ResnetにDnCNNを入れたNetworkのTypeErrorについて

ResnetにDnCNNをいれてノイズ(落書き)を消そうとしているのですが、エラーを解決できません。
該当箇所に関しては参考にしたDnCNNと同じ書き方です。
modelまでは表示できるのですがその先に進めません。

import keras
from tensorflow.keras.models import load_model
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, BatchNormalization, Activation
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras.layers import Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Concatenate
import os
import pickle
import numpy as np

batch_size = 32
#num_classes = 10
epochs = 10
saveDir = "/home/script/savex/"

class Data():
    def __init__(self):
        self.input_img = (32, 32, 3)


    def get_batch(self):
        (x_train, y_train), (x_test, y_test) = cifar10.load_data()
        x_train_line = np.copy(x_train)
        x_test_line = np.copy(x_test)
        print(x_train_line)
        x_train_line = self.drawLines(x_train_line)
        x_test_line = self.drawLines(x_test_line)

        # normalize data
        x_train = x_train.astype('float32')
        x_test = x_test.astype('float32')
        x_train /= 255
        x_test /= 255

        # normalize data
        x_train_line = x_train_line.astype('float32')
        x_test_line = x_test_line.astype('float32')
        x_train_line /= 255
        x_test_line /= 255
        print('x_train shape:', x_train.shape)
        print(x_train.shape[0], 'train samples')
        print(x_test.shape[0], 'test samples')

    def drawUpLeft(x,i,col):
        vh = np.random.randint(2)
        start = np.random.randint(10)
        length = start + np.random.randint(4,21)
        position = np.random.randint(32)
        for j in range(3):
            if vh == 0:
                x[i,:,:,j][start:length, position] = col[j]
            else:
                x[i,:,:,j][position, start:length] = col[j]
        return x

    def drawDnRight(x,i,col):
        vh = np.random.randint(2)
        start = np.random.randint(10,25)
        length = start + np.random.randint(5,32-start)
        position = np.random.randint(32)
        for j in range(3):
            if vh == 0:
                x[i,:,:,j][start:length, position] = col[j]
            else:
                x[i,:,:,j][position, start:length] = col[j]
        return x

    def drawLines(x):
        for i in range(len(x)):
            numLines = np.random.randint(25) + 1
            for l in range(numLines):
                col = [np.random.randint(256),np.random.randint(256),np.random.randint(256)]
                if l % 2 == 0:
                    drawUpLeft(x,i,col)
                else:
                    drawDnRight(x,i,col)
        return x




def network(input_shape):
    inputs = Input(shape=input_shape)
    x = Conv2D(32, kernel_size=3, padding="same", activation="relu")(inputs)
    x = BatchNormalization()(x)
    for i in range(17):
        shutcut = x #ショートカットコネクション用にモジュールの入力データを取得する
        x = Conv2D(64, (3, 3), padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = Dropout(rate=0.3)(x)
        x = Concatenate()([x, shutcut]) #ショートカットコネクション
    x = Conv2D(3, (3, 3), padding='same')(x)
    output_img = Activation('tanh')(x)

    model = Model(inputs,output_img)
    print(model.summary())
    return model

# モデルを学習させるクラス
class Trainer():
  # モデルをコンパイルして、学習するための設定をプライベートプロパティに設定する。
  def __init__(self, model, loss, optimizer):
    self._model = model
    self._model.compile(
        loss=loss,
        optimizer=optimizer,
        metrics=["accuracy"]
    )
    self._verbose = 1
    self._batch_size = 128
    self._epochs = 100

      # 実際の学習
  def fit(self, x_train, y_train, x_test, y_test):
    chkpt = saveDir + 'Cifar10_Resnet_weights.{epoch:02d}-{loss:.2f}-{val_loss:.2f}.hdf5'
    cp_cb = ModelCheckpoint(filepath = chkpt, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')

    self._model.fit(
        x_train,
        x_train_line,
        batch_size=self._batch_size,
        epochs=self._epochs,
        verbose=self._verbose,
        validation_data=(x_test_line, x_test),
        callbacks=[cp_cb],
        shuffle=True
    )
    score = model.evaluate(x_test_line, x_test, verbose=1)
    print(score)
    return self._model

dataset = Data() # データを取得するためのCIFAR10Datasetのインスタンス化
model = network(dataset.input_img) #モデルの取得

x_train, y_train, x_test, y_test = dataset.get_batch()  # 学習データとテストデータの取得
trainer = Trainer(model, loss="mean_squared_error", optimizer="adam") # モデルとロス関数、最適化アルゴリズムを引数にして、Trainerのインスタンス化
model = trainer.fit(x_train, y_train, x_test, y_test) # モデルの学習

# モデルの評価
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss: ', score[0])
print('Test accuracy: ', score[1])

c10test = model.predict(x_test_line)

# definition to show original image and reconstructed image
def showOrigDec(orig, noise, denoise, num=10):
    import matplotlib.pyplot as plt
    n = num
    plt.figure(figsize=(20, 6))

    for i in range(n):
        # display original
        ax = plt.subplot(3, n, i+1)
        plt.imshow(orig[i].reshape(32, 32, 3))
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)

        # display noisy image
        ax = plt.subplot(3, n, i +1 + n)
        plt.imshow(noise[i].reshape(32, 32, 3))
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)

        # display denoised image
        ax = plt.subplot(3, n, i +1 + n + n)
        plt.imshow(denoise[i].reshape(32, 32, 3))
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)
    plt.show()

showOrigDec(x_test, x_test_line, c10test)

ERROR
1Traceback (most recent call last):
2  File "DnResnet.py", line 139, in <module>
3    x_train, y_train, x_test, y_test = dataset.get_batch()  # 学習データとテストデータの取得
4  File "DnResnet.py", line 29, in get_batch
5    x_train_line = self.drawLines(x_train_line)
6TypeError: drawLines() takes 1 positional argument but 2 were given
7