system: You are an expert security code reviewer. You will be shown a source code file along with a multi-select question listing several CWE (Common Weakness Enumeration) categories. Your task is to identify which of the listed CWEs are present in the code.

OUTPUT FORMAT: Respond with the letter(s) of all correct options, sorted alphabetically and comma-separated, wrapped in a single \boxed{...}. Examples: `\boxed{A}` for one correct, `\boxed{A,C}` for two, `\boxed{A,C,E}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
#!/usr/bin/env python
# coding: utf-8

# # Predict the effect of Genetic Variants

# In[1]:


from __future__ import print_function
import os
import re
import tqdm
import string
import pandas as pd
import numpy as np
import keras


# ## 1. Loading Data

# In[2]:


""" Read Data """
train_variant = pd.read_csv("../input/training_variants")
test_variant = pd.read_csv("../input/test_variants")
train_text = pd.read_csv("../input/training_text", sep="\|\|", engine='python', header=None, skiprows=1, names=["ID","Text"])
test_text = pd.read_csv("../input/test_text", sep="\|\|", engine='python', header=None, skiprows=1, names=["ID","Text"])
train = pd.merge(train_variant, train_text, how='left', on='ID')
train_y = train['Class'].values
train_x = train.drop('Class', axis=1)
train_size=len(train_x)
print('Number of training variants: %d' % (train_size))
# number of train data : 3321

test_x = pd.merge(test_variant, test_text, how='left', on='ID')
test_size=len(test_x)
print('Number of test variants: %d' % (test_size))
# number of test data : 5668

test_index = test_x['ID'].values
all_data = np.concatenate((train_x, test_x), axis=0)
all_data = pd.DataFrame(all_data)
all_data.columns = ["ID", "Gene", "Variation", "Text"]


# In[3]:


all_data.head()


# ## 2. Data Preprocessing

# In[4]:


from nltk.corpus import stopwords
from gensim.models.doc2vec import LabeledSentence
from gensim import utils

def constructLabeledSentences(data):
    sentences=[]
    for index, row in data.iteritems():
        sentences.append(LabeledSentence(utils.to_unicode(row).split(), ['Text' + '_%s' % str(index)]))
    return sentences

def textClean(text):
    text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ", text)
    text = text.lower().split()
    stops = set(stopwords.words("english"))
    text = [w for w in text if not w in stops]    
    text = " ".join(text)
    return(text)
    
def cleanup(text):
    text = textClean(text)
    text= text.translate(str.maketrans("","", string.punctuation))
    return text

allText = all_data['Text'].apply(cleanup)
sentences = constructLabeledSentences(allText)
allText.head()


# In[5]:


sentences[0]


# ## 3. Data Preparation and Features Extraction

# ### 3.1 Text Featurizer using Doc2Vec

# Training Doc2Vec with your data or import a saved file

# In[6]:


from gensim.models import Doc2Vec

Text_INPUT_DIM=300


text_model=None
filename='docEmbeddings_5_clean.d2v'
if os.path.isfile(filename):
    text_model = Doc2Vec.load(filename)
else:
    text_model = Doc2Vec(min_count=1, window=5, size=Text_INPUT_DIM, sample=1e-4, negative=5, workers=4, iter=5,seed=1)
    text_model.build_vocab(sentences)
    text_model.train(sentences, total_examples=text_model.corpus_count, epochs=text_model.iter)
    text_model.save(filename)


# Featurize text for your training and testing dataset 

# In[7]:


text_train_arrays = np.zeros((train_size, Text_INPUT_DIM))
text_test_arrays = np.zeros((test_size, Text_INPUT_DIM))

for i in range(train_size):
    text_train_arrays[i] = text_model.docvecs['Text_'+str(i)]

j=0
for i in range(train_size,train_size+test_size):
    text_test_arrays[j] = text_model.docvecs['Text_'+str(i)]
    j=j+1
    
print(text_train_arrays[0][:50])


# ### 3.2 Gene and Varation Featurizer

# In[8]:


from sklearn.decomposition import TruncatedSVD
Gene_INPUT_DIM=25

svd = TruncatedSVD(n_components=25, n_iter=Gene_INPUT_DIM, random_state=12)

one_hot_gene = pd.get_dummies(all_data['Gene'])
truncated_one_hot_gene = svd.fit_transform(one_hot_gene.values)

one_hot_variation = pd.get_dummies(all_data['Variation'])
truncated_one_hot_variation = svd.fit_transform(one_hot_variation.values)


# ### 3.3 Output class encoding

# In[9]:


from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder

label_encoder = LabelEncoder()
label_encoder.fit(train_y)
encoded_y = np_utils.to_categorical((label_encoder.transform(train_y)))
print(encoded_y[0])


# ### 3.4 Merge Input features

# In[10]:


train_set=np.hstack((truncated_one_hot_gene[:train_size],truncated_one_hot_variation[:train_size],text_train_arrays))
test_set=np.hstack((truncated_one_hot_gene[train_size:],truncated_one_hot_variation[train_size:],text_test_arrays))
print(train_set[0][:50])


# ## 4. Define Keras Model

# In[11]:


from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM, Embedding, Input, RepeatVector
from keras.optimizers import SGD

def baseline_model():
    model = Sequential()
    model.add(Dense(256, input_dim=Text_INPUT_DIM+Gene_INPUT_DIM*2, init='normal', activation='relu'))
    model.add(Dropout(0.3))
    model.add(Dense(256, init='normal', activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(80, init='normal', activation='relu'))
    model.add(Dense(9, init='normal', activation="softmax"))
    
    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)  
    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
    return model


# In[12]:


model = baseline_model()
model.summary()


# ## 5. Training and Evaluating the Model

# Create estimator for training the model

# In[13]:


estimator=model.fit(train_set, encoded_y, validation_split=0.2, epochs=10, batch_size=64)


# Final model accuracy

# In[14]:


print("Training accuracy: %.2f%% / Validation accuracy: %.2f%%" % (100*estimator.history['acc'][-1], 100*estimator.history['val_acc'][-1]))


# In[15]:


import matplotlib.pyplot as plt

# summarize history for accuracy
plt.plot(estimator.history['acc'])
plt.plot(estimator.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper left')
plt.show()

# summarize history for loss
plt.plot(estimator.history['loss'])
plt.plot(estimator.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper left')
plt.show()


# ## 6. Make Predictions

# In[16]:


y_pred = model.predict_proba(test_set)


# Make Submission File

# In[17]:


submission = pd.DataFrame(y_pred)
submission['id'] = test_index
submission.columns = ['class1', 'class2', 'class3', 'class4', 'class5', 'class6', 'class7', 'class8', 'class9', 'id']
submission.to_csv("submission_all.csv",index=False)
submission.head()


# ## 7. Layers Visualization

# In[18]:


from keras import backend as K
import seaborn as sns

layer_of_interest=0
intermediate_tensor_function = K.function([model.layers[0].input],[model.layers[layer_of_interest].output])
intermediate_tensor = intermediate_tensor_function([train_set[0,:].reshape(1,-1)])[0]


# In[19]:


import matplotlib
colors = list(matplotlib.colors.cnames)

intermediates = []
color_intermediates = []
for i in range(len(train_set)):
    output_class = np.argmax(encoded_y[i,:])
    intermediate_tensor = intermediate_tensor_function([train_set[i,:].reshape(1,-1)])[0]
    intermediates.append(intermediate_tensor[0])
    color_intermediates.append(colors[output_class])


# In[20]:


from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, random_state=0)
intermediates_tsne = tsne.fit_transform(intermediates)
plt.figure(figsize=(8, 8))
plt.scatter(x = intermediates_tsne[:,0], y=intermediates_tsne[:,1], color=color_intermediates)
plt.show()


```

Which of the following CWE vulnerability categories are present in this code? Multiple options may be correct.

  A) CWE-943: Improper Neutralization of Special Elements in Data Query Logic
  B) CWE-200: Exposure of Sensitive Information to an Unauthorized Actor
  C) CWE-295: Improper Certificate Validation
  D) CWE-20: Improper Input Validation
  E) CWE-113: Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')
  F) CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Answer: D