delete

Prompt:
Implement the Python class `Review_upload` described below.

Class description:
Implement the Review_upload class.

Method signatures and docstrings:
- def post(self, request): 리뷰 생성 --- # /reviews/ ## headers - Authorization : Token "key 값" ## body parameter - counselor : 상담사 user - client : 내담자 user - content : 후기 내용
- def put(self, request): 리뷰 수정 --- # /reviews/ ## headers - Authorization : Token "key 값" ## body parameter - review_id : 후기 id 값 - content : 후기 내용
- def delete(self, request, **kwargs): 리뷰 삭제 --- # /reviews/<id:int>/ ## headers - Authorization : Token "key 값" [ex> Token 822a24a314dfbc387128d82af6b952191dd71651]

Implement the Python class `Review_upload` described below.

Class description:
Implement the Review_upload class.

Method signatures and docstrings:
- def post(self, request): 리뷰 생성 --- # /reviews/ ## headers - Authorization : Token "key 값" ## body parameter - counselor : 상담사 user - client : 내담자 user - content : 후기 내용
- def put(self, request): 리뷰 수정 --- # /reviews/ ## headers - Authorization : Token "key 값" ## body parameter - review_id : 후기 id 값 - content : 후기 내용
- def delete(self, request, **kwargs): 리뷰 삭제 --- # /reviews/<id:int>/ ## headers - Authorization : Token "key 값" [ex> Token 822a24a314dfbc387128d82af6b952191dd71651]

<|skeleton|>
class Review_upload:

    def post(self, request):
        """리뷰 생성 --- # /reviews/ ## headers - Authorization : Token "key 값" ## body parameter - counselor : 상담사 user - client : 내담자 user - content : 후기 내용"""
        <|body_0|>

    def put(self, request):
        """리뷰 수정 --- # /reviews/ ## headers - Authorization : Token "key 값" ## body parameter - review_id : 후기 id 값 - content : 후기 내용"""
        <|body_1|>

    def delete(self, request, **kwargs):
        """리뷰 삭제 --- # /reviews/<id:int>/ ## headers - Authorization : Token "key 값" [ex> Token 822a24a314dfbc387128d82af6b952191dd71651]"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        selected_counselor_email = request.data.get('counselor')
        counselor = User.objects.get(email=selected_counselor_email)
        content = request.data.get('content')
        review = Review(client=request.user, counselor=counselor, create_date=datetime.now(), content=content)
        serializer = ReviewSerializer(review)
        if serializer.is_valid:
            review.save()
            return Response(serializer.data, status=status.HTTP_201_CREATED)
        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
<|end_body_0|>

<|body_start_1|>
        try:
            selected_review = Review.objects.get(id=request.data.get('review_id'))
        except:
            return Response('Review not exist', status=status.HTTP_400_BAD_REQUEST)
        content = request.data.get('content')
        selected_review.content = content
        try:
            selected_review.save()
        except:
            return Response('Review updated failed', status=status.HTTP_400_BAD_REQUEST)
        return Response('Review updated success', status=status.HTTP_201_CREATED)
<|end_body_1|>

<|body_start_2|>
        if kwargs.get('id') is None:
            return Response('invalid request', status=status.HTTP_400_BAD_REQUEST)
        else:
            review_id = kwargs.get('id')
            try:
                review_obj = Review.objects.get(id=review_id)
                review_obj.delete()
                return Response('Review was deleted', status=status.HTTP_200_OK)
            except:
                return Response('Review not Found', status=status.HTTP_400_BAD_REQUEST)
<|end_body_2|>

Solution:
class Review_upload:
    def post(self, request):
        """리뷰 생성 --- # /reviews/ ## headers - Authorization : Token "key 값" ## body parameter - counselor : 상담사 user - client : 내담자 user - content : 후기 내용"""
        selected_counselor_email = request.data.get('counselor')
        counselor = User.objects.get(email=selected_counselor_email)
        content = request.data.get('content')
        review = Review(client=request.user, counselor=counselor, create_date=datetime.now(), content=content)
        serializer = ReviewSerializer(review)
        if serializer.is_valid:
            review.save()
            return Response(serializer.data, status=status.HTTP_201_CREATED)
        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)

    def put(self, request):
        """리뷰 수정 --- # /reviews/ ## headers - Authorization : Token "key 값" ## body parameter - review_id : 후기 id 값 - content : 후기 내용"""
        try:
            selected_review = Review.objects.get(id=request.data.get('review_id'))
        except:
            return Response('Review not exist', status=status.HTTP_400_BAD_REQUEST)
        content = request.data.get('content')
        selected_review.content = content
        try:
            selected_review.save()
        except:
            return Response('Review updated failed', status=status.HTTP_400_BAD_REQUEST)
        return Response('Review updated success', status=status.HTTP_201_CREATED)

    def delete(self, request, **kwargs):
        """리뷰 삭제 --- # /reviews/<id:int>/ ## headers - Authorization : Token "key 값" [ex> Token 822a24a314dfbc387128d82af6b952191dd71651]"""
        if kwargs.get('id') is None:
            return Response('invalid request', status=status.HTTP_400_BAD_REQUEST)
        else:
            review_id = kwargs.get('id')
            try:
                review_obj = Review.objects.get(id=review_id)
                review_obj.delete()
                return Response('Review was deleted', status=status.HTTP_200_OK)
            except:
                return Response('Review not Found', status=status.HTTP_400_BAD_REQUEST)


Prompt:
Implement the Python class `TestBDD100KInsSegEval` described below.

Class description:
Test cases for BDD100K detection evaluation.

Method signatures and docstrings:
- def test_frame(self) -> None: Test case for the function frame().
- def test_summary(self) -> None: Check evaluation scores' correctness.

Implement the Python class `TestBDD100KInsSegEval` described below.

Class description:
Test cases for BDD100K detection evaluation.

Method signatures and docstrings:
- def test_frame(self) -> None: Test case for the function frame().
- def test_summary(self) -> None: Check evaluation scores' correctness.

<|skeleton|>
class TestBDD100KInsSegEval:
    """Test cases for BDD100K detection evaluation."""

    def test_frame(self) -> None:
        """Test case for the function frame()."""
        <|body_0|>

    def test_summary(self) -> None:
        """Check evaluation scores' correctness."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        data_frame = self.result.pd_frame()
        categories = set(['pedestrian', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle', 'OVERALL'])
        self.assertSetEqual(categories, set(data_frame.index.values))
        data_arr = data_frame.to_numpy()
        APs = np.array([100.0, 90.0, 15.81683168, -1.0, -1.0, -1.0, -1.0, -1.0, 68.60561056])
        self.assertTrue(np.isclose(np.nan_to_num(data_arr[:, 0], nan=-1.0), APs).all())
        overall_scores = np.array([68.60561056, 89.68646865, 66.66666667, 68.60561056, 70.92409241, 70.92409241, 65.83333333, 70.83333333, 70.83333333, 70.83333333, 70.83333333, 70.83333333])
        self.assertTrue(np.isclose(np.nan_to_num(data_arr[-1], nan=-1.0), overall_scores).all())
<|end_body_0|>

<|body_start_1|>
        summary = self.result.summary()
        overall_reference = {'AP/pedestrian': 99.99999999999997, 'AP/rider': 89.99999999999999, 'AP/car': 15.816831683168317, 'AP/truck': -1.0, 'AP/bus': -1.0, 'AP/train': -1.0, 'AP/motorcycle': -1.0, 'AP/bicycle': -1.0, 'AP': 68.60561056105611, 'AP50': 89.68646864686468, 'AP75': 66.66666666666666, 'APs': 68.60561056105611, 'APm': 70.92409240924093, 'APl': 70.92409240924093, 'AR1': 65.83333333333333, 'AR10': 70.83333333333334, 'AR100': 70.83333333333334, 'ARs': 70.83333333333334, 'ARm': 70.83333333333334, 'ARl': 70.83333333333334}
        self.assertSetEqual(set(summary.keys()), set(overall_reference.keys()))
        for name, score in summary.items():
            if np.isnan(score):
                score = np.nan_to_num(score, nan=-1.0)
            self.assertAlmostEqual(score, overall_reference[name])
<|end_body_1|>

Solution:
class TestBDD100KInsSegEval:
    """Test cases for BDD100K detection evaluation."""

    def test_frame(self) -> None:
        """Test case for the function frame()."""
        data_frame = self.result.pd_frame()
        categories = set(['pedestrian', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle', 'OVERALL'])
        self.assertSetEqual(categories, set(data_frame.index.values))
        data_arr = data_frame.to_numpy()
        APs = np.array([100.0, 90.0, 15.81683168, -1.0, -1.0, -1.0, -1.0, -1.0, 68.60561056])
        self.assertTrue(np.isclose(np.nan_to_num(data_arr[:, 0], nan=-1.0), APs).all())
        overall_scores = np.array([68.60561056, 89.68646865, 66.66666667, 68.60561056, 70.92409241, 70.92409241, 65.83333333, 70.83333333, 70.83333333, 70.83333333, 70.83333333, 70.83333333])
        self.assertTrue(np.isclose(np.nan_to_num(data_arr[-1], nan=-1.0), overall_scores).all())

    def test_summary(self) -> None:
        """Check evaluation scores' correctness."""
        summary = self.result.summary()
        overall_reference = {'AP/pedestrian': 99.99999999999997, 'AP/rider': 89.99999999999999, 'AP/car': 15.816831683168317, 'AP/truck': -1.0, 'AP/bus': -1.0, 'AP/train': -1.0, 'AP/motorcycle': -1.0, 'AP/bicycle': -1.0, 'AP': 68.60561056105611, 'AP50': 89.68646864686468, 'AP75': 66.66666666666666, 'APs': 68.60561056105611, 'APm': 70.92409240924093, 'APl': 70.92409240924093, 'AR1': 65.83333333333333, 'AR10': 70.83333333333334, 'AR100': 70.83333333333334, 'ARs': 70.83333333333334, 'ARm': 70.83333333333334, 'ARl': 70.83333333333334}
        self.assertSetEqual(set(summary.keys()), set(overall_reference.keys()))
        for name, score in summary.items():
            if np.isnan(score):
                score = np.nan_to_num(score, nan=-1.0)
            self.assertAlmostEqual(score, overall_reference[name])


minAreaRect

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minAreaRect(self, points): :type points: List[List[int]] :rtype: int
- def minAreaRect2(self, points): :type points: List[List[int]] :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minAreaRect(self, points): :type points: List[List[int]] :rtype: int
- def minAreaRect2(self, points): :type points: List[List[int]] :rtype: int

<|skeleton|>
class Solution:

    def minAreaRect(self, points):
        """:type points: List[List[int]] :rtype: int"""
        <|body_0|>

    def minAreaRect2(self, points):
        """:type points: List[List[int]] :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        s = set()
        for p in points:
            s.add((p[0], p[1]))

        def ok(p1, p2, p3):
            row, col = (False, False)
            if p1[0] == p2[0] or p1[0] == p3[0] or p2[0] == p3[0]:
                row = True
            if p1[1] == p2[1] or p1[1] == p3[1] or p2[1] == p3[1]:
                col = True
            return row and col
        ans = 40000 * 40001
        for i in range(len(points)):
            for j in range(i + 1, len(points)):
                for k in range(j + 1, len(points)):
                    if ok(points[i], points[j], points[k]) and (points[i][0] ^ points[j][0] ^ points[k][0], points[i][1] ^ points[j][1] ^ points[k][1]) in s:
                        r1, r2, r3 = (points[i][0], points[j][0], points[k][0])
                        c1, c2, c3 = (points[i][1], points[j][1], points[k][1])
                        ans = min(ans, (max(r1, r2, r3) - min(r1, r2, r3)) * (max(c1, c2, c3) - min(c1, c2, c3)))
        return 0 if ans == 40000 * 40001 else ans
<|end_body_0|>

<|body_start_1|>
        points = sorted(points, key=lambda x: x[0])
        s = set()
        for p in points:
            s.add((p[0], p[1]))
        ans = 1600000001
        for i in range(len(points)):
            for j in range(i + 1, len(points)):
                r1, c1, r2, c2 = (points[i][0], points[i][1], points[j][0], points[j][1])
                if points[i][0] == points[j][0] or points[i][1] == points[j][1] or (r2 - r1) * abs(c1 - c2) > ans:
                    continue
                if (r1, c2) in s and (r2, c1) in s:
                    ans = (r2 - r1) * abs(c1 - c2)
        return 0 if ans == 1600000001 else ans
<|end_body_1|>

Solution:
class Solution:
    def minAreaRect(self, points):
        """:type points: List[List[int]] :rtype: int"""
        s = set()
        for p in points:
            s.add((p[0], p[1]))

        def ok(p1, p2, p3):
            row, col = (False, False)
            if p1[0] == p2[0] or p1[0] == p3[0] or p2[0] == p3[0]:
                row = True
            if p1[1] == p2[1] or p1[1] == p3[1] or p2[1] == p3[1]:
                col = True
            return row and col
        ans = 40000 * 40001
        for i in range(len(points)):
            for j in range(i + 1, len(points)):
                for k in range(j + 1, len(points)):
                    if ok(points[i], points[j], points[k]) and (points[i][0] ^ points[j][0] ^ points[k][0], points[i][1] ^ points[j][1] ^ points[k][1]) in s:
                        r1, r2, r3 = (points[i][0], points[j][0], points[k][0])
                        c1, c2, c3 = (points[i][1], points[j][1], points[k][1])
                        ans = min(ans, (max(r1, r2, r3) - min(r1, r2, r3)) * (max(c1, c2, c3) - min(c1, c2, c3)))
        return 0 if ans == 40000 * 40001 else ans

    def minAreaRect2(self, points):
        """:type points: List[List[int]] :rtype: int"""
        points = sorted(points, key=lambda x: x[0])
        s = set()
        for p in points:
            s.add((p[0], p[1]))
        ans = 1600000001
        for i in range(len(points)):
            for j in range(i + 1, len(points)):
                r1, c1, r2, c2 = (points[i][0], points[i][1], points[j][0], points[j][1])
                if points[i][0] == points[j][0] or points[i][1] == points[j][1] or (r2 - r1) * abs(c1 - c2) > ans:
                    continue
                if (r1, c2) in s and (r2, c1) in s:
                    ans = (r2 - r1) * abs(c1 - c2)
        return 0 if ans == 1600000001 else ans


save

Prompt:
Implement the Python class `ElasticCommand` described below.

Class description:
static interface to manage elasticsearch indices and data

Method signatures and docstrings:
- def get(zone, id): raise SearchError :Example: >>> ElasticCommand.get(zone, id) True
- def save(zone, id, dict_of_data, force_refresh=False): raise IndexError :Example: >>> ElasticCommand.save(zone, id, dict_of_data, force_refresh=False) True
- def delete_date_range(zone, date_field, max_days): raise SearchError
- def create_index(zone): raise IndexError :Example: >>> ElasticCommand.create_index(zone) True
- def delete_index(zone): raise IndexError :Example: >>> ElasticCommand.delete_index(zone) True

Implement the Python class `ElasticCommand` described below.

Class description:
static interface to manage elasticsearch indices and data

Method signatures and docstrings:
- def get(zone, id): raise SearchError :Example: >>> ElasticCommand.get(zone, id) True
- def save(zone, id, dict_of_data, force_refresh=False): raise IndexError :Example: >>> ElasticCommand.save(zone, id, dict_of_data, force_refresh=False) True
- def delete_date_range(zone, date_field, max_days): raise SearchError
- def create_index(zone): raise IndexError :Example: >>> ElasticCommand.create_index(zone) True
- def delete_index(zone): raise IndexError :Example: >>> ElasticCommand.delete_index(zone) True

<|skeleton|>
class ElasticCommand:
    """static interface to manage elasticsearch indices and data"""

    def get(zone, id):
        """raise SearchError :Example: >>> ElasticCommand.get(zone, id) True"""
        <|body_0|>

    def save(zone, id, dict_of_data, force_refresh=False):
        """raise IndexError :Example: >>> ElasticCommand.save(zone, id, dict_of_data, force_refresh=False) True"""
        <|body_1|>

    def delete_date_range(zone, date_field, max_days):
        """raise SearchError"""
        <|body_2|>

    def create_index(zone):
        """raise IndexError :Example: >>> ElasticCommand.create_index(zone) True"""
        <|body_3|>

    def delete_index(zone):
        """raise IndexError :Example: >>> ElasticCommand.delete_index(zone) True"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.get(id)
        except ValueError as ve:
            raise SearchError(ve)
<|end_body_0|>

<|body_start_1|>
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.save(id, dict_of_data, force_refresh=force_refresh)
        except ValueError as ve:
            raise IndexError(ve)
<|end_body_1|>

<|body_start_2|>
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.delete_date_range(date_field, max_days)
        except ValueError as ve:
            raise SearchError(ve)
<|end_body_2|>

<|body_start_3|>
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.create_index()
        except ValueError as ve:
            raise IndexError(ve)
<|end_body_3|>

<|body_start_4|>
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.delete_index()
        except ValueError as ve:
            raise IndexError(ve)
<|end_body_4|>

Solution:
class ElasticCommand:
    """static interface to manage elasticsearch indices and data"""

    def get(zone, id):
        """raise SearchError :Example: >>> ElasticCommand.get(zone, id) True"""
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.get(id)
        except ValueError as ve:
            raise SearchError(ve)

    def save(zone, id, dict_of_data, force_refresh=False):
        """raise IndexError :Example: >>> ElasticCommand.save(zone, id, dict_of_data, force_refresh=False) True"""
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.save(id, dict_of_data, force_refresh=force_refresh)
        except ValueError as ve:
            raise IndexError(ve)

    def delete_date_range(zone, date_field, max_days):
        """raise SearchError"""
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.delete_date_range(date_field, max_days)
        except ValueError as ve:
            raise SearchError(ve)

    def create_index(zone):
        """raise IndexError :Example: >>> ElasticCommand.create_index(zone) True"""
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.create_index()
        except ValueError as ve:
            raise IndexError(ve)

    def delete_index(zone):
        """raise IndexError :Example: >>> ElasticCommand.delete_index(zone) True"""
        try:
            session = ElasticSession(hosts=[config.ES.ES_HOST], zone=zone)
            return session.delete_index()
        except ValueError as ve:
            raise IndexError(ve)


Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def countBinarySubstrings(self, s): :type s: str :rtype: int
- def countBinarySubstrings2(self, s): :type s: str :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def countBinarySubstrings(self, s): :type s: str :rtype: int
- def countBinarySubstrings2(self, s): :type s: str :rtype: int

<|skeleton|>
class Solution:

    def countBinarySubstrings(self, s):
        """:type s: str :rtype: int"""
        <|body_0|>

    def countBinarySubstrings2(self, s):
        """:type s: str :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        temp = ''
        cur_cnt = 0
        pre_cnt = 0
        sum_total = 0
        for str in s:
            if str == temp:
                cur_cnt += 1
            else:
                temp = str
                sum_total += min((cur_cnt, pre_cnt))
                pre_cnt = cur_cnt
                cur_cnt = 1
        sum_total += min((cur_cnt, pre_cnt))
        return sum_total
<|end_body_0|>

<|body_start_1|>
        sum_total = 0
        pre = s[0]
        pos = 0
        pre_len = 0
        for i in range(1, len(s)):
            if s[i] != pre:
                sum_total += 1
                pre_len = i - pos
                pos = i
            elif i - pos + 1 <= pre_len:
                sum_total += 1
            pre = s[i]
        return sum_total
<|end_body_1|>

Solution:
class Solution:
    def countBinarySubstrings(self, s):
        """:type s: str :rtype: int"""
        temp = ''
        cur_cnt = 0
        pre_cnt = 0
        sum_total = 0
        for str in s:
            if str == temp:
                cur_cnt += 1
            else:
                temp = str
                sum_total += min((cur_cnt, pre_cnt))
                pre_cnt = cur_cnt
                cur_cnt = 1
        sum_total += min((cur_cnt, pre_cnt))
        return sum_total

    def countBinarySubstrings2(self, s):
        """:type s: str :rtype: int"""
        sum_total = 0
        pre = s[0]
        pos = 0
        pre_len = 0
        for i in range(1, len(s)):
            if s[i] != pre:
                sum_total += 1
                pre_len = i - pos
                pos = i
            elif i - pos + 1 <= pre_len:
                sum_total += 1
            pre = s[i]
        return sum_total


Prompt:
Implement the Python class `BifrostConfig` described below.

Class description:
Implementation of the 'BifrostConfig' model. Specifies the settings of a BifrostConfig. Its used by both Request and Response structures. Attributes: image_version (string): Specifies the bifrost image version. cpu (int): Specifies the cpu for the bifrost config. description (string): Specifies a description of the VLAN. id (int): Specifies the id of the VLAN tag. iface_group_name (string): Specifies the interface group name of the VLAN. It is in the format of <base_interface_group_name>.<vlan_id>. memory (int): Specifies the memory for the bifrost config. mtu (int): Specifies the mtu for the bifrost vlan. state (string): 4 types of States UNKNOWN ACTIVE DISABLED DELETING subnet (BifrostSubn

Method signatures and docstrings:
- def __init__(self, image_version=None, cpu=None, description=None, id=None, iface_group_name=None, memory=None, mtu=None, state=None, subnet=None, tenant_id=None, mtype=None, vlan_name=None): Constructor for the BifrostConfig class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `BifrostConfig` described below.

Class description:
Implementation of the 'BifrostConfig' model. Specifies the settings of a BifrostConfig. Its used by both Request and Response structures. Attributes: image_version (string): Specifies the bifrost image version. cpu (int): Specifies the cpu for the bifrost config. description (string): Specifies a description of the VLAN. id (int): Specifies the id of the VLAN tag. iface_group_name (string): Specifies the interface group name of the VLAN. It is in the format of <base_interface_group_name>.<vlan_id>. memory (int): Specifies the memory for the bifrost config. mtu (int): Specifies the mtu for the bifrost vlan. state (string): 4 types of States UNKNOWN ACTIVE DISABLED DELETING subnet (BifrostSubn

Method signatures and docstrings:
- def __init__(self, image_version=None, cpu=None, description=None, id=None, iface_group_name=None, memory=None, mtu=None, state=None, subnet=None, tenant_id=None, mtype=None, vlan_name=None): Constructor for the BifrostConfig class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class BifrostConfig:
    """Implementation of the 'BifrostConfig' model. Specifies the settings of a BifrostConfig. Its used by both Request and Response structures. Attributes: image_version (string): Specifies the bifrost image version. cpu (int): Specifies the cpu for the bifrost config. description (string): Specifies a description of the VLAN. id (int): Specifies the id of the VLAN tag. iface_group_name (string): Specifies the interface group name of the VLAN. It is in the format of <base_interface_group_name>.<vlan_id>. memory (int): Specifies the memory for the bifrost config. mtu (int): Specifies the mtu for the bifrost vlan. state (string): 4 types of States UNKNOWN ACTIVE DISABLED DELETING subnet (BifrostSubn"""

    def __init__(self, image_version=None, cpu=None, description=None, id=None, iface_group_name=None, memory=None, mtu=None, state=None, subnet=None, tenant_id=None, mtype=None, vlan_name=None):
        """Constructor for the BifrostConfig class"""
        <|body_0|>

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.image_version = image_version
        self.cpu = cpu
        self.description = description
        self.id = id
        self.iface_group_name = iface_group_name
        self.memory = memory
        self.mtu = mtu
        self.state = state
        self.subnet = subnet
        self.tenant_id = tenant_id
        self.mtype = mtype
        self.vlan_name = vlan_name
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        image_version = dictionary.get('ImageVersion')
        cpu = dictionary.get('cpu')
        description = dictionary.get('description')
        id = dictionary.get('id')
        iface_group_name = dictionary.get('ifaceGroupName')
        memory = dictionary.get('memory')
        mtu = dictionary.get('mtu')
        state = dictionary.get('state')
        subnet = cohesity_management_sdk.models.bifrost_subnet.BifrostSubnet.from_dictionary(dictionary.get('subnet')) if dictionary.get('subnet') else None
        tenant_id = dictionary.get('tenantId')
        mtype = dictionary.get('type')
        vlan_name = dictionary.get('vlanName')
        return cls(image_version, cpu, description, id, iface_group_name, memory, mtu, state, subnet, tenant_id, mtype, vlan_name)
<|end_body_1|>

Solution:
class BifrostConfig:
    """Implementation of the 'BifrostConfig' model. Specifies the settings of a BifrostConfig. Its used by both Request and Response structures. Attributes: image_version (string): Specifies the bifrost image version. cpu (int): Specifies the cpu for the bifrost config. description (string): Specifies a description of the VLAN. id (int): Specifies the id of the VLAN tag. iface_group_name (string): Specifies the interface group name of the VLAN. It is in the format of <base_interface_group_name>.<vlan_id>. memory (int): Specifies the memory for the bifrost config. mtu (int): Specifies the mtu for the bifrost vlan. state (string): 4 types of States UNKNOWN ACTIVE DISABLED DELETING subnet (BifrostSubn"""

    def __init__(self, image_version=None, cpu=None, description=None, id=None, iface_group_name=None, memory=None, mtu=None, state=None, subnet=None, tenant_id=None, mtype=None, vlan_name=None):
        """Constructor for the BifrostConfig class"""
        self.image_version = image_version
        self.cpu = cpu
        self.description = description
        self.id = id
        self.iface_group_name = iface_group_name
        self.memory = memory
        self.mtu = mtu
        self.state = state
        self.subnet = subnet
        self.tenant_id = tenant_id
        self.mtype = mtype
        self.vlan_name = vlan_name

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        image_version = dictionary.get('ImageVersion')
        cpu = dictionary.get('cpu')
        description = dictionary.get('description')
        id = dictionary.get('id')
        iface_group_name = dictionary.get('ifaceGroupName')
        memory = dictionary.get('memory')
        mtu = dictionary.get('mtu')
        state = dictionary.get('state')
        subnet = cohesity_management_sdk.models.bifrost_subnet.BifrostSubnet.from_dictionary(dictionary.get('subnet')) if dictionary.get('subnet') else None
        tenant_id = dictionary.get('tenantId')
        mtype = dictionary.get('type')
        vlan_name = dictionary.get('vlanName')
        return cls(image_version, cpu, description, id, iface_group_name, memory, mtu, state, subnet, tenant_id, mtype, vlan_name)


forward

Prompt:
Implement the Python class `GaussianDistributionOutput` described below.

Class description:
Implement the GaussianDistributionOutput class.

Method signatures and docstrings:
- def __init__(self, network, dist_dim, use_tied_cov=False, use_trainable_cov=True, sigma=None, max_scale=1.0, scale_nonlinearity='softplus'): A Gaussian distribution on top of a neural network. Args: network (nn.Module): A torch module that outputs the parameters of the Gaussian distribution. dist_dim ([type]): The dimension of the Gaussian distribution. use_tied_cov (bool, optional): Whether to use a tied covariance matrix. Defaults to False. use_trainable_cov (bool, optional): True if the covariance matrix is to be learned. Defaults to True. If False, the covariance matrix is set to I. sigma (float, optional): Initial value of scale. max_scale (float, optional): Maximum scale when using sigmoid non-linearity. scale_nonlinearity (str, optional): Which non-linearity to use for scale -- sigmoid or softplus. Defaults to softplus.
- def forward(self, tensor): The forward pass. Args: tensor (torch.Tensor): The input tensor. Returns: out_dist: The Gaussian distribution with parameters obtained by passing the input tensor through self.network.

Implement the Python class `GaussianDistributionOutput` described below.

Class description:
Implement the GaussianDistributionOutput class.

Method signatures and docstrings:
- def __init__(self, network, dist_dim, use_tied_cov=False, use_trainable_cov=True, sigma=None, max_scale=1.0, scale_nonlinearity='softplus'): A Gaussian distribution on top of a neural network. Args: network (nn.Module): A torch module that outputs the parameters of the Gaussian distribution. dist_dim ([type]): The dimension of the Gaussian distribution. use_tied_cov (bool, optional): Whether to use a tied covariance matrix. Defaults to False. use_trainable_cov (bool, optional): True if the covariance matrix is to be learned. Defaults to True. If False, the covariance matrix is set to I. sigma (float, optional): Initial value of scale. max_scale (float, optional): Maximum scale when using sigmoid non-linearity. scale_nonlinearity (str, optional): Which non-linearity to use for scale -- sigmoid or softplus. Defaults to softplus.
- def forward(self, tensor): The forward pass. Args: tensor (torch.Tensor): The input tensor. Returns: out_dist: The Gaussian distribution with parameters obtained by passing the input tensor through self.network.

<|skeleton|>
class GaussianDistributionOutput:

    def __init__(self, network, dist_dim, use_tied_cov=False, use_trainable_cov=True, sigma=None, max_scale=1.0, scale_nonlinearity='softplus'):
        """A Gaussian distribution on top of a neural network. Args: network (nn.Module): A torch module that outputs the parameters of the Gaussian distribution. dist_dim ([type]): The dimension of the Gaussian distribution. use_tied_cov (bool, optional): Whether to use a tied covariance matrix. Defaults to False. use_trainable_cov (bool, optional): True if the covariance matrix is to be learned. Defaults to True. If False, the covariance matrix is set to I. sigma (float, optional): Initial value of scale. max_scale (float, optional): Maximum scale when using sigmoid non-linearity. scale_nonlinearity (str, optional): Which non-linearity to use for scale -- sigmoid or softplus. Defaults to softplus."""
        <|body_0|>

    def forward(self, tensor):
        """The forward pass. Args: tensor (torch.Tensor): The input tensor. Returns: out_dist: The Gaussian distribution with parameters obtained by passing the input tensor through self.network."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.dist_dim = dist_dim
        self.network = network
        self.use_tied_cov = use_tied_cov
        self.use_trainable_cov = use_trainable_cov
        self.max_scale = max_scale
        self.scale_nonlinearity = scale_nonlinearity
        if not self.use_trainable_cov:
            assert sigma is not None, 'sigma cannot be None for non-trainable covariance!'
            self.sigma = sigma
        if self.use_trainable_cov and self.use_tied_cov:
            self.usigma = nn.Parameter(inverse_softplus(torch.full([1, dist_dim], sigma if sigma is not None else 0.1)))
<|end_body_0|>

<|body_start_1|>
        args_tensor = self.network(tensor)
        mean_tensor = args_tensor[..., :self.dist_dim]
        if self.use_trainable_cov:
            if self.use_tied_cov:
                if self.scale_nonlinearity == 'sigmoid':
                    scale_tensor = self.max_scale * torch.sigmoid(self.usigma)
                else:
                    scale_tensor = F.softplus(self.usigma)
                out_dist = td.normal.Normal(mean_tensor, scale_tensor + SCALE_OFFSET)
                out_dist = td.independent.Independent(out_dist, 1)
            else:
                if self.scale_nonlinearity == 'sigmoid':
                    scale_tensor = self.max_scale * torch.sigmoid(args_tensor[..., self.dist_dim:])
                else:
                    scale_tensor = F.softplus(args_tensor[..., self.dist_dim:])
                out_dist = td.normal.Normal(mean_tensor, scale_tensor + SCALE_OFFSET)
                out_dist = td.independent.Independent(out_dist, 1)
        else:
            out_dist = td.normal.Normal(mean_tensor, self.sigma)
            out_dist = td.independent.Independent(out_dist, 1)
        return out_dist
<|end_body_1|>

Solution:
class GaussianDistributionOutput:
    def __init__(self, network, dist_dim, use_tied_cov=False, use_trainable_cov=True, sigma=None, max_scale=1.0, scale_nonlinearity='softplus'):
        """A Gaussian distribution on top of a neural network. Args: network (nn.Module): A torch module that outputs the parameters of the Gaussian distribution. dist_dim ([type]): The dimension of the Gaussian distribution. use_tied_cov (bool, optional): Whether to use a tied covariance matrix. Defaults to False. use_trainable_cov (bool, optional): True if the covariance matrix is to be learned. Defaults to True. If False, the covariance matrix is set to I. sigma (float, optional): Initial value of scale. max_scale (float, optional): Maximum scale when using sigmoid non-linearity. scale_nonlinearity (str, optional): Which non-linearity to use for scale -- sigmoid or softplus. Defaults to softplus."""
        super().__init__()
        self.dist_dim = dist_dim
        self.network = network
        self.use_tied_cov = use_tied_cov
        self.use_trainable_cov = use_trainable_cov
        self.max_scale = max_scale
        self.scale_nonlinearity = scale_nonlinearity
        if not self.use_trainable_cov:
            assert sigma is not None, 'sigma cannot be None for non-trainable covariance!'
            self.sigma = sigma
        if self.use_trainable_cov and self.use_tied_cov:
            self.usigma = nn.Parameter(inverse_softplus(torch.full([1, dist_dim], sigma if sigma is not None else 0.1)))

    def forward(self, tensor):
        """The forward pass. Args: tensor (torch.Tensor): The input tensor. Returns: out_dist: The Gaussian distribution with parameters obtained by passing the input tensor through self.network."""
        args_tensor = self.network(tensor)
        mean_tensor = args_tensor[..., :self.dist_dim]
        if self.use_trainable_cov:
            if self.use_tied_cov:
                if self.scale_nonlinearity == 'sigmoid':
                    scale_tensor = self.max_scale * torch.sigmoid(self.usigma)
                else:
                    scale_tensor = F.softplus(self.usigma)
                out_dist = td.normal.Normal(mean_tensor, scale_tensor + SCALE_OFFSET)
                out_dist = td.independent.Independent(out_dist, 1)
            else:
                if self.scale_nonlinearity == 'sigmoid':
                    scale_tensor = self.max_scale * torch.sigmoid(args_tensor[..., self.dist_dim:])
                else:
                    scale_tensor = F.softplus(args_tensor[..., self.dist_dim:])
                out_dist = td.normal.Normal(mean_tensor, scale_tensor + SCALE_OFFSET)
                out_dist = td.independent.Independent(out_dist, 1)
        else:
            out_dist = td.normal.Normal(mean_tensor, self.sigma)
            out_dist = td.independent.Independent(out_dist, 1)
        return out_dist


isAnagram

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isAnagram(self, s1, s2): 排序对比法 思路： 1. 将两个字符串转为list； 2. 堆两个list进行排序； 3. 然后注意比较对应位置的字符是否相等即可
- def isAnagram2(self, s1, s2): 字符统计对比法(Knowledge) 思路： 1. 创建两个长度位26的数组； 2. 各自统计两个字符串中每个字符出现的次数； 3. 接着对比两个数组即可 PS: ord => python中的一个内置函数，传入一个字符，会返回其对应的ASCII值

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isAnagram(self, s1, s2): 排序对比法 思路： 1. 将两个字符串转为list； 2. 堆两个list进行排序； 3. 然后注意比较对应位置的字符是否相等即可
- def isAnagram2(self, s1, s2): 字符统计对比法(Knowledge) 思路： 1. 创建两个长度位26的数组； 2. 各自统计两个字符串中每个字符出现的次数； 3. 接着对比两个数组即可 PS: ord => python中的一个内置函数，传入一个字符，会返回其对应的ASCII值

<|skeleton|>
class Solution:

    def isAnagram(self, s1, s2):
        """排序对比法 思路： 1. 将两个字符串转为list； 2. 堆两个list进行排序； 3. 然后注意比较对应位置的字符是否相等即可"""
        <|body_0|>

    def isAnagram2(self, s1, s2):
        """字符统计对比法(Knowledge) 思路： 1. 创建两个长度位26的数组； 2. 各自统计两个字符串中每个字符出现的次数； 3. 接着对比两个数组即可 PS: ord => python中的一个内置函数，传入一个字符，会返回其对应的ASCII值"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if len(s1) != len(s2):
            return False
        list1 = list(s1)
        list2 = list(s2)
        list1.sort()
        list2.sort()
        for i in range(len(list1)):
            if list1[i] != list2[i]:
                return False
        return True
<|end_body_0|>

<|body_start_1|>
        if len(s1) != len(s2):
            return False
        c1 = [0] * 26
        c2 = [0] * 26
        for i in range(len(s1)):
            c1[ord(s1[i]) - ord('a')] += 1
            c2[ord(s1[i]) - ord('a')] += 1
        for i in range(26):
            if c1[i] != c2[i]:
                return False
        return True
<|end_body_1|>

Solution:
class Solution:
    def isAnagram(self, s1, s2):
        """排序对比法 思路： 1. 将两个字符串转为list； 2. 堆两个list进行排序； 3. 然后注意比较对应位置的字符是否相等即可"""
        if len(s1) != len(s2):
            return False
        list1 = list(s1)
        list2 = list(s2)
        list1.sort()
        list2.sort()
        for i in range(len(list1)):
            if list1[i] != list2[i]:
                return False
        return True

    def isAnagram2(self, s1, s2):
        """字符统计对比法(Knowledge) 思路： 1. 创建两个长度位26的数组； 2. 各自统计两个字符串中每个字符出现的次数； 3. 接着对比两个数组即可 PS: ord => python中的一个内置函数，传入一个字符，会返回其对应的ASCII值"""
        if len(s1) != len(s2):
            return False
        c1 = [0] * 26
        c2 = [0] * 26
        for i in range(len(s1)):
            c1[ord(s1[i]) - ord('a')] += 1
            c2[ord(s1[i]) - ord('a')] += 1
        for i in range(26):
            if c1[i] != c2[i]:
                return False
        return True


Prompt:
Implement the Python class `CodepipelineStages` described below.

Class description:
Check if CodePipeline Stages are set up properly.

Method signatures and docstrings:
- def _format_error_message(self, message, scenario): Format error message with scenario text
- def check_stage_count(self, stages, path, scenario): Check that there is minimum 2 stages.
- def check_first_stage(self, stages, path, scenario): Validate the first stage of a pipeline has source actions.
- def check_source_actions(self, stages, path, scenario): Validate the all of the stages.
- def check_names_unique(self, value, path, scenario): Check that stage names are unique.
- def match(self, cfn): Check CodePipeline stages

Implement the Python class `CodepipelineStages` described below.

Class description:
Check if CodePipeline Stages are set up properly.

Method signatures and docstrings:
- def _format_error_message(self, message, scenario): Format error message with scenario text
- def check_stage_count(self, stages, path, scenario): Check that there is minimum 2 stages.
- def check_first_stage(self, stages, path, scenario): Validate the first stage of a pipeline has source actions.
- def check_source_actions(self, stages, path, scenario): Validate the all of the stages.
- def check_names_unique(self, value, path, scenario): Check that stage names are unique.
- def match(self, cfn): Check CodePipeline stages

<|skeleton|>
class CodepipelineStages:
    """Check if CodePipeline Stages are set up properly."""

    def _format_error_message(self, message, scenario):
        """Format error message with scenario text"""
        <|body_0|>

    def check_stage_count(self, stages, path, scenario):
        """Check that there is minimum 2 stages."""
        <|body_1|>

    def check_first_stage(self, stages, path, scenario):
        """Validate the first stage of a pipeline has source actions."""
        <|body_2|>

    def check_source_actions(self, stages, path, scenario):
        """Validate the all of the stages."""
        <|body_3|>

    def check_names_unique(self, value, path, scenario):
        """Check that stage names are unique."""
        <|body_4|>

    def match(self, cfn):
        """Check CodePipeline stages"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        if scenario:
            scenario_text = ' When ' + ' and '.join(['condition "%s" is %s' % (k, v) for k, v in scenario.items()])
            return message + scenario_text
        return message
<|end_body_0|>

<|body_start_1|>
        matches = []
        if len(stages) < 2:
            message = 'CodePipeline has {} stages. There must be at least two stages.'.format(len(stages))
            matches.append(RuleMatch(path, self._format_error_message(message, scenario)))
        return matches
<|end_body_1|>

<|body_start_2|>
        matches = []
        if len(stages) < 1:
            self.logger.debug('Stages was empty. Should have been caught by generic linting.')
            return matches
        first_stage = set([a.get('ActionTypeId').get('Category') for a in stages[0]['Actions']])
        if first_stage and 'Source' not in first_stage:
            message = 'The first stage of a pipeline must contain at least one source action.'
            matches.append(RuleMatch(path + [0, 'Name'], self._format_error_message(message, scenario)))
        if len(first_stage) != 1:
            message = 'The first stage of a pipeline must contain only source actions.'
            matches.append(RuleMatch(path + [0, 'Name'], self._format_error_message(message, scenario)))
        return matches
<|end_body_2|>

<|body_start_3|>
        matches = []
        categories = set()
        if len(stages) < 1:
            self.logger.debug('Stages was empty. Should have been caught by generic linting.')
            return matches
        for sidx, stage in enumerate(stages):
            for aidx, action in enumerate(stage.get('Actions', [])):
                action_type_id = action.get('ActionTypeId')
                categories.add(action_type_id.get('Category'))
                if sidx > 0 and action_type_id.get('Category') == 'Source':
                    message = 'Only the first stage of a pipeline may contain source actions.'
                    matches.append(RuleMatch(path + [sidx, 'Actions', aidx], self._format_error_message(message, scenario)))
        if not categories - set(['Source']):
            message = 'At least one stage in pipeline must contain an action that is not a source action.'
            matches.append(RuleMatch(path, self._format_error_message(message, scenario)))
        return matches
<|end_body_3|>

<|body_start_4|>
        matches = []
        stage_names = set()
        for sidx, stage in enumerate(value):
            stage_name = stage.get('Name')
            if isinstance(stage_name, six.string_types):
                if stage_name in stage_names:
                    message = 'All stage names within a pipeline must be unique. ({name})'.format(name=stage_name)
                    matches.append(RuleMatch(path + [sidx, 'Name'], self._format_error_message(message, scenario)))
                stage_names.add(stage_name)
            else:
                self.logger.debug('Found non string for stage name: %s', stage_name)
        return matches
<|end_body_4|>

<|body_start_5|>
        matches = []
        resources = cfn.get_resource_properties(['AWS::CodePipeline::Pipeline'])
        for resource in resources:
            path = resource['Path'] + ['Stages']
            properties = resource['Value']
            s_stages = cfn.get_object_without_nested_conditions(properties.get('Stages'), path)
            for s_stage in s_stages:
                s_stage_obj = s_stage.get('Object')
                s_scenario = s_stage.get('Scenario')
                if not isinstance(s_stage_obj, list):
                    self.logger.debug('Stages not list. Should have been caught by generic linting.')
                    continue
                try:
                    matches.extend(self.check_stage_count(s_stage_obj, path, s_scenario))
                    matches.extend(self.check_first_stage(s_stage_obj, path, s_scenario))
                    matches.extend(self.check_source_actions(s_stage_obj, path, s_scenario))
                    matches.extend(self.check_names_unique(s_stage_obj, path, s_scenario))
                except AttributeError as err:
                    self.logger.debug('Got AttributeError. Should have been caught by generic linting. Ignoring the error here: %s', str(err))
        return matches
<|end_body_5|>

Solution:
class CodepipelineStages:
    """Check if CodePipeline Stages are set up properly."""

    def _format_error_message(self, message, scenario):
        """Format error message with scenario text"""
        if scenario:
            scenario_text = ' When ' + ' and '.join(['condition "%s" is %s' % (k, v) for k, v in scenario.items()])
            return message + scenario_text
        return message

    def check_stage_count(self, stages, path, scenario):
        """Check that there is minimum 2 stages."""
        matches = []
        if len(stages) < 2:
            message = 'CodePipeline has {} stages. There must be at least two stages.'.format(len(stages))
            matches.append(RuleMatch(path, self._format_error_message(message, scenario)))
        return matches

    def check_first_stage(self, stages, path, scenario):
        """Validate the first stage of a pipeline has source actions."""
        matches = []
        if len(stages) < 1:
            self.logger.debug('Stages was empty. Should have been caught by generic linting.')
            return matches
        first_stage = set([a.get('ActionTypeId').get('Category') for a in stages[0]['Actions']])
        if first_stage and 'Source' not in first_stage:
            message = 'The first stage of a pipeline must contain at least one source action.'
            matches.append(RuleMatch(path + [0, 'Name'], self._format_error_message(message, scenario)))
        if len(first_stage) != 1:
            message = 'The first stage of a pipeline must contain only source actions.'
            matches.append(RuleMatch(path + [0, 'Name'], self._format_error_message(message, scenario)))
        return matches

    def check_source_actions(self, stages, path, scenario):
        """Validate the all of the stages."""
        matches = []
        categories = set()
        if len(stages) < 1:
            self.logger.debug('Stages was empty. Should have been caught by generic linting.')
            return matches
        for sidx, stage in enumerate(stages):
            for aidx, action in enumerate(stage.get('Actions', [])):
                action_type_id = action.get('ActionTypeId')
                categories.add(action_type_id.get('Category'))
                if sidx > 0 and action_type_id.get('Category') == 'Source':
                    message = 'Only the first stage of a pipeline may contain source actions.'
                    matches.append(RuleMatch(path + [sidx, 'Actions', aidx], self._format_error_message(message, scenario)))
        if not categories - set(['Source']):
            message = 'At least one stage in pipeline must contain an action that is not a source action.'
            matches.append(RuleMatch(path, self._format_error_message(message, scenario)))
        return matches

    def check_names_unique(self, value, path, scenario):
        """Check that stage names are unique."""
        matches = []
        stage_names = set()
        for sidx, stage in enumerate(value):
            stage_name = stage.get('Name')
            if isinstance(stage_name, six.string_types):
                if stage_name in stage_names:
                    message = 'All stage names within a pipeline must be unique. ({name})'.format(name=stage_name)
                    matches.append(RuleMatch(path + [sidx, 'Name'], self._format_error_message(message, scenario)))
                stage_names.add(stage_name)
            else:
                self.logger.debug('Found non string for stage name: %s', stage_name)
        return matches

    def match(self, cfn):
        """Check CodePipeline stages"""
        matches = []
        resources = cfn.get_resource_properties(['AWS::CodePipeline::Pipeline'])
        for resource in resources:
            path = resource['Path'] + ['Stages']
            properties = resource['Value']
            s_stages = cfn.get_object_without_nested_conditions(properties.get('Stages'), path)
            for s_stage in s_stages:
                s_stage_obj = s_stage.get('Object')
                s_scenario = s_stage.get('Scenario')
                if not isinstance(s_stage_obj, list):
                    self.logger.debug('Stages not list. Should have been caught by generic linting.')
                    continue
                try:
                    matches.extend(self.check_stage_count(s_stage_obj, path, s_scenario))
                    matches.extend(self.check_first_stage(s_stage_obj, path, s_scenario))
                    matches.extend(self.check_source_actions(s_stage_obj, path, s_scenario))
                    matches.extend(self.check_names_unique(s_stage_obj, path, s_scenario))
                except AttributeError as err:
                    self.logger.debug('Got AttributeError. Should have been caught by generic linting. Ignoring the error here: %s', str(err))
        return matches


decode

Prompt:
Implement the Python class `WORD` described below.

Class description:
Class to implement WORD datatype of CIP especification. Methods ------- class encode class decode classmethod validate_range classmethod GetIDCode staticmethod Identify classmethod set_flag classmethod get_flag

Method signatures and docstrings:
- def encode(cls, value): encode a value in a byte array Parameters ----------- value: int range from -2^63 to 2^63-1 Value to encode Return ------- Byte Array -- encoded value in a byte array to send trough a network
- def decode(cls, buffer): decode a value from a byte array Parameters ----------- buffer: bytes buffer to decode Return ------- value : int encoded value in the byte array received
- def set_flag(cls, value, offset=0, flag=True): set the boolean flag value in a byte's offset position Parameters ----------- value: int range from 0 to 0xFFFF offset: int range 0 to 15 flag : boolean Return ------- return int
- def get_flag(cls, value, offset=0): get the boolean value in a byte's offset position Parameters ----------- value: int range from 0 to 0xFFFF offset: int range 0 to 15 Return ------- return int

Implement the Python class `WORD` described below.

Class description:
Class to implement WORD datatype of CIP especification. Methods ------- class encode class decode classmethod validate_range classmethod GetIDCode staticmethod Identify classmethod set_flag classmethod get_flag

Method signatures and docstrings:
- def encode(cls, value): encode a value in a byte array Parameters ----------- value: int range from -2^63 to 2^63-1 Value to encode Return ------- Byte Array -- encoded value in a byte array to send trough a network
- def decode(cls, buffer): decode a value from a byte array Parameters ----------- buffer: bytes buffer to decode Return ------- value : int encoded value in the byte array received
- def set_flag(cls, value, offset=0, flag=True): set the boolean flag value in a byte's offset position Parameters ----------- value: int range from 0 to 0xFFFF offset: int range 0 to 15 flag : boolean Return ------- return int
- def get_flag(cls, value, offset=0): get the boolean value in a byte's offset position Parameters ----------- value: int range from 0 to 0xFFFF offset: int range 0 to 15 Return ------- return int

<|skeleton|>
class WORD:
    """Class to implement WORD datatype of CIP especification. Methods ------- class encode class decode classmethod validate_range classmethod GetIDCode staticmethod Identify classmethod set_flag classmethod get_flag"""

    def encode(cls, value):
        """encode a value in a byte array Parameters ----------- value: int range from -2^63 to 2^63-1 Value to encode Return ------- Byte Array -- encoded value in a byte array to send trough a network"""
        <|body_0|>

    def decode(cls, buffer):
        """decode a value from a byte array Parameters ----------- buffer: bytes buffer to decode Return ------- value : int encoded value in the byte array received"""
        <|body_1|>

    def set_flag(cls, value, offset=0, flag=True):
        """set the boolean flag value in a byte's offset position Parameters ----------- value: int range from 0 to 0xFFFF offset: int range 0 to 15 flag : boolean Return ------- return int"""
        <|body_2|>

    def get_flag(cls, value, offset=0):
        """get the boolean value in a byte's offset position Parameters ----------- value: int range from 0 to 0xFFFF offset: int range 0 to 15 Return ------- return int"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        if isinstance(value, int):
            buffer = None
            if cls.validate_range(value):
                buffer = value.to_bytes(2, 'little')
                return buffer
            else:
                raise ValueError('value is not in valid cip range')
        else:
            raise TypeError('value must be int')
<|end_body_0|>

<|body_start_1|>
        if isinstance(buffer, bytes):
            value = None
            if len(buffer) == 2:
                value = int.from_bytes(buffer, 'little', signed=False)
                return value
            else:
                raise ValueError('buffer length mitsmatch with USINT encoding')
        else:
            raise TypeError('buffer must be bytes')
<|end_body_1|>

<|body_start_2|>
        if isinstance(value, int) and isinstance(offset, int) and isinstance(flag, bool):
            if cls.validate_range(value):
                buffer = bytearray(value.to_bytes(2, 'little'))
                if offset >= 0 and offset <= 7:
                    buffer[0] = BYTE.set_flag(buffer[0], offset, flag)
                    return int.from_bytes(buffer, 'little', signed=False)
                elif offset >= 8 and offset <= 15:
                    buffer[1] = BYTE.set_flag(buffer[1], offset - 8, flag)
                    return int.from_bytes(buffer, 'little', signed=False)
                else:
                    raise ValueError('offset is nat in valid range')
            else:
                raise ValueError('value is not in valid cip range')
        else:
            raise TypeError('value must be int')
<|end_body_2|>

<|body_start_3|>
        if isinstance(value, int) and isinstance(offset, int):
            if cls.validate_range(value):
                buffer = value.to_bytes(2, 'little')
                if offset >= 0 and offset <= 7:
                    return BYTE.get_flag(buffer[0], offset)
                elif offset >= 8 and offset <= 15:
                    return BYTE.get_flag(buffer[1], offset - 8)
                else:
                    raise ValueError('offset is nat in valid range')
            else:
                raise ValueError('value is not in valid cip range')
        else:
            raise TypeError('value must be int')
<|end_body_3|>

Solution:
class WORD:
    """Class to implement WORD datatype of CIP especification. Methods ------- class encode class decode classmethod validate_range classmethod GetIDCode staticmethod Identify classmethod set_flag classmethod get_flag"""

    def encode(cls, value):
        """encode a value in a byte array Parameters ----------- value: int range from -2^63 to 2^63-1 Value to encode Return ------- Byte Array -- encoded value in a byte array to send trough a network"""
        if isinstance(value, int):
            buffer = None
            if cls.validate_range(value):
                buffer = value.to_bytes(2, 'little')
                return buffer
            else:
                raise ValueError('value is not in valid cip range')
        else:
            raise TypeError('value must be int')

    def decode(cls, buffer):
        """decode a value from a byte array Parameters ----------- buffer: bytes buffer to decode Return ------- value : int encoded value in the byte array received"""
        if isinstance(buffer, bytes):
            value = None
            if len(buffer) == 2:
                value = int.from_bytes(buffer, 'little', signed=False)
                return value
            else:
                raise ValueError('buffer length mitsmatch with USINT encoding')
        else:
            raise TypeError('buffer must be bytes')

    def set_flag(cls, value, offset=0, flag=True):
        """set the boolean flag value in a byte's offset position Parameters ----------- value: int range from 0 to 0xFFFF offset: int range 0 to 15 flag : boolean Return ------- return int"""
        if isinstance(value, int) and isinstance(offset, int) and isinstance(flag, bool):
            if cls.validate_range(value):
                buffer = bytearray(value.to_bytes(2, 'little'))
                if offset >= 0 and offset <= 7:
                    buffer[0] = BYTE.set_flag(buffer[0], offset, flag)
                    return int.from_bytes(buffer, 'little', signed=False)
                elif offset >= 8 and offset <= 15:
                    buffer[1] = BYTE.set_flag(buffer[1], offset - 8, flag)
                    return int.from_bytes(buffer, 'little', signed=False)
                else:
                    raise ValueError('offset is nat in valid range')
            else:
                raise ValueError('value is not in valid cip range')
        else:
            raise TypeError('value must be int')

    def get_flag(cls, value, offset=0):
        """get the boolean value in a byte's offset position Parameters ----------- value: int range from 0 to 0xFFFF offset: int range 0 to 15 Return ------- return int"""
        if isinstance(value, int) and isinstance(offset, int):
            if cls.validate_range(value):
                buffer = value.to_bytes(2, 'little')
                if offset >= 0 and offset <= 7:
                    return BYTE.get_flag(buffer[0], offset)
                elif offset >= 8 and offset <= 15:
                    return BYTE.get_flag(buffer[1], offset - 8)
                else:
                    raise ValueError('offset is nat in valid range')
            else:
                raise ValueError('value is not in valid cip range')
        else:
            raise TypeError('value must be int')


resolve

Prompt:
Implement the Python class `PIDSlugFieldContext` described below.

Class description:
PID Slug Field Context.

Method signatures and docstrings:
- def parse_pid(self, value): Parse pid.
- def resolve(self, pid_value, registered_only=True): Resolve identifier (either uuid or slug).

Implement the Python class `PIDSlugFieldContext` described below.

Class description:
PID Slug Field Context.

Method signatures and docstrings:
- def parse_pid(self, value): Parse pid.
- def resolve(self, pid_value, registered_only=True): Resolve identifier (either uuid or slug).

<|skeleton|>
class PIDSlugFieldContext:
    """PID Slug Field Context."""

    def parse_pid(self, value):
        """Parse pid."""
        <|body_0|>

    def resolve(self, pid_value, registered_only=True):
        """Resolve identifier (either uuid or slug)."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if isinstance(value, UUID):
            return value
        try:
            return UUID(value)
        except (TypeError, ValueError) as e:
            return value
<|end_body_0|>

<|body_start_1|>
        pid_value = self.parse_pid(pid_value)
        if isinstance(pid_value, UUID):
            field_name = self.field._id_field
        else:
            field_name = self.field._slug_field
            if not pid_value:
                raise PIDDoesNotExistError('comid', '')
        with db.session.no_autoflush:
            model = self.record_cls.model_cls.query.filter_by(**{field_name: pid_value}).one_or_none()
            if model is None:
                raise PIDDoesNotExistError('comid', str(pid_value))
            record = self.record_cls(model.data, model=model)
            if record.is_deleted:
                raise PIDDeletedError(PersistentIdentifierWrapper(pid_value), record)
            return record
<|end_body_1|>

Solution:
class PIDSlugFieldContext:
    """PID Slug Field Context."""

    def parse_pid(self, value):
        """Parse pid."""
        if isinstance(value, UUID):
            return value
        try:
            return UUID(value)
        except (TypeError, ValueError) as e:
            return value

    def resolve(self, pid_value, registered_only=True):
        """Resolve identifier (either uuid or slug)."""
        pid_value = self.parse_pid(pid_value)
        if isinstance(pid_value, UUID):
            field_name = self.field._id_field
        else:
            field_name = self.field._slug_field
            if not pid_value:
                raise PIDDoesNotExistError('comid', '')
        with db.session.no_autoflush:
            model = self.record_cls.model_cls.query.filter_by(**{field_name: pid_value}).one_or_none()
            if model is None:
                raise PIDDoesNotExistError('comid', str(pid_value))
            record = self.record_cls(model.data, model=model)
            if record.is_deleted:
                raise PIDDeletedError(PersistentIdentifierWrapper(pid_value), record)
            return record


Prompt:
Implement the Python class `ResidObject` described below.

Class description:
Implement the ResidObject class.

Method signatures and docstrings:
- def __init__(self, initp=[], resid_in=None, psf_in=None): A model for the object on sky, consisting of a point source and a map that has been convolved with the PSF map. The idea is that, iteratively, the fit residuals can be added to to the input residuals to last model residuals, and the final problem is a standard deconvolution problem with a known PSF. Parameters ---------- initp: array-like A single parameter, the relative flux of the resolved part of the image. resid_in: numpy array Residuals from the previous iteration. Same size and format as the input image, but with N down and E left when displayed with imshow. psf_in: numpy array The PSF that should be used for the residuals, weighted in the same way.
- def model_uv(self, p_in, uv): Return a model of the Fourier transform of the object given a set of points in the uv plane Parameters ---------- p_in: array-like model parameters. Can be None if if the model has no parameters! uv: array-like Coordinates in the uv plane

Implement the Python class `ResidObject` described below.

Class description:
Implement the ResidObject class.

Method signatures and docstrings:
- def __init__(self, initp=[], resid_in=None, psf_in=None): A model for the object on sky, consisting of a point source and a map that has been convolved with the PSF map. The idea is that, iteratively, the fit residuals can be added to to the input residuals to last model residuals, and the final problem is a standard deconvolution problem with a known PSF. Parameters ---------- initp: array-like A single parameter, the relative flux of the resolved part of the image. resid_in: numpy array Residuals from the previous iteration. Same size and format as the input image, but with N down and E left when displayed with imshow. psf_in: numpy array The PSF that should be used for the residuals, weighted in the same way.
- def model_uv(self, p_in, uv): Return a model of the Fourier transform of the object given a set of points in the uv plane Parameters ---------- p_in: array-like model parameters. Can be None if if the model has no parameters! uv: array-like Coordinates in the uv plane

<|skeleton|>
class ResidObject:

    def __init__(self, initp=[], resid_in=None, psf_in=None):
        """A model for the object on sky, consisting of a point source and a map that has been convolved with the PSF map. The idea is that, iteratively, the fit residuals can be added to to the input residuals to last model residuals, and the final problem is a standard deconvolution problem with a known PSF. Parameters ---------- initp: array-like A single parameter, the relative flux of the resolved part of the image. resid_in: numpy array Residuals from the previous iteration. Same size and format as the input image, but with N down and E left when displayed with imshow. psf_in: numpy array The PSF that should be used for the residuals, weighted in the same way."""
        <|body_0|>

    def model_uv(self, p_in, uv):
        """Return a model of the Fourier transform of the object given a set of points in the uv plane Parameters ---------- p_in: array-like model parameters. Can be None if if the model has no parameters! uv: array-like Coordinates in the uv plane"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.p = initp
        self.np = len(initp)
        im = resid_in.copy()
        im /= np.sum(im)
        mean_psf = psf_in.copy()
        mean_psf /= np.sum(mean_psf)
        if im.shape[0] != im.shape[1]:
            raise ValueError('Model Image must be square')
        if im.shape != mean_psf.shape:
            raise ValueError('PSF must have the same shape as input residuals.')
        self.mod_ft = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(im))) / np.fft.fftshift(np.fft.fft2(np.fft.fftshift(mean_psf)))
        uv_coord = 2 * np.pi * (np.arange(im.shape[0]) - im.shape[0] // 2) / float(im.shape[0])
        self.mod_ft_realfunc = RectBivariateSpline(uv_coord, uv_coord, self.mod_ft.real, kx=1, ky=1)
        self.mod_ft_imagfunc = RectBivariateSpline(uv_coord, uv_coord, self.mod_ft.imag, kx=1, ky=1)
<|end_body_0|>

<|body_start_1|>
        ret_array = self.mod_ft_realfunc(uv[0], uv[1], grid=False).astype(np.complex)
        ret_array += 1j * self.mod_ft_imagfunc(uv[0], uv[1], grid=False)
        return p_in[0] * ret_array + (1 - p_in[0])
<|end_body_1|>

Solution:
class ResidObject:
    def __init__(self, initp=[], resid_in=None, psf_in=None):
        """A model for the object on sky, consisting of a point source and a map that has been convolved with the PSF map. The idea is that, iteratively, the fit residuals can be added to to the input residuals to last model residuals, and the final problem is a standard deconvolution problem with a known PSF. Parameters ---------- initp: array-like A single parameter, the relative flux of the resolved part of the image. resid_in: numpy array Residuals from the previous iteration. Same size and format as the input image, but with N down and E left when displayed with imshow. psf_in: numpy array The PSF that should be used for the residuals, weighted in the same way."""
        self.p = initp
        self.np = len(initp)
        im = resid_in.copy()
        im /= np.sum(im)
        mean_psf = psf_in.copy()
        mean_psf /= np.sum(mean_psf)
        if im.shape[0] != im.shape[1]:
            raise ValueError('Model Image must be square')
        if im.shape != mean_psf.shape:
            raise ValueError('PSF must have the same shape as input residuals.')
        self.mod_ft = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(im))) / np.fft.fftshift(np.fft.fft2(np.fft.fftshift(mean_psf)))
        uv_coord = 2 * np.pi * (np.arange(im.shape[0]) - im.shape[0] // 2) / float(im.shape[0])
        self.mod_ft_realfunc = RectBivariateSpline(uv_coord, uv_coord, self.mod_ft.real, kx=1, ky=1)
        self.mod_ft_imagfunc = RectBivariateSpline(uv_coord, uv_coord, self.mod_ft.imag, kx=1, ky=1)

    def model_uv(self, p_in, uv):
        """Return a model of the Fourier transform of the object given a set of points in the uv plane Parameters ---------- p_in: array-like model parameters. Can be None if if the model has no parameters! uv: array-like Coordinates in the uv plane"""
        ret_array = self.mod_ft_realfunc(uv[0], uv[1], grid=False).astype(np.complex)
        ret_array += 1j * self.mod_ft_imagfunc(uv[0], uv[1], grid=False)
        return p_in[0] * ret_array + (1 - p_in[0])


multiplicity

Prompt:
Implement the Python class `SchemeMorphism_point_affine_field` described below.

Class description:
Implement the SchemeMorphism_point_affine_field class.

Method signatures and docstrings:
- def weil_restriction(self): Compute the Weil restriction of this point over some extension field. If the field is a finite field, then this computes the Weil restriction to the prime subfield. A Weil restriction of scalars - denoted `Res_{L/k}` - is a functor which, for any finite extension of fields `L/k` and any algebraic variety `X` over `L`, produces another corresponding variety `Res_{L/k}(X)`, defined over `k`. It is useful for reducing questions about varieties over large fields to questions about more complicated varieties over smaller fields. This functor applied to a point gives the equivalent point on the Weil restriction of its codomain. OUTPUT: Scheme point on the Weil restriction of the codomain of this p
- def intersection_multiplicity(self, X): Return the intersection multiplicity of the codomain of this point and ``X`` at this point. This uses the intersection_multiplicity implementations for projective/affine subschemes. This point must be a point on an affine subscheme. INPUT: - ``X`` -- a subscheme in the same ambient space as that of the codomain of this point. OUTPUT: Integer. EXAMPLES:: sage: A.<x,y> = AffineSpace(GF(17), 2) sage: X = A.subscheme([y^2 - x^3 + 2*x^2 - x]) sage: Y = A.subscheme([y - 2*x + 2]) sage: Q1 = Y([1,0]) sage: Q1.intersection_multiplicity(X) 2 sage: Q2 = X([4,6]) sage: Q2.intersection_multiplicity(Y) 1 :: sage: A.<x,y,z,w> = AffineSpace(QQ, 4) sage: X = A.subscheme([x^2 - y*z^2, z - 2*w^2]) sage: Q = A
- def multiplicity(self): Return the multiplicity of this point on its codomain. Uses the subscheme multiplicity implementation. This point must be a point on an affine subscheme. OUTPUT: an integer. EXAMPLES:: sage: A.<x,y,z> = AffineSpace(QQ, 3) sage: X = A.subscheme([y^2 - x^7*z]) sage: Q1 = X([1,1,1]) sage: Q1.multiplicity() 1 sage: Q2 = X([0,0,2]) sage: Q2.multiplicity() 2

Implement the Python class `SchemeMorphism_point_affine_field` described below.

Class description:
Implement the SchemeMorphism_point_affine_field class.

Method signatures and docstrings:
- def weil_restriction(self): Compute the Weil restriction of this point over some extension field. If the field is a finite field, then this computes the Weil restriction to the prime subfield. A Weil restriction of scalars - denoted `Res_{L/k}` - is a functor which, for any finite extension of fields `L/k` and any algebraic variety `X` over `L`, produces another corresponding variety `Res_{L/k}(X)`, defined over `k`. It is useful for reducing questions about varieties over large fields to questions about more complicated varieties over smaller fields. This functor applied to a point gives the equivalent point on the Weil restriction of its codomain. OUTPUT: Scheme point on the Weil restriction of the codomain of this p
- def intersection_multiplicity(self, X): Return the intersection multiplicity of the codomain of this point and ``X`` at this point. This uses the intersection_multiplicity implementations for projective/affine subschemes. This point must be a point on an affine subscheme. INPUT: - ``X`` -- a subscheme in the same ambient space as that of the codomain of this point. OUTPUT: Integer. EXAMPLES:: sage: A.<x,y> = AffineSpace(GF(17), 2) sage: X = A.subscheme([y^2 - x^3 + 2*x^2 - x]) sage: Y = A.subscheme([y - 2*x + 2]) sage: Q1 = Y([1,0]) sage: Q1.intersection_multiplicity(X) 2 sage: Q2 = X([4,6]) sage: Q2.intersection_multiplicity(Y) 1 :: sage: A.<x,y,z,w> = AffineSpace(QQ, 4) sage: X = A.subscheme([x^2 - y*z^2, z - 2*w^2]) sage: Q = A
- def multiplicity(self): Return the multiplicity of this point on its codomain. Uses the subscheme multiplicity implementation. This point must be a point on an affine subscheme. OUTPUT: an integer. EXAMPLES:: sage: A.<x,y,z> = AffineSpace(QQ, 3) sage: X = A.subscheme([y^2 - x^7*z]) sage: Q1 = X([1,1,1]) sage: Q1.multiplicity() 1 sage: Q2 = X([0,0,2]) sage: Q2.multiplicity() 2

<|skeleton|>
class SchemeMorphism_point_affine_field:

    def weil_restriction(self):
        """Compute the Weil restriction of this point over some extension field. If the field is a finite field, then this computes the Weil restriction to the prime subfield. A Weil restriction of scalars - denoted `Res_{L/k}` - is a functor which, for any finite extension of fields `L/k` and any algebraic variety `X` over `L`, produces another corresponding variety `Res_{L/k}(X)`, defined over `k`. It is useful for reducing questions about varieties over large fields to questions about more complicated varieties over smaller fields. This functor applied to a point gives the equivalent point on the Weil restriction of its codomain. OUTPUT: Scheme point on the Weil restriction of the codomain of this p"""
        <|body_0|>

    def intersection_multiplicity(self, X):
        """Return the intersection multiplicity of the codomain of this point and ``X`` at this point. This uses the intersection_multiplicity implementations for projective/affine subschemes. This point must be a point on an affine subscheme. INPUT: - ``X`` -- a subscheme in the same ambient space as that of the codomain of this point. OUTPUT: Integer. EXAMPLES:: sage: A.<x,y> = AffineSpace(GF(17), 2) sage: X = A.subscheme([y^2 - x^3 + 2*x^2 - x]) sage: Y = A.subscheme([y - 2*x + 2]) sage: Q1 = Y([1,0]) sage: Q1.intersection_multiplicity(X) 2 sage: Q2 = X([4,6]) sage: Q2.intersection_multiplicity(Y) 1 :: sage: A.<x,y,z,w> = AffineSpace(QQ, 4) sage: X = A.subscheme([x^2 - y*z^2, z - 2*w^2]) sage: Q = A"""
        <|body_1|>

    def multiplicity(self):
        """Return the multiplicity of this point on its codomain. Uses the subscheme multiplicity implementation. This point must be a point on an affine subscheme. OUTPUT: an integer. EXAMPLES:: sage: A.<x,y,z> = AffineSpace(QQ, 3) sage: X = A.subscheme([y^2 - x^7*z]) sage: Q1 = X([1,1,1]) sage: Q1.multiplicity() 1 sage: Q2 = X([0,0,2]) sage: Q2.multiplicity() 2"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        L = self.codomain().base_ring()
        WR = self.codomain().weil_restriction()
        if L.is_finite():
            d = L.degree()
            if d == 1:
                return self
            newP = []
            for t in self:
                c = t.polynomial().coefficients(sparse=False)
                c = c + (d - len(c)) * [0]
                newP += c
        else:
            d = L.relative_degree()
            if d == 1:
                return self
            from sage.rings.number_field.number_field_element import CoordinateFunction
            v = L.gen()
            V, from_V, to_V = L.relative_vector_space()
            h = L(1)
            B = [to_V(h)]
            f = v.minpoly()
            for i in range(f.degree() - 1):
                h *= v
                B.append(to_V(h))
            W = V.span_of_basis(B)
            p = CoordinateFunction(v, W, to_V)
            newP = []
            for t in self:
                newP += p(t)
        return WR(newP)
<|end_body_0|>

<|body_start_1|>
        from sage.schemes.affine.affine_space import is_AffineSpace
        if is_AffineSpace(self.codomain()):
            raise TypeError('this point must be a point on an affine subscheme')
        return self.codomain().intersection_multiplicity(X, self)
<|end_body_1|>

<|body_start_2|>
        from sage.schemes.affine.affine_space import is_AffineSpace
        if is_AffineSpace(self.codomain()):
            raise TypeError('this point must be a point on an affine subscheme')
        return self.codomain().multiplicity(self)
<|end_body_2|>

Solution:
class SchemeMorphism_point_affine_field:
    def weil_restriction(self):
        """Compute the Weil restriction of this point over some extension field. If the field is a finite field, then this computes the Weil restriction to the prime subfield. A Weil restriction of scalars - denoted `Res_{L/k}` - is a functor which, for any finite extension of fields `L/k` and any algebraic variety `X` over `L`, produces another corresponding variety `Res_{L/k}(X)`, defined over `k`. It is useful for reducing questions about varieties over large fields to questions about more complicated varieties over smaller fields. This functor applied to a point gives the equivalent point on the Weil restriction of its codomain. OUTPUT: Scheme point on the Weil restriction of the codomain of this p"""
        L = self.codomain().base_ring()
        WR = self.codomain().weil_restriction()
        if L.is_finite():
            d = L.degree()
            if d == 1:
                return self
            newP = []
            for t in self:
                c = t.polynomial().coefficients(sparse=False)
                c = c + (d - len(c)) * [0]
                newP += c
        else:
            d = L.relative_degree()
            if d == 1:
                return self
            from sage.rings.number_field.number_field_element import CoordinateFunction
            v = L.gen()
            V, from_V, to_V = L.relative_vector_space()
            h = L(1)
            B = [to_V(h)]
            f = v.minpoly()
            for i in range(f.degree() - 1):
                h *= v
                B.append(to_V(h))
            W = V.span_of_basis(B)
            p = CoordinateFunction(v, W, to_V)
            newP = []
            for t in self:
                newP += p(t)
        return WR(newP)

    def intersection_multiplicity(self, X):
        """Return the intersection multiplicity of the codomain of this point and ``X`` at this point. This uses the intersection_multiplicity implementations for projective/affine subschemes. This point must be a point on an affine subscheme. INPUT: - ``X`` -- a subscheme in the same ambient space as that of the codomain of this point. OUTPUT: Integer. EXAMPLES:: sage: A.<x,y> = AffineSpace(GF(17), 2) sage: X = A.subscheme([y^2 - x^3 + 2*x^2 - x]) sage: Y = A.subscheme([y - 2*x + 2]) sage: Q1 = Y([1,0]) sage: Q1.intersection_multiplicity(X) 2 sage: Q2 = X([4,6]) sage: Q2.intersection_multiplicity(Y) 1 :: sage: A.<x,y,z,w> = AffineSpace(QQ, 4) sage: X = A.subscheme([x^2 - y*z^2, z - 2*w^2]) sage: Q = A"""
        from sage.schemes.affine.affine_space import is_AffineSpace
        if is_AffineSpace(self.codomain()):
            raise TypeError('this point must be a point on an affine subscheme')
        return self.codomain().intersection_multiplicity(X, self)

    def multiplicity(self):
        """Return the multiplicity of this point on its codomain. Uses the subscheme multiplicity implementation. This point must be a point on an affine subscheme. OUTPUT: an integer. EXAMPLES:: sage: A.<x,y,z> = AffineSpace(QQ, 3) sage: X = A.subscheme([y^2 - x^7*z]) sage: Q1 = X([1,1,1]) sage: Q1.multiplicity() 1 sage: Q2 = X([0,0,2]) sage: Q2.multiplicity() 2"""
        from sage.schemes.affine.affine_space import is_AffineSpace
        if is_AffineSpace(self.codomain()):
            raise TypeError('this point must be a point on an affine subscheme')
        return self.codomain().multiplicity(self)


forward

Prompt:
Implement the Python class `MLPerceptron` described below.

Class description:
a simple perceptron based classifier

Method signatures and docstrings:
- def __init__(self, input_dim, hidden_dim, output_dim): Args: input_dim (int): the size of the input feature vector hidden_dim (int): the size of the hidden feature vector output_dim (int): the size of the output feature vector
- def forward(self, x_in, apply_softmax=False): The forward pass of the classifier Args: x_in (torch.Tensor): an input data tensor. x_in.shape should be (batch, input_dim) apply_sigmoid (bool): a flag for the sigmoid activation should be false if used with the Cross Entropy losses Returns: the resulting tensor. tensor.shape should be (batch,)

Implement the Python class `MLPerceptron` described below.

Class description:
a simple perceptron based classifier

Method signatures and docstrings:
- def __init__(self, input_dim, hidden_dim, output_dim): Args: input_dim (int): the size of the input feature vector hidden_dim (int): the size of the hidden feature vector output_dim (int): the size of the output feature vector
- def forward(self, x_in, apply_softmax=False): The forward pass of the classifier Args: x_in (torch.Tensor): an input data tensor. x_in.shape should be (batch, input_dim) apply_sigmoid (bool): a flag for the sigmoid activation should be false if used with the Cross Entropy losses Returns: the resulting tensor. tensor.shape should be (batch,)

<|skeleton|>
class MLPerceptron:
    """a simple perceptron based classifier"""

    def __init__(self, input_dim, hidden_dim, output_dim):
        """Args: input_dim (int): the size of the input feature vector hidden_dim (int): the size of the hidden feature vector output_dim (int): the size of the output feature vector"""
        <|body_0|>

    def forward(self, x_in, apply_softmax=False):
        """The forward pass of the classifier Args: x_in (torch.Tensor): an input data tensor. x_in.shape should be (batch, input_dim) apply_sigmoid (bool): a flag for the sigmoid activation should be false if used with the Cross Entropy losses Returns: the resulting tensor. tensor.shape should be (batch,)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(MLPerceptron, self).__init__()
        self.fc1 = nn.Linear(in_features=input_dim, out_features=hidden_dim)
        self.fc2 = nn.Linear(in_features=hidden_dim, out_features=output_dim)
<|end_body_0|>

<|body_start_1|>
        intermediate_vector = F.relu(self.fc1(x_in))
        prediciton_vector = self.fc2(F.dropout(intermediate_vector, p=0.2))
        if apply_softmax:
            prediciton_vector = F.softmax(prediciton_vector, dim=1)
        return prediciton_vector
<|end_body_1|>

Solution:
class MLPerceptron:
    """a simple perceptron based classifier"""

    def __init__(self, input_dim, hidden_dim, output_dim):
        """Args: input_dim (int): the size of the input feature vector hidden_dim (int): the size of the hidden feature vector output_dim (int): the size of the output feature vector"""
        super(MLPerceptron, self).__init__()
        self.fc1 = nn.Linear(in_features=input_dim, out_features=hidden_dim)
        self.fc2 = nn.Linear(in_features=hidden_dim, out_features=output_dim)

    def forward(self, x_in, apply_softmax=False):
        """The forward pass of the classifier Args: x_in (torch.Tensor): an input data tensor. x_in.shape should be (batch, input_dim) apply_sigmoid (bool): a flag for the sigmoid activation should be false if used with the Cross Entropy losses Returns: the resulting tensor. tensor.shape should be (batch,)"""
        intermediate_vector = F.relu(self.fc1(x_in))
        prediciton_vector = self.fc2(F.dropout(intermediate_vector, p=0.2))
        if apply_softmax:
            prediciton_vector = F.softmax(prediciton_vector, dim=1)
        return prediciton_vector


post

Prompt:
Implement the Python class `DeleteOperatorFavAPIView` described below.

Class description:
Implement the DeleteOperatorFavAPIView class.

Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Delete a Fav set date_deleted = now
- def post(self, request, *args, **kwargs): Delete several Favs at a time set date_deleted = now

Implement the Python class `DeleteOperatorFavAPIView` described below.

Class description:
Implement the DeleteOperatorFavAPIView class.

Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Delete a Fav set date_deleted = now
- def post(self, request, *args, **kwargs): Delete several Favs at a time set date_deleted = now

<|skeleton|>
class DeleteOperatorFavAPIView:

    def get(self, request, *args, **kwargs):
        """Delete a Fav set date_deleted = now"""
        <|body_0|>

    def post(self, request, *args, **kwargs):
        """Delete several Favs at a time set date_deleted = now"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        favs = get_favs(request)
        favs = favs.filter(tour_operator__pk=self.kwargs.get('operator_pk'))
        if favs.exists():
            for fav in favs.all():
                fav.date_deleted = datetime.today()
                fav.save()
        return Response({'status': 'ok', 'count': get_favs_count(request), 'count_to': get_to_favs_count(request)})
<|end_body_0|>

<|body_start_1|>
        for fav_id in self.request.POST.getlist('favs[]'):
            favs = get_favs(self.request)
            favs = favs.filter(tour_operator__pk=fav_id)
            if favs.exists():
                for fav in favs.all():
                    fav.date_deleted = datetime.today()
                    fav.save()
        return Response({'status': 'ok', 'count': get_favs_count(request), 'count_to': get_to_favs_count(request)})
<|end_body_1|>

Solution:
class DeleteOperatorFavAPIView:
    def get(self, request, *args, **kwargs):
        """Delete a Fav set date_deleted = now"""
        favs = get_favs(request)
        favs = favs.filter(tour_operator__pk=self.kwargs.get('operator_pk'))
        if favs.exists():
            for fav in favs.all():
                fav.date_deleted = datetime.today()
                fav.save()
        return Response({'status': 'ok', 'count': get_favs_count(request), 'count_to': get_to_favs_count(request)})

    def post(self, request, *args, **kwargs):
        """Delete several Favs at a time set date_deleted = now"""
        for fav_id in self.request.POST.getlist('favs[]'):
            favs = get_favs(self.request)
            favs = favs.filter(tour_operator__pk=fav_id)
            if favs.exists():
                for fav in favs.all():
                    fav.date_deleted = datetime.today()
                    fav.save()
        return Response({'status': 'ok', 'count': get_favs_count(request), 'count_to': get_to_favs_count(request)})


Prompt:
Implement the Python class `CreateMixin` described below.

Class description:
Add POST / route

Method signatures and docstrings:
- def post(self, **kwargs): --- tags: ["{tag_name}"] summary: Creates {class_model} requestBody: required: true content: application/json: schema: {schema_class} responses: 201: description: Created content: application/json: schema: {schema_class} 409: description: Duplicated key found content: application/json: schema: {schema_class}
- def _perform_create(self, data, **kwargs): Check for conflicts and create a new object Is is passed the data parsed by the marshmallow schema (it transform from raw post data to a JSON)

Implement the Python class `CreateMixin` described below.

Class description:
Add POST / route

Method signatures and docstrings:
- def post(self, **kwargs): --- tags: ["{tag_name}"] summary: Creates {class_model} requestBody: required: true content: application/json: schema: {schema_class} responses: 201: description: Created content: application/json: schema: {schema_class} 409: description: Duplicated key found content: application/json: schema: {schema_class}
- def _perform_create(self, data, **kwargs): Check for conflicts and create a new object Is is passed the data parsed by the marshmallow schema (it transform from raw post data to a JSON)

<|skeleton|>
class CreateMixin:
    """Add POST / route"""

    def post(self, **kwargs):
        """--- tags: ["{tag_name}"] summary: Creates {class_model} requestBody: required: true content: application/json: schema: {schema_class} responses: 201: description: Created content: application/json: schema: {schema_class} 409: description: Duplicated key found content: application/json: schema: {schema_class}"""
        <|body_0|>

    def _perform_create(self, data, **kwargs):
        """Check for conflicts and create a new object Is is passed the data parsed by the marshmallow schema (it transform from raw post data to a JSON)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        context = {'updating': False}
        data = self._parse_data(self._get_schema_instance(kwargs, context=context), flask.request)
        data.pop('id', None)
        created = self._perform_create(data, **kwargs)
        created.creator = g.user
        db.session.commit()
        return (self._dump(created, kwargs), 201)
<|end_body_0|>

<|body_start_1|>
        obj = self.model_class(**data)
        try:
            db.session.add(obj)
            db.session.commit()
        except sqlalchemy.exc.IntegrityError as ex:
            if not is_unique_constraint_violation(ex):
                raise
            db.session.rollback()
            conflict_obj = get_conflict_object(db.session, obj, data)
            if conflict_obj:
                flask.abort(409, ValidationError({'message': 'Existing value', 'object': self._get_schema_class()().dump(conflict_obj).data}))
            else:
                raise
        return obj
<|end_body_1|>

Solution:
class CreateMixin:
    """Add POST / route"""

    def post(self, **kwargs):
        """--- tags: ["{tag_name}"] summary: Creates {class_model} requestBody: required: true content: application/json: schema: {schema_class} responses: 201: description: Created content: application/json: schema: {schema_class} 409: description: Duplicated key found content: application/json: schema: {schema_class}"""
        context = {'updating': False}
        data = self._parse_data(self._get_schema_instance(kwargs, context=context), flask.request)
        data.pop('id', None)
        created = self._perform_create(data, **kwargs)
        created.creator = g.user
        db.session.commit()
        return (self._dump(created, kwargs), 201)

    def _perform_create(self, data, **kwargs):
        """Check for conflicts and create a new object Is is passed the data parsed by the marshmallow schema (it transform from raw post data to a JSON)"""
        obj = self.model_class(**data)
        try:
            db.session.add(obj)
            db.session.commit()
        except sqlalchemy.exc.IntegrityError as ex:
            if not is_unique_constraint_violation(ex):
                raise
            db.session.rollback()
            conflict_obj = get_conflict_object(db.session, obj, data)
            if conflict_obj:
                flask.abort(409, ValidationError({'message': 'Existing value', 'object': self._get_schema_class()().dump(conflict_obj).data}))
            else:
                raise
        return obj


Prompt:
Implement the Python class `LeakychBLSTMLayer` described below.

Class description:
a leaky ch BLSTM layer

Method signatures and docstrings:
- def __init__(self, num_units, layer_norm=False, recurrent_dropout=1.0, leak_factor=1.0): LeakyBLSTMLayer constructor Args: num_units: The number of units in the one directon layer_norm: whether layer normalization should be applied recurrent_dropout: the recurrent dropout keep probability leak_factor: the leak factor (if 1, there is no leakage)
- def __call__(self, inputs, sequence_length, scope=None): Create the variables and do the forward computation Args: inputs: the input to the layer as a [batch_size, max_length, dim] tensor sequence_length: the length of the input sequences as a [batch_size] tensor scope: The variable scope sets the namespace under which the variables created during this call will be stored. Returns: the output of the layer

Implement the Python class `LeakychBLSTMLayer` described below.

Class description:
a leaky ch BLSTM layer

Method signatures and docstrings:
- def __init__(self, num_units, layer_norm=False, recurrent_dropout=1.0, leak_factor=1.0): LeakyBLSTMLayer constructor Args: num_units: The number of units in the one directon layer_norm: whether layer normalization should be applied recurrent_dropout: the recurrent dropout keep probability leak_factor: the leak factor (if 1, there is no leakage)
- def __call__(self, inputs, sequence_length, scope=None): Create the variables and do the forward computation Args: inputs: the input to the layer as a [batch_size, max_length, dim] tensor sequence_length: the length of the input sequences as a [batch_size] tensor scope: The variable scope sets the namespace under which the variables created during this call will be stored. Returns: the output of the layer

<|skeleton|>
class LeakychBLSTMLayer:
    """a leaky ch BLSTM layer"""

    def __init__(self, num_units, layer_norm=False, recurrent_dropout=1.0, leak_factor=1.0):
        """LeakyBLSTMLayer constructor Args: num_units: The number of units in the one directon layer_norm: whether layer normalization should be applied recurrent_dropout: the recurrent dropout keep probability leak_factor: the leak factor (if 1, there is no leakage)"""
        <|body_0|>

    def __call__(self, inputs, sequence_length, scope=None):
        """Create the variables and do the forward computation Args: inputs: the input to the layer as a [batch_size, max_length, dim] tensor sequence_length: the length of the input sequences as a [batch_size] tensor scope: The variable scope sets the namespace under which the variables created during this call will be stored. Returns: the output of the layer"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.num_units = num_units
        self.layer_norm = layer_norm
        self.recurrent_dropout = recurrent_dropout
        self.leak_factor = leak_factor
<|end_body_0|>

<|body_start_1|>
        with tf.variable_scope(scope or type(self).__name__):
            lstm_cell_fw = rnn_cell.LayerNormBasicLeakchLSTMCell(num_units=self.num_units, leak_factor=self.leak_factor, layer_norm=self.layer_norm, dropout_keep_prob=self.recurrent_dropout, reuse=tf.get_variable_scope().reuse)
            lstm_cell_bw = rnn_cell.LayerNormBasicLeakchLSTMCell(self.num_units, leak_factor=self.leak_factor, layer_norm=self.layer_norm, dropout_keep_prob=self.recurrent_dropout, reuse=tf.get_variable_scope().reuse)
            outputs_tupple, _ = bidirectional_dynamic_rnn(lstm_cell_fw, lstm_cell_bw, inputs, dtype=tf.float32, sequence_length=sequence_length)
            outputs = tf.concat(outputs_tupple, 2)
            return outputs
<|end_body_1|>

Solution:
class LeakychBLSTMLayer:
    """a leaky ch BLSTM layer"""

    def __init__(self, num_units, layer_norm=False, recurrent_dropout=1.0, leak_factor=1.0):
        """LeakyBLSTMLayer constructor Args: num_units: The number of units in the one directon layer_norm: whether layer normalization should be applied recurrent_dropout: the recurrent dropout keep probability leak_factor: the leak factor (if 1, there is no leakage)"""
        self.num_units = num_units
        self.layer_norm = layer_norm
        self.recurrent_dropout = recurrent_dropout
        self.leak_factor = leak_factor

    def __call__(self, inputs, sequence_length, scope=None):
        """Create the variables and do the forward computation Args: inputs: the input to the layer as a [batch_size, max_length, dim] tensor sequence_length: the length of the input sequences as a [batch_size] tensor scope: The variable scope sets the namespace under which the variables created during this call will be stored. Returns: the output of the layer"""
        with tf.variable_scope(scope or type(self).__name__):
            lstm_cell_fw = rnn_cell.LayerNormBasicLeakchLSTMCell(num_units=self.num_units, leak_factor=self.leak_factor, layer_norm=self.layer_norm, dropout_keep_prob=self.recurrent_dropout, reuse=tf.get_variable_scope().reuse)
            lstm_cell_bw = rnn_cell.LayerNormBasicLeakchLSTMCell(self.num_units, leak_factor=self.leak_factor, layer_norm=self.layer_norm, dropout_keep_prob=self.recurrent_dropout, reuse=tf.get_variable_scope().reuse)
            outputs_tupple, _ = bidirectional_dynamic_rnn(lstm_cell_fw, lstm_cell_bw, inputs, dtype=tf.float32, sequence_length=sequence_length)
            outputs = tf.concat(outputs_tupple, 2)
            return outputs