Can you generate a documentation for below python function?

def cal\_score\_age\_gender(img1, img1\_filename, img2, img2\_filename, facial\_similarity, predict\_age\_and\_gender,
 detect\_face):
 # Similarity check
 response = {}
 technical\_details = []
 img1 = np.asarray(Image.open(img1.stream).convert('RGB'))
 img2 = np.asarray(Image.open(img2.stream).convert('RGB'))

 can\_process\_res = can\_process\_image(img1, img2)

 if facial\_similarity:
 if can\_process\_res['can\_process']==False:
 if can\_process\_res['problem\_with\_image1'] and can\_process\_res['problem\_with\_image2']:
 similarity = "Couldn't predict similarity due to low image quality of {0} and {1}".format(img1\_filename,
 img2\_filename)
 elif can\_process\_res['problem\_with\_image1']:
 similarity = "Couldn't predict similarity due to low image quality of {0}".format(img1\_filename)
 else:
 similarity = "Couldn't predict similarity due to low image quality of {0}".format(img2\_filename)
 else:

 res = DeepFace.verify(img1, img2, detector\_backend=DETECTOR\_BACKEND\_1, prog\_bar=False,
 model\_name=MODEL\_NAME, model=MODEL, distance\_metric=METRIC)

 score = round(res['distance'], 2)

 similarity = distance\_2\_category(score, THRESHOLD[0],THRESHOLD[1])

 response['similarity'] = similarity

 technical\_details.append("Similarity score: '{0}'".format(score))
 if detect\_face | predict\_age\_and\_gender:
 response['image-1'] = {'filename': img1\_filename}
 response['image-2'] = {'filename': img2\_filename}

 if detect\_face:
 if can\_process\_res['problem\_with\_image1'] == False:
 face\_1 = DeepFace.detectFace(img1, target\_size=(240, 240),
 detector\_backend=DETECTOR\_BACKEND\_1) # returns numpy arr in rgb format
 response['image-1']['detected-face'] = encode\_img\_to\_b64(face\_1)
 else:
 response['image-1']['detected-face'] = "Couldn't detect face due to low image quality"

 if can\_process\_res['problem\_with\_image2'] == False:
 face\_2 = DeepFace.detectFace(img2, target\_size=(240, 240),
 detector\_backend=DETECTOR\_BACKEND\_1) # returns numpy arr in rgb format
 response['image-2']['detected-face'] = encode\_img\_to\_b64(face\_2)
 else:
 response['image-2']['detected-face'] = "Couldn't detect face due to low image quality"

 if predict\_age\_and\_gender:
 if can\_process\_res['problem\_with\_image1'] == False:
 analyzer\_1 = DeepFace.analyze(img1, actions=['age', 'gender'], models=age\_and\_gender\_models,
 detector\_backend=DETECTOR\_BACKEND\_1, prog\_bar=False)
 age\_1 = analyzer\_1['age']
 gender\_1 = analyzer\_1['gender']
 response['image-1']['age-gender'] = age\_gender\_fun(age\_1, gender\_1)
 else:
 response['image-1']['age-gender'] = "Couldn't predict age & gender due to low image quality"

 if can\_process\_res['problem\_with\_image2'] == False:
 analyzer\_2 = DeepFace.analyze(img2, actions=['age', 'gender'], models=age\_and\_gender\_models,
 detector\_backend=DETECTOR\_BACKEND\_1, prog\_bar=False)
 age\_2 = analyzer\_2['age']
 gender\_2 = analyzer\_2['gender']
 response['image-2']['age-gender'] = age\_gender\_fun(age\_2, gender\_2)
 else:
 response['image-2']['age-gender'] = "Couldn't predict age & gender due to low image quality"
 technical\_details.append("Similarity score threshold: '{0}'".format(THRESHOLD))
 technical\_details.append("Face recognition model: '{0}'".format(MODEL\_NAME))
 technical\_details.append("Face detector bankend: '{0}'".format(DETECTOR\_BACKEND\_1))
 technical\_details.append("Face detector bankend for filtering low quality image: '{0}'".format(DETECTOR\_BACKEND\_2))
 response['technical-details'] = technical\_details

 return response
아래 파이썬 함수에 대한 문서를 생성할 수 있나요?def cal\_score\_age\_gender(img1, img1\_파일이름, img2, img2\_파일이름, facial\_similarity, predict\_age\_and\_gender, detect\_face): # 유사도 검사 response = {} technical\_details = [] img1 = np.asarray(Image.open(img1.stream).convert('RGB')) img2 = np.asarray(Image.open(img2.stream).convert('RGB')) can\_process\_res = can\_process\_image(img1, img2) if facial\_similarity: if can\_process\_res['can\_process']==False: if can\_process\_res['problem\_with\_image1'] and can\_process\_res['problem\_with\_image2']: 유사성 = "{0} 및 {1}의 이미지 품질이 낮아 유사성을 예측할 수 없습니다.".format(img1\_filename, img2\_파일명) elif can\_process\_res['problem\_with\_image1']: 유사도 = "{0}의 낮은 이미지 품질로 인해 유사도를 예측할 수 없습니다".format(img1\_filename) else: 유사도 = "{0}의 낮은 이미지 품질로 인해 유사도를 예측할 수 없습니다.".format(img2\_filename) else: res = DeepFace.verify(img1, img2, detector\_backend=DETECTOR\_BACKEND\_1, prog\_bar=False, model\_name=MODEL\_NAME, model=MODEL, distance\_metric=METRIC) score = round(res['distance'], 2) 유사도 = distance\_2\_category(score, THRESHOLD[0],THRESHOLD[1]) response['유사도'] = 유사도 technical\_details.append("유사도 점수: '{0}'".format(score)) if detect\_face | predict\_age\_and\_gender: response['image-1'] = {'filename': img1\_filename} response['image-2'] = {'filename': img2\_filename} if detect\_face: can\_process\_res['problem\_with\_image1'] == False: face\_1 = DeepFace.detectFace(img1, target\_size=(240, 240), detector\_backend=DETECTOR\_BACKEND\_1) # RGB 형식의 배열을 반환합니다. response['image-1']['detected-face'] = encode\_img\_to\_b64(face\_1) else: response['image-1']['detected-face'] = "낮은 이미지 품질로 인해 얼굴을 감지할 수 없습니다." if can\_process\_res['problem\_with\_image2'] == False: face\_2 = DeepFace.detectFace(img2, target\_size=(240, 240), detector\_backend=DETECTOR\_BACKEND\_1) # RGB 형식의 배열을 반환합니다. response['image-2']['detected-face'] = encode\_img\_to\_b64(face\_2) else: response['image-2']['detected-face'] = "낮은 이미지 품질로 인해 얼굴을 감지할 수 없습니다." if predict\_age\_and\_gender: if can\_process\_res['problem\_with\_image1'] == False: analyzer\_1 = DeepFace.analyze(img1, actions=['age', 'gender'], models=age\_and\_gender\_models, detector\_backend=DETECTOR\_BACKEND\_1, prog\_bar=False) age\_1 = 분석기\_1['age'] gender\_1 = 분석기\_1['성별'] response['image-1']['age-gender'] = age\_gender\_fun(age\_1, gender\_1) else: response['image-1']['age-gender'] = "낮은 이미지 품질로 인해 나이와 성별을 예측할 수 없습니다." if can\_process\_res['problem\_with\_image2'] == False: analyzer\_2 = DeepFace.analyze(img2, actions=['age', 'gender'], models=age\_and\_gender\_models, detector\_backend=DETECTOR\_BACKEND\_1, prog\_bar=False) age\_2 = 분석기\_2['age'] gender\_2 = 분석기\_2['성별'] response['image-2']['age-gender'] = age\_gender\_fun(age\_2, gender\_2) else: response['image-2']['age-gender'] = "낮은 이미지 품질로 인해 연령 및 성별을 예측할 수 없습니다." technical\_details.append("유사도 점수 임계값: '{0}'".format(THRESHOLD)) technical\_details.append("얼굴 인식 모델: '{0}'".format(MODEL\_NAME)) technical\_details.append("얼굴 검출기 뱅크엔드: '{0}'".format(DETECTOR\_BACKEND\_1)) technical\_details.append("저화질 이미지 필터링을 위한 얼굴 검출기 뱅크엔드: '{0}'".format(DETECTOR\_BACKEND\_2)) response['technical-details'] = technical\_details 응답 반환