotlib
  import numpy
  import os
  import subprocess
  import sys
  import tempfile
  import matplotlib.pyplot
  from dragonn.metrics import ClassificationResult
  from keras.layers.core import Activation, Dense, Dropout, Flatten, Permute, Reshape, TimeDistributedDense
  from keras.layers.convolutional import Convolution2D, MaxPooling2D
  from keras.layers.recurrent import GRU
  from keras.regularizers import l1
  from keras.layers.core import Activation, Dense, Flatten, TimeDistributedDense
  from keras.layers.recurrent import GRU
  from keras.callbacks import EarlyStopping
label_desc: Use image node_id 1 to create a new gkmSVM object from inherited base classes: Model with example: obj = gkmSVM(prefix, word_length, mismatches, C, threads, cache_memory, verbosity)