post

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

Class description:
A class that represents the update page for an about page entry

Method signatures and docstrings:
- def get(self, request, slug): The get request to view the update page for an entry on the about page :return: an http response showing the aboutPerson form for creating new about entry
- def post(self, request, slug): The post request for updating an existing entry on the about page. :return: an http response that redirects to a new page if the entry update is successful

Implement the Python class `AboutUpdateView` described below.

Class description:
A class that represents the update page for an about page entry

Method signatures and docstrings:
- def get(self, request, slug): The get request to view the update page for an entry on the about page :return: an http response showing the aboutPerson form for creating new about entry
- def post(self, request, slug): The post request for updating an existing entry on the about page. :return: an http response that redirects to a new page if the entry update is successful

<|skeleton|>
class AboutUpdateView:
    """A class that represents the update page for an about page entry"""

    def get(self, request, slug):
        """The get request to view the update page for an entry on the about page :return: an http response showing the aboutPerson form for creating new about entry"""
        <|body_0|>

    def post(self, request, slug):
        """The post request for updating an existing entry on the about page. :return: an http response that redirects to a new page if the entry update is successful"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        user_prof = UserProfile.objects.get(user=request.user)
        try:
            about_person = AboutPerson.objects.get(slug=str(slug))
            form = AboutFunderForm(initial={'name': about_person.name, 'content': about_person.content, 'image': about_person.image, 'funder_or_adviser': about_person.funder_or_adviser})
            user_prof = UserProfile.objects.get(user=request.user)
            if not user_prof.user.email == 'relevate@outlook.com':
                return HttpResponseRedirect(reverse('contribution:home'))
            return render(request, 'about_update.html', {'form': form, 'slug': slug, 'user_prof': user_prof, 'first_name': request.user.first_name, 'last_name': request.user.last_name})
        except ObjectDoesNotExist:
            return HttpResponseRedirect(reverse('contribution:about'))
<|end_body_0|>

<|body_start_1|>
        user_prof = UserProfile.objects.get(user=request.user)
        about_person = AboutPerson.objects.get(slug=slug)
        form = AboutFunderForm(request.POST, request.FILES)
        if form.is_valid():
            x = form.cleaned_data.get('x')
            y = form.cleaned_data.get('y')
            w = form.cleaned_data.get('width')
            h = form.cleaned_data.get('height')
            image = form.cleaned_data.get('image')
            if user_prof.user.email == 'relevate@outlook.com':
                about_person.name = form.cleaned_data['name']
                about_person.content = form.cleaned_data.get('content')
                about_person.funder_or_adviser = form.cleaned_data.get('funder_or_adviser')
                if image:
                    photo = Image.open(form.cleaned_data.get('image'))
                    cropped_image = photo.crop((x, y, w + x, h + y))
                    filename, file_extension = os.path.splitext(os.path.basename(urlparse(about_person.image.url).path))
                    cropped_image.save(settings.BASE_DIR + '/media/about_person/image/' + filename + file_extension)
                    about_person.image = 'about_person/image/' + filename + file_extension
                about_person.save()
                messages.success(request, 'Funder or Advisor Was Successfully Added!')
                return HttpResponseRedirect(reverse_lazy('contribution:about'))
            else:
                return HttpResponseRedirect(reverse_lazy('contribution:home'))
        else:
            print('Invalid')
            display_error(form, request)
            return render(request, 'about_create.html', {'form': form, 'user_prof': user_prof})
<|end_body_1|>

Solution:
class AboutUpdateView:
    """A class that represents the update page for an about page entry"""

    def get(self, request, slug):
        """The get request to view the update page for an entry on the about page :return: an http response showing the aboutPerson form for creating new about entry"""
        user_prof = UserProfile.objects.get(user=request.user)
        try:
            about_person = AboutPerson.objects.get(slug=str(slug))
            form = AboutFunderForm(initial={'name': about_person.name, 'content': about_person.content, 'image': about_person.image, 'funder_or_adviser': about_person.funder_or_adviser})
            user_prof = UserProfile.objects.get(user=request.user)
            if not user_prof.user.email == 'relevate@outlook.com':
                return HttpResponseRedirect(reverse('contribution:home'))
            return render(request, 'about_update.html', {'form': form, 'slug': slug, 'user_prof': user_prof, 'first_name': request.user.first_name, 'last_name': request.user.last_name})
        except ObjectDoesNotExist:
            return HttpResponseRedirect(reverse('contribution:about'))

    def post(self, request, slug):
        """The post request for updating an existing entry on the about page. :return: an http response that redirects to a new page if the entry update is successful"""
        user_prof = UserProfile.objects.get(user=request.user)
        about_person = AboutPerson.objects.get(slug=slug)
        form = AboutFunderForm(request.POST, request.FILES)
        if form.is_valid():
            x = form.cleaned_data.get('x')
            y = form.cleaned_data.get('y')
            w = form.cleaned_data.get('width')
            h = form.cleaned_data.get('height')
            image = form.cleaned_data.get('image')
            if user_prof.user.email == 'relevate@outlook.com':
                about_person.name = form.cleaned_data['name']
                about_person.content = form.cleaned_data.get('content')
                about_person.funder_or_adviser = form.cleaned_data.get('funder_or_adviser')
                if image:
                    photo = Image.open(form.cleaned_data.get('image'))
                    cropped_image = photo.crop((x, y, w + x, h + y))
                    filename, file_extension = os.path.splitext(os.path.basename(urlparse(about_person.image.url).path))
                    cropped_image.save(settings.BASE_DIR + '/media/about_person/image/' + filename + file_extension)
                    about_person.image = 'about_person/image/' + filename + file_extension
                about_person.save()
                messages.success(request, 'Funder or Advisor Was Successfully Added!')
                return HttpResponseRedirect(reverse_lazy('contribution:about'))
            else:
                return HttpResponseRedirect(reverse_lazy('contribution:home'))
        else:
            print('Invalid')
            display_error(form, request)
            return render(request, 'about_create.html', {'form': form, 'user_prof': user_prof})


isBackbone

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

Class description:
Class DefinitionAtom The DefinitionAtom class inherits off the Atom class. It provides a trimmed down version of the initializating function from the Atom class for the definition files.

Method signatures and docstrings:
- def __init__(self, serial, name, resName, x, y, z): Initialize using a few basic parameters - set all other fields to null, which is necessary for debugging output by using the string function in the parent class. Parameters serial: Atom serial number (int) name: Atom name. (string) resName: Residue name. (string) resSeq: Residue sequence number. (int) x: Orthogonal coordinates for X in Angstroms. (float) y: Orthogonal coordinates for Y in Angstroms. (float) z: Orthogonal coordinates for Z in Angstroms. (float)
- def isBackbone(self): Return true if atom name is in backbone, otherwise false Returns state: 1 if true, 0 if false

Implement the Python class `DefinitionAtom` described below.

Class description:
Class DefinitionAtom The DefinitionAtom class inherits off the Atom class. It provides a trimmed down version of the initializating function from the Atom class for the definition files.

Method signatures and docstrings:
- def __init__(self, serial, name, resName, x, y, z): Initialize using a few basic parameters - set all other fields to null, which is necessary for debugging output by using the string function in the parent class. Parameters serial: Atom serial number (int) name: Atom name. (string) resName: Residue name. (string) resSeq: Residue sequence number. (int) x: Orthogonal coordinates for X in Angstroms. (float) y: Orthogonal coordinates for Y in Angstroms. (float) z: Orthogonal coordinates for Z in Angstroms. (float)
- def isBackbone(self): Return true if atom name is in backbone, otherwise false Returns state: 1 if true, 0 if false

<|skeleton|>
class DefinitionAtom:
    """Class DefinitionAtom The DefinitionAtom class inherits off the Atom class. It provides a trimmed down version of the initializating function from the Atom class for the definition files."""

    def __init__(self, serial, name, resName, x, y, z):
        """Initialize using a few basic parameters - set all other fields to null, which is necessary for debugging output by using the string function in the parent class. Parameters serial: Atom serial number (int) name: Atom name. (string) resName: Residue name. (string) resSeq: Residue sequence number. (int) x: Orthogonal coordinates for X in Angstroms. (float) y: Orthogonal coordinates for Y in Angstroms. (float) z: Orthogonal coordinates for Z in Angstroms. (float)"""
        <|body_0|>

    def isBackbone(self):
        """Return true if atom name is in backbone, otherwise false Returns state: 1 if true, 0 if false"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.type = 'ATOM'
        self.serial = serial
        self.name = name
        self.altLoc = ''
        self.resName = resName
        self.chainID = ''
        self.resSeq = 1
        self.iCode = ''
        self.x = x
        self.y = y
        self.z = z
        self.occupancy = 0.0
        self.tempFactor = 0.0
        self.segID = ''
        self.element = ''
        self.charge = ''
        self.ffcharge = 0.0
        self.radius = 0.0
        self.intrabonds = []
        self.residue = None
        self.refdistance = None
<|end_body_0|>

<|body_start_1|>
        state = 0
        if self.name in BACKBONE:
            state = 1
        return state
<|end_body_1|>

Solution:
class DefinitionAtom:
    """Class DefinitionAtom The DefinitionAtom class inherits off the Atom class. It provides a trimmed down version of the initializating function from the Atom class for the definition files."""

    def __init__(self, serial, name, resName, x, y, z):
        """Initialize using a few basic parameters - set all other fields to null, which is necessary for debugging output by using the string function in the parent class. Parameters serial: Atom serial number (int) name: Atom name. (string) resName: Residue name. (string) resSeq: Residue sequence number. (int) x: Orthogonal coordinates for X in Angstroms. (float) y: Orthogonal coordinates for Y in Angstroms. (float) z: Orthogonal coordinates for Z in Angstroms. (float)"""
        self.type = 'ATOM'
        self.serial = serial
        self.name = name
        self.altLoc = ''
        self.resName = resName
        self.chainID = ''
        self.resSeq = 1
        self.iCode = ''
        self.x = x
        self.y = y
        self.z = z
        self.occupancy = 0.0
        self.tempFactor = 0.0
        self.segID = ''
        self.element = ''
        self.charge = ''
        self.ffcharge = 0.0
        self.radius = 0.0
        self.intrabonds = []
        self.residue = None
        self.refdistance = None

    def isBackbone(self):
        """Return true if atom name is in backbone, otherwise false Returns state: 1 if true, 0 if false"""
        state = 0
        if self.name in BACKBONE:
            state = 1
        return state


minDepthIterate

迭代方式，层序遍历，遇到的第一个叶子节点就是最小深度的节点

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minDepth(self, root): :type root: TreeNode :rtype: int
- def minDepthIterate(self, root): 迭代方式，层序遍历，遇到的第一个叶子节点就是最小深度的节点

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minDepth(self, root): :type root: TreeNode :rtype: int
- def minDepthIterate(self, root): 迭代方式，层序遍历，遇到的第一个叶子节点就是最小深度的节点

<|skeleton|>
class Solution:

    def minDepth(self, root):
        """:type root: TreeNode :rtype: int"""
        <|body_0|>

    def minDepthIterate(self, root):
        """迭代方式，层序遍历，遇到的第一个叶子节点就是最小深度的节点"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not root:
            return 0
        left_subtree_depth = self.minDepth(root.left)
        right_subtree_depth = self.minDepth(root.right)
        return (min(left_subtree_depth, right_subtree_depth) or max(left_subtree_depth, right_subtree_depth)) + 1
<|end_body_0|>

<|body_start_1|>
        if not root:
            return 0
        queue = [root]
        min_depth = 0
        row_node_num = 1
        while row_node_num > 0:
            min_depth += 1
            while row_node_num > 0:
                current_node = queue.pop(0)
                if not current_node.left and (not current_node.right):
                    return min_depth
                if current_node.left:
                    queue.append(current_node.left)
                if current_node.right:
                    queue.append(current_node.right)
                row_node_num -= 1
            row_node_num = len(queue)
<|end_body_1|>

Solution:
class Solution:
    def minDepth(self, root):
        """:type root: TreeNode :rtype: int"""
        if not root:
            return 0
        left_subtree_depth = self.minDepth(root.left)
        right_subtree_depth = self.minDepth(root.right)
        return (min(left_subtree_depth, right_subtree_depth) or max(left_subtree_depth, right_subtree_depth)) + 1

    def minDepthIterate(self, root):
        """迭代方式，层序遍历，遇到的第一个叶子节点就是最小深度的节点"""
        if not root:
            return 0
        queue = [root]
        min_depth = 0
        row_node_num = 1
        while row_node_num > 0:
            min_depth += 1
            while row_node_num > 0:
                current_node = queue.pop(0)
                if not current_node.left and (not current_node.right):
                    return min_depth
                if current_node.left:
                    queue.append(current_node.left)
                if current_node.right:
                    queue.append(current_node.right)
                row_node_num -= 1
            row_node_num = len(queue)


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

Class description:
### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration, Medical Image Analysis (2018) https://doi.org/10.1016/j.media.2018.07.002 see also: https://github.com/YipengHu/label-reg ### Building blocks [GLOBAL] - INetAffine from interventional_affine_net.py [DENSE] - INetDense from intervetional_dense_net.py ### Diagram INPUT PAIR --> [GLOBAL] --> AFFINE FIELD --- DENSE + AFFINE FIELD | | -------> [DENSE] --> DENSE FIELD ------ ### Constrai

Method signatures and docstrings:
- def __init__(self, decay, affine_w_initializer=None, affine_b_initializer=None, disp_w_initializer=None, disp_b_initializer=None, acti_func='relu', interp='linear', boundary='replicate', name='inet-hybrid-two-stream'): :param decay: float, regularisation decay :param affine_w_initializer: weight initialisation for affine registration network :param affine_b_initializer: bias initialisation for affine registration network :param disp_w_initializer: weight initialisation for dense registration network :param disp_b_initializer: bias initialisation for dense registration network :param acti_func: activation function to use :param interp: string, type of interpolation for the resampling [default:linear] - not in use :param boundary: string, padding mode to deal with image boundary [default: replicate] - not is use :param name: layer name
- def layer_op(self, fixed_image, moving_image, is_training=True, **unused_kwargs): :param fixed_image: tensor, fixed image for registration (defines reference space) :param moving_image: tensor, moving image to be registered to fixed :param is_training: boolean, True if network is in training mode :param unused_kwargs: not in use :return: estimated total, dense and affine displacement fields

Implement the Python class `INetHybridTwoStream` described below.

Class description:
### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration, Medical Image Analysis (2018) https://doi.org/10.1016/j.media.2018.07.002 see also: https://github.com/YipengHu/label-reg ### Building blocks [GLOBAL] - INetAffine from interventional_affine_net.py [DENSE] - INetDense from intervetional_dense_net.py ### Diagram INPUT PAIR --> [GLOBAL] --> AFFINE FIELD --- DENSE + AFFINE FIELD | | -------> [DENSE] --> DENSE FIELD ------ ### Constrai

Method signatures and docstrings:
- def __init__(self, decay, affine_w_initializer=None, affine_b_initializer=None, disp_w_initializer=None, disp_b_initializer=None, acti_func='relu', interp='linear', boundary='replicate', name='inet-hybrid-two-stream'): :param decay: float, regularisation decay :param affine_w_initializer: weight initialisation for affine registration network :param affine_b_initializer: bias initialisation for affine registration network :param disp_w_initializer: weight initialisation for dense registration network :param disp_b_initializer: bias initialisation for dense registration network :param acti_func: activation function to use :param interp: string, type of interpolation for the resampling [default:linear] - not in use :param boundary: string, padding mode to deal with image boundary [default: replicate] - not is use :param name: layer name
- def layer_op(self, fixed_image, moving_image, is_training=True, **unused_kwargs): :param fixed_image: tensor, fixed image for registration (defines reference space) :param moving_image: tensor, moving image to be registered to fixed :param is_training: boolean, True if network is in training mode :param unused_kwargs: not in use :return: estimated total, dense and affine displacement fields

<|skeleton|>
class INetHybridTwoStream:
    """### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration, Medical Image Analysis (2018) https://doi.org/10.1016/j.media.2018.07.002 see also: https://github.com/YipengHu/label-reg ### Building blocks [GLOBAL] - INetAffine from interventional_affine_net.py [DENSE] - INetDense from intervetional_dense_net.py ### Diagram INPUT PAIR --> [GLOBAL] --> AFFINE FIELD --- DENSE + AFFINE FIELD | | -------> [DENSE] --> DENSE FIELD ------ ### Constrai"""

    def __init__(self, decay, affine_w_initializer=None, affine_b_initializer=None, disp_w_initializer=None, disp_b_initializer=None, acti_func='relu', interp='linear', boundary='replicate', name='inet-hybrid-two-stream'):
        """:param decay: float, regularisation decay :param affine_w_initializer: weight initialisation for affine registration network :param affine_b_initializer: bias initialisation for affine registration network :param disp_w_initializer: weight initialisation for dense registration network :param disp_b_initializer: bias initialisation for dense registration network :param acti_func: activation function to use :param interp: string, type of interpolation for the resampling [default:linear] - not in use :param boundary: string, padding mode to deal with image boundary [default: replicate] - not is use :param name: layer name"""
        <|body_0|>

    def layer_op(self, fixed_image, moving_image, is_training=True, **unused_kwargs):
        """:param fixed_image: tensor, fixed image for registration (defines reference space) :param moving_image: tensor, moving image to be registered to fixed :param is_training: boolean, True if network is in training mode :param unused_kwargs: not in use :return: estimated total, dense and affine displacement fields"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        BaseNet.__init__(self, name=name)
        self.global_net = INetAffine(decay=decay, affine_w_initializer=affine_w_initializer, affine_b_initializer=affine_b_initializer, acti_func=acti_func, name='inet-global')
        self.local_net = INetDense(decay=decay, disp_w_initializer=disp_w_initializer, disp_b_initializer=disp_b_initializer, acti_func=acti_func, name='inet-local')
        self.interp = interp
        self.boundary = boundary
<|end_body_0|>

<|body_start_1|>
        affine_field = self.global_net(fixed_image, moving_image, is_training)
        dense_field = self.local_net(fixed_image, moving_image, is_training)
        return (dense_field + affine_field, dense_field, affine_field)
<|end_body_1|>

Solution:
class INetHybridTwoStream:
    """### Description Re-implementation of the registration network proposed in: Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666 Hu et al., Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration, Medical Image Analysis (2018) https://doi.org/10.1016/j.media.2018.07.002 see also: https://github.com/YipengHu/label-reg ### Building blocks [GLOBAL] - INetAffine from interventional_affine_net.py [DENSE] - INetDense from intervetional_dense_net.py ### Diagram INPUT PAIR --> [GLOBAL] --> AFFINE FIELD --- DENSE + AFFINE FIELD | | -------> [DENSE] --> DENSE FIELD ------ ### Constrai"""

    def __init__(self, decay, affine_w_initializer=None, affine_b_initializer=None, disp_w_initializer=None, disp_b_initializer=None, acti_func='relu', interp='linear', boundary='replicate', name='inet-hybrid-two-stream'):
        """:param decay: float, regularisation decay :param affine_w_initializer: weight initialisation for affine registration network :param affine_b_initializer: bias initialisation for affine registration network :param disp_w_initializer: weight initialisation for dense registration network :param disp_b_initializer: bias initialisation for dense registration network :param acti_func: activation function to use :param interp: string, type of interpolation for the resampling [default:linear] - not in use :param boundary: string, padding mode to deal with image boundary [default: replicate] - not is use :param name: layer name"""
        BaseNet.__init__(self, name=name)
        self.global_net = INetAffine(decay=decay, affine_w_initializer=affine_w_initializer, affine_b_initializer=affine_b_initializer, acti_func=acti_func, name='inet-global')
        self.local_net = INetDense(decay=decay, disp_w_initializer=disp_w_initializer, disp_b_initializer=disp_b_initializer, acti_func=acti_func, name='inet-local')
        self.interp = interp
        self.boundary = boundary

    def layer_op(self, fixed_image, moving_image, is_training=True, **unused_kwargs):
        """:param fixed_image: tensor, fixed image for registration (defines reference space) :param moving_image: tensor, moving image to be registered to fixed :param is_training: boolean, True if network is in training mode :param unused_kwargs: not in use :return: estimated total, dense and affine displacement fields"""
        affine_field = self.global_net(fixed_image, moving_image, is_training)
        dense_field = self.local_net(fixed_image, moving_image, is_training)
        return (dense_field + affine_field, dense_field, affine_field)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def reverse(self, x): :type x: int :rtype: int
- def reverse2(self, x): :type x: int :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def reverse(self, x): :type x: int :rtype: int
- def reverse2(self, x): :type x: int :rtype: int

<|skeleton|>
class Solution:

    def reverse(self, x):
        """:type x: int :rtype: int"""
        <|body_0|>

    def reverse2(self, x):
        """:type x: int :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        res = 0
        if x < 0:
            symbol = -1
            x = -x
        else:
            symbol = 1
        while x:
            pop = x % 10
            x = x // 10
            res = res * 10 + pop
        return 0 if res > pow(2, 31) else res * symbol
<|end_body_0|>

<|body_start_1|>
        temp = str(x)
        if temp[0] != '-':
            temp = temp[::-1]
        else:
            temp2 = temp[1:]
            temp2 = temp2[::-1]
            temp = temp[0] + temp2
        return int(temp) if -2 ** 31 - 1 < int(temp) < 2 ** 31 else 0
<|end_body_1|>

Solution:
class Solution:
    def reverse(self, x):
        """:type x: int :rtype: int"""
        res = 0
        if x < 0:
            symbol = -1
            x = -x
        else:
            symbol = 1
        while x:
            pop = x % 10
            x = x // 10
            res = res * 10 + pop
        return 0 if res > pow(2, 31) else res * symbol

    def reverse2(self, x):
        """:type x: int :rtype: int"""
        temp = str(x)
        if temp[0] != '-':
            temp = temp[::-1]
        else:
            temp2 = temp[1:]
            temp2 = temp2[::-1]
            temp = temp[0] + temp2
        return int(temp) if -2 ** 31 - 1 < int(temp) < 2 ** 31 else 0


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

Class description:
Tests the clean_text() function.

Method signatures and docstrings:
- def test_clean_url(self): Tests the clean_text() function for text containing a URL.
- def test_clean_at(self): Tests the clean_text() function for text containing an @ symbol.
- def test_clean_hashtag(self): Tests the clean_text() function for text containing a hasttag.

Implement the Python class `CleanTextTestCase` described below.

Class description:
Tests the clean_text() function.

Method signatures and docstrings:
- def test_clean_url(self): Tests the clean_text() function for text containing a URL.
- def test_clean_at(self): Tests the clean_text() function for text containing an @ symbol.
- def test_clean_hashtag(self): Tests the clean_text() function for text containing a hasttag.

<|skeleton|>
class CleanTextTestCase:
    """Tests the clean_text() function."""

    def test_clean_url(self):
        """Tests the clean_text() function for text containing a URL."""
        <|body_0|>

    def test_clean_at(self):
        """Tests the clean_text() function for text containing an @ symbol."""
        <|body_1|>

    def test_clean_hashtag(self):
        """Tests the clean_text() function for text containing a hasttag."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        actual = clean_text('check out this link: https://cyphon.io')
        expected = 'check out this link'
        self.assertEqual(actual, expected)
<|end_body_0|>

<|body_start_1|>
        actual = clean_text('@foobar hey there')
        expected = 'hey there'
        self.assertEqual(actual, expected)
<|end_body_1|>

<|body_start_2|>
        actual = clean_text('#foobar hey there')
        expected = 'foobar hey there'
        self.assertEqual(actual, expected)
<|end_body_2|>

Solution:
class CleanTextTestCase:
    """Tests the clean_text() function."""

    def test_clean_url(self):
        """Tests the clean_text() function for text containing a URL."""
        actual = clean_text('check out this link: https://cyphon.io')
        expected = 'check out this link'
        self.assertEqual(actual, expected)

    def test_clean_at(self):
        """Tests the clean_text() function for text containing an @ symbol."""
        actual = clean_text('@foobar hey there')
        expected = 'hey there'
        self.assertEqual(actual, expected)

    def test_clean_hashtag(self):
        """Tests the clean_text() function for text containing a hasttag."""
        actual = clean_text('#foobar hey there')
        expected = 'foobar hey there'
        self.assertEqual(actual, expected)


select

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

Class description:
Implement the SelectPatch class.

Method signatures and docstrings:
- def dropdown(self, name, attributes=None, get_option_attributes=None): Monkey patch for FormBuild 3.0.3 bug
- def select(value, options, multiple, name, attributes=None, get_option_attributes=None, self=None): This is the same as formbuild.internal._select, with two changes.

Implement the Python class `SelectPatch` described below.

Class description:
Implement the SelectPatch class.

Method signatures and docstrings:
- def dropdown(self, name, attributes=None, get_option_attributes=None): Monkey patch for FormBuild 3.0.3 bug
- def select(value, options, multiple, name, attributes=None, get_option_attributes=None, self=None): This is the same as formbuild.internal._select, with two changes.

<|skeleton|>
class SelectPatch:

    def dropdown(self, name, attributes=None, get_option_attributes=None):
        """Monkey patch for FormBuild 3.0.3 bug"""
        <|body_0|>

    def select(value, options, multiple, name, attributes=None, get_option_attributes=None, self=None):
        """This is the same as formbuild.internal._select, with two changes."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        value = self.value.get(name, [])
        if isinstance(value, basestring):
            value = [value]
        return SelectPatch.select(value, self.option.get(name, []), False, name, attributes, get_option_attributes, self)
<|end_body_0|>

<|body_start_1|>
        from bn import HTMLFragment
        from formbuild.internal import _select, check_attributes, html_open
        attributes = check_attributes(attributes, ['name', 'multiple'])
        if multiple:
            attributes['multiple'] = 'multiple'
        attributes['name'] = name
        values = [unicode(val) for val in value]
        fragment = HTMLFragment()
        fragment.safe(html_open(u'select', False, attributes) + '\n')
        counter = 0
        for v, k in options:
            if get_option_attributes:
                option_attr = get_option_attributes(self, v, k)
            else:
                option_attr = {}
            option_attr = check_attributes(option_attr, ['value', 'selected'])
            if unicode(v) in values:
                option_attr['selected'] = 'selected'
            option_attr['value'] = v
            fragment.safe(html_open(u'option', False, option_attr))
            fragment.write(k)
            fragment.safe('</option>\n')
        fragment.safe(u'</select>')
        return fragment.getvalue()
<|end_body_1|>

Solution:
class SelectPatch:
    def dropdown(self, name, attributes=None, get_option_attributes=None):
        """Monkey patch for FormBuild 3.0.3 bug"""
        value = self.value.get(name, [])
        if isinstance(value, basestring):
            value = [value]
        return SelectPatch.select(value, self.option.get(name, []), False, name, attributes, get_option_attributes, self)

    def select(value, options, multiple, name, attributes=None, get_option_attributes=None, self=None):
        """This is the same as formbuild.internal._select, with two changes."""
        from bn import HTMLFragment
        from formbuild.internal import _select, check_attributes, html_open
        attributes = check_attributes(attributes, ['name', 'multiple'])
        if multiple:
            attributes['multiple'] = 'multiple'
        attributes['name'] = name
        values = [unicode(val) for val in value]
        fragment = HTMLFragment()
        fragment.safe(html_open(u'select', False, attributes) + '\n')
        counter = 0
        for v, k in options:
            if get_option_attributes:
                option_attr = get_option_attributes(self, v, k)
            else:
                option_attr = {}
            option_attr = check_attributes(option_attr, ['value', 'selected'])
            if unicode(v) in values:
                option_attr['selected'] = 'selected'
            option_attr['value'] = v
            fragment.safe(html_open(u'option', False, option_attr))
            fragment.write(k)
            fragment.safe('</option>\n')
        fragment.safe(u'</select>')
        return fragment.getvalue()


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

Class description:
Simple class for binarizing tensors into high and low class values. The operation is: .. code-block: python x = val_low_class if x <= post_target_thresh else val_high_class .. note:: :py:attr:`val_low_class` needs *not* to be lower than :py:attr:`val_high_class`, so one can also invert binary masks with this.

Method signatures and docstrings:
- def __init__(self, threshold: float=0.5, val_low_class: float=0.0, val_high_class: float=1.0): Init. :param threshold: the threshold that defines the border between low and high class :param val_high_class: the value to which to set entries from high class :param val_low_class: the value to which to set entries from low class
- def settings(self) -> Dict[str, Any]: Settings to reproduce instance.
- def __call__(self, input_tensor: torch.Tensor) -> torch.Tensor: Binarize ``input_tensor`` according to the settings.

Implement the Python class `Binarize` described below.

Class description:
Simple class for binarizing tensors into high and low class values. The operation is: .. code-block: python x = val_low_class if x <= post_target_thresh else val_high_class .. note:: :py:attr:`val_low_class` needs *not* to be lower than :py:attr:`val_high_class`, so one can also invert binary masks with this.

Method signatures and docstrings:
- def __init__(self, threshold: float=0.5, val_low_class: float=0.0, val_high_class: float=1.0): Init. :param threshold: the threshold that defines the border between low and high class :param val_high_class: the value to which to set entries from high class :param val_low_class: the value to which to set entries from low class
- def settings(self) -> Dict[str, Any]: Settings to reproduce instance.
- def __call__(self, input_tensor: torch.Tensor) -> torch.Tensor: Binarize ``input_tensor`` according to the settings.

<|skeleton|>
class Binarize:
    """Simple class for binarizing tensors into high and low class values. The operation is: .. code-block: python x = val_low_class if x <= post_target_thresh else val_high_class .. note:: :py:attr:`val_low_class` needs *not* to be lower than :py:attr:`val_high_class`, so one can also invert binary masks with this."""

    def __init__(self, threshold: float=0.5, val_low_class: float=0.0, val_high_class: float=1.0):
        """Init. :param threshold: the threshold that defines the border between low and high class :param val_high_class: the value to which to set entries from high class :param val_low_class: the value to which to set entries from low class"""
        <|body_0|>

    def settings(self) -> Dict[str, Any]:
        """Settings to reproduce instance."""
        <|body_1|>

    def __call__(self, input_tensor: torch.Tensor) -> torch.Tensor:
        """Binarize ``input_tensor`` according to the settings."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(Binarize, self).__init__()
        self.threshold = threshold
        'Threshold by which to decide the class;\n        low class if ``x<=post_target_thresh``, else high'
        self.val_low_class = val_low_class
        'Value to set the low class to.'
        self.val_high_class = val_high_class
        'Value to set the high class to.'
<|end_body_0|>

<|body_start_1|>
        settings = dict(threshold=self.threshold)
        if self.val_low_class != 0.0:
            settings['val_low_class'] = self.val_low_class
        if self.val_high_class != 1.0:
            settings['val_high_class'] = self.val_high_class
        return settings
<|end_body_1|>

<|body_start_2|>
        if not isinstance(input_tensor, torch.Tensor):
            raise ValueError('input_tensor must be of type torch.Tensor, but was {}'.format(type(input_tensor)))
        low_class: torch.tensor = (input_tensor <= self.threshold).float()
        high_class: torch.tensor = (input_tensor > self.threshold).float()
        return low_class * self.val_low_class + high_class * self.val_high_class
<|end_body_2|>

Solution:
class Binarize:
    """Simple class for binarizing tensors into high and low class values. The operation is: .. code-block: python x = val_low_class if x <= post_target_thresh else val_high_class .. note:: :py:attr:`val_low_class` needs *not* to be lower than :py:attr:`val_high_class`, so one can also invert binary masks with this."""

    def __init__(self, threshold: float=0.5, val_low_class: float=0.0, val_high_class: float=1.0):
        """Init. :param threshold: the threshold that defines the border between low and high class :param val_high_class: the value to which to set entries from high class :param val_low_class: the value to which to set entries from low class"""
        super(Binarize, self).__init__()
        self.threshold = threshold
        'Threshold by which to decide the class;\n        low class if ``x<=post_target_thresh``, else high'
        self.val_low_class = val_low_class
        'Value to set the low class to.'
        self.val_high_class = val_high_class
        'Value to set the high class to.'

    def settings(self) -> Dict[str, Any]:
        """Settings to reproduce instance."""
        settings = dict(threshold=self.threshold)
        if self.val_low_class != 0.0:
            settings['val_low_class'] = self.val_low_class
        if self.val_high_class != 1.0:
            settings['val_high_class'] = self.val_high_class
        return settings

    def __call__(self, input_tensor: torch.Tensor) -> torch.Tensor:
        """Binarize ``input_tensor`` according to the settings."""
        if not isinstance(input_tensor, torch.Tensor):
            raise ValueError('input_tensor must be of type torch.Tensor, but was {}'.format(type(input_tensor)))
        low_class: torch.tensor = (input_tensor <= self.threshold).float()
        high_class: torch.tensor = (input_tensor > self.threshold).float()
        return low_class * self.val_low_class + high_class * self.val_high_class


delete

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

Class description:
Implement the DatabaseOutputter class.

Method signatures and docstrings:
- def get(self) -> RestaurantCatalog: Retrieve all current restaurants from the API
- def insert(self, data: Union[dict, list], token: str) -> None: Send restaurants marked as insert to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data
- def update(self, data: Union[dict, list], token: str) -> None: Send restaurants marked as update to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data
- def delete(self, data: Union[dict, list], token: str) -> None: Send restaurants marked as delete to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data

Implement the Python class `DatabaseOutputter` described below.

Class description:
Implement the DatabaseOutputter class.

Method signatures and docstrings:
- def get(self) -> RestaurantCatalog: Retrieve all current restaurants from the API
- def insert(self, data: Union[dict, list], token: str) -> None: Send restaurants marked as insert to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data
- def update(self, data: Union[dict, list], token: str) -> None: Send restaurants marked as update to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data
- def delete(self, data: Union[dict, list], token: str) -> None: Send restaurants marked as delete to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data

<|skeleton|>
class DatabaseOutputter:

    def get(self) -> RestaurantCatalog:
        """Retrieve all current restaurants from the API"""
        <|body_0|>

    def insert(self, data: Union[dict, list], token: str) -> None:
        """Send restaurants marked as insert to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data"""
        <|body_1|>

    def update(self, data: Union[dict, list], token: str) -> None:
        """Send restaurants marked as update to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data"""
        <|body_2|>

    def delete(self, data: Union[dict, list], token: str) -> None:
        """Send restaurants marked as delete to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        catalog = RestaurantCatalog()
        try:
            res = requests.get(self.ENDPOINT, timeout=4)
            if res.status_code == 200:
                catalog.add_many([Restaurant.from_json(row) for row in res.json()])
        except ConnectionError:
            print('Failed to connect to API')
        return catalog
<|end_body_0|>

<|body_start_1|>
        if len(data) == 0:
            return
        put_data = {'timestamp': token, 'data': data}
        res = requests.post(self.ENDPOINT, json=put_data)
        if res.status_code != 200:
            print('Failed to send insert data to database, writing to file instead')
            FileOutputter().insert(data, token)
<|end_body_1|>

<|body_start_2|>
        if len(data) == 0:
            return
        post_data = {'timestamp': token, 'data': data}
        res = requests.put(self.ENDPOINT, json=post_data)
        if res.status_code != 200:
            print('Failed to send update data to database, writing to file instead')
            FileOutputter().update(data, token)
<|end_body_2|>

<|body_start_3|>
        if len(data) == 0:
            return
        delete_data = {'timestamp': token, 'data': data}
        res = requests.delete(self.ENDPOINT, json=delete_data)
        if res.status_code != 200:
            print('Failed to send delete data to database, writing to file instead')
            FileOutputter().delete(data, token)
<|end_body_3|>

Solution:
class DatabaseOutputter:
    def get(self) -> RestaurantCatalog:
        """Retrieve all current restaurants from the API"""
        catalog = RestaurantCatalog()
        try:
            res = requests.get(self.ENDPOINT, timeout=4)
            if res.status_code == 200:
                catalog.add_many([Restaurant.from_json(row) for row in res.json()])
        except ConnectionError:
            print('Failed to connect to API')
        return catalog

    def insert(self, data: Union[dict, list], token: str) -> None:
        """Send restaurants marked as insert to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data"""
        if len(data) == 0:
            return
        put_data = {'timestamp': token, 'data': data}
        res = requests.post(self.ENDPOINT, json=put_data)
        if res.status_code != 200:
            print('Failed to send insert data to database, writing to file instead')
            FileOutputter().insert(data, token)

    def update(self, data: Union[dict, list], token: str) -> None:
        """Send restaurants marked as update to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data"""
        if len(data) == 0:
            return
        post_data = {'timestamp': token, 'data': data}
        res = requests.put(self.ENDPOINT, json=post_data)
        if res.status_code != 200:
            print('Failed to send update data to database, writing to file instead')
            FileOutputter().update(data, token)

    def delete(self, data: Union[dict, list], token: str) -> None:
        """Send restaurants marked as delete to API :param data: a list of restaurants or a single restaurant :param token: an identifier for the current session, to ensure that separate POST / PUT / DELETE requests are recognized as a single version of data"""
        if len(data) == 0:
            return
        delete_data = {'timestamp': token, 'data': data}
        res = requests.delete(self.ENDPOINT, json=delete_data)
        if res.status_code != 200:
            print('Failed to send delete data to database, writing to file instead')
            FileOutputter().delete(data, token)


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

Class description:
Implementation of the 'ExpandPhysicalClusterParameters' model. Specifies the parameters needed to expand a Cohesity Physical Edition Cluster. Attributes: chassis_serial_to_rack_id_map (object): ChassisSerialToRackId map. node_configs (list of PhysicalNodeConfiguration, required): Specifies the configuration details of the Nodes in the Cluster. vips (list of string): Specifies the virtual IPs to add to the Cluster.

Method signatures and docstrings:
- def __init__(self, chassis_serial_to_rack_id_map=None, node_configs=None, vips=None): Constructor for the ExpandPhysicalClusterParameters 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 `ExpandPhysicalClusterParameters` described below.

Class description:
Implementation of the 'ExpandPhysicalClusterParameters' model. Specifies the parameters needed to expand a Cohesity Physical Edition Cluster. Attributes: chassis_serial_to_rack_id_map (object): ChassisSerialToRackId map. node_configs (list of PhysicalNodeConfiguration, required): Specifies the configuration details of the Nodes in the Cluster. vips (list of string): Specifies the virtual IPs to add to the Cluster.

Method signatures and docstrings:
- def __init__(self, chassis_serial_to_rack_id_map=None, node_configs=None, vips=None): Constructor for the ExpandPhysicalClusterParameters 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 ExpandPhysicalClusterParameters:
    """Implementation of the 'ExpandPhysicalClusterParameters' model. Specifies the parameters needed to expand a Cohesity Physical Edition Cluster. Attributes: chassis_serial_to_rack_id_map (object): ChassisSerialToRackId map. node_configs (list of PhysicalNodeConfiguration, required): Specifies the configuration details of the Nodes in the Cluster. vips (list of string): Specifies the virtual IPs to add to the Cluster."""

    def __init__(self, chassis_serial_to_rack_id_map=None, node_configs=None, vips=None):
        """Constructor for the ExpandPhysicalClusterParameters 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.chassis_serial_to_rack_id_map = chassis_serial_to_rack_id_map
        self.node_configs = node_configs
        self.vips = vips
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        chassis_serial_to_rack_id_map = dictionary.get('chassisSerialToRackIdMap')
        node_configs = None
        if dictionary.get('nodeConfigs') != None:
            node_configs = list()
            for structure in dictionary.get('nodeConfigs'):
                node_configs.append(cohesity_management_sdk.models.physical_node_configuration.PhysicalNodeConfiguration.from_dictionary(structure))
        vips = dictionary.get('vips')
        return cls(chassis_serial_to_rack_id_map, node_configs, vips)
<|end_body_1|>

Solution:
class ExpandPhysicalClusterParameters:
    """Implementation of the 'ExpandPhysicalClusterParameters' model. Specifies the parameters needed to expand a Cohesity Physical Edition Cluster. Attributes: chassis_serial_to_rack_id_map (object): ChassisSerialToRackId map. node_configs (list of PhysicalNodeConfiguration, required): Specifies the configuration details of the Nodes in the Cluster. vips (list of string): Specifies the virtual IPs to add to the Cluster."""

    def __init__(self, chassis_serial_to_rack_id_map=None, node_configs=None, vips=None):
        """Constructor for the ExpandPhysicalClusterParameters class"""
        self.chassis_serial_to_rack_id_map = chassis_serial_to_rack_id_map
        self.node_configs = node_configs
        self.vips = vips

    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
        chassis_serial_to_rack_id_map = dictionary.get('chassisSerialToRackIdMap')
        node_configs = None
        if dictionary.get('nodeConfigs') != None:
            node_configs = list()
            for structure in dictionary.get('nodeConfigs'):
                node_configs.append(cohesity_management_sdk.models.physical_node_configuration.PhysicalNodeConfiguration.from_dictionary(structure))
        vips = dictionary.get('vips')
        return cls(chassis_serial_to_rack_id_map, node_configs, vips)


serialize

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

Class description:
The Base Class of Device Enrollment Configuration

Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceEnrollmentConfiguration: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: DeviceEnrollmentConfiguration
- def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]: The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]
- def serialize(self, writer: SerializationWriter) -> None: Serializes information the current object Args: writer: Serialization writer to use to serialize this model

Implement the Python class `DeviceEnrollmentConfiguration` described below.

Class description:
The Base Class of Device Enrollment Configuration

Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceEnrollmentConfiguration: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: DeviceEnrollmentConfiguration
- def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]: The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]
- def serialize(self, writer: SerializationWriter) -> None: Serializes information the current object Args: writer: Serialization writer to use to serialize this model

<|skeleton|>
class DeviceEnrollmentConfiguration:
    """The Base Class of Device Enrollment Configuration"""

    def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceEnrollmentConfiguration:
        """Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: DeviceEnrollmentConfiguration"""
        <|body_0|>

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        <|body_1|>

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if not parse_node:
            raise TypeError('parse_node cannot be null.')
        try:
            mapping_value = parse_node.get_child_node('@odata.type').get_str_value()
        except AttributeError:
            mapping_value = None
        if mapping_value and mapping_value.casefold() == '#microsoft.graph.deviceEnrollmentLimitConfiguration'.casefold():
            from .device_enrollment_limit_configuration import DeviceEnrollmentLimitConfiguration
            return DeviceEnrollmentLimitConfiguration()
        if mapping_value and mapping_value.casefold() == '#microsoft.graph.deviceEnrollmentPlatformRestrictionsConfiguration'.casefold():
            from .device_enrollment_platform_restrictions_configuration import DeviceEnrollmentPlatformRestrictionsConfiguration
            return DeviceEnrollmentPlatformRestrictionsConfiguration()
        if mapping_value and mapping_value.casefold() == '#microsoft.graph.deviceEnrollmentWindowsHelloForBusinessConfiguration'.casefold():
            from .device_enrollment_windows_hello_for_business_configuration import DeviceEnrollmentWindowsHelloForBusinessConfiguration
            return DeviceEnrollmentWindowsHelloForBusinessConfiguration()
        return DeviceEnrollmentConfiguration()
<|end_body_0|>

<|body_start_1|>
        from .device_enrollment_limit_configuration import DeviceEnrollmentLimitConfiguration
        from .device_enrollment_platform_restrictions_configuration import DeviceEnrollmentPlatformRestrictionsConfiguration
        from .device_enrollment_windows_hello_for_business_configuration import DeviceEnrollmentWindowsHelloForBusinessConfiguration
        from .enrollment_configuration_assignment import EnrollmentConfigurationAssignment
        from .entity import Entity
        from .device_enrollment_limit_configuration import DeviceEnrollmentLimitConfiguration
        from .device_enrollment_platform_restrictions_configuration import DeviceEnrollmentPlatformRestrictionsConfiguration
        from .device_enrollment_windows_hello_for_business_configuration import DeviceEnrollmentWindowsHelloForBusinessConfiguration
        from .enrollment_configuration_assignment import EnrollmentConfigurationAssignment
        from .entity import Entity
        fields: Dict[str, Callable[[Any], None]] = {'assignments': lambda n: setattr(self, 'assignments', n.get_collection_of_object_values(EnrollmentConfigurationAssignment)), 'createdDateTime': lambda n: setattr(self, 'created_date_time', n.get_datetime_value()), 'description': lambda n: setattr(self, 'description', n.get_str_value()), 'displayName': lambda n: setattr(self, 'display_name', n.get_str_value()), 'lastModifiedDateTime': lambda n: setattr(self, 'last_modified_date_time', n.get_datetime_value()), 'priority': lambda n: setattr(self, 'priority', n.get_int_value()), 'version': lambda n: setattr(self, 'version', n.get_int_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields
<|end_body_1|>

<|body_start_2|>
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_collection_of_object_values('assignments', self.assignments)
        writer.write_datetime_value('createdDateTime', self.created_date_time)
        writer.write_str_value('description', self.description)
        writer.write_str_value('displayName', self.display_name)
        writer.write_datetime_value('lastModifiedDateTime', self.last_modified_date_time)
        writer.write_int_value('priority', self.priority)
        writer.write_int_value('version', self.version)
<|end_body_2|>

Solution:
class DeviceEnrollmentConfiguration:
    """The Base Class of Device Enrollment Configuration"""

    def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceEnrollmentConfiguration:
        """Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: DeviceEnrollmentConfiguration"""
        if not parse_node:
            raise TypeError('parse_node cannot be null.')
        try:
            mapping_value = parse_node.get_child_node('@odata.type').get_str_value()
        except AttributeError:
            mapping_value = None
        if mapping_value and mapping_value.casefold() == '#microsoft.graph.deviceEnrollmentLimitConfiguration'.casefold():
            from .device_enrollment_limit_configuration import DeviceEnrollmentLimitConfiguration
            return DeviceEnrollmentLimitConfiguration()
        if mapping_value and mapping_value.casefold() == '#microsoft.graph.deviceEnrollmentPlatformRestrictionsConfiguration'.casefold():
            from .device_enrollment_platform_restrictions_configuration import DeviceEnrollmentPlatformRestrictionsConfiguration
            return DeviceEnrollmentPlatformRestrictionsConfiguration()
        if mapping_value and mapping_value.casefold() == '#microsoft.graph.deviceEnrollmentWindowsHelloForBusinessConfiguration'.casefold():
            from .device_enrollment_windows_hello_for_business_configuration import DeviceEnrollmentWindowsHelloForBusinessConfiguration
            return DeviceEnrollmentWindowsHelloForBusinessConfiguration()
        return DeviceEnrollmentConfiguration()

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        from .device_enrollment_limit_configuration import DeviceEnrollmentLimitConfiguration
        from .device_enrollment_platform_restrictions_configuration import DeviceEnrollmentPlatformRestrictionsConfiguration
        from .device_enrollment_windows_hello_for_business_configuration import DeviceEnrollmentWindowsHelloForBusinessConfiguration
        from .enrollment_configuration_assignment import EnrollmentConfigurationAssignment
        from .entity import Entity
        from .device_enrollment_limit_configuration import DeviceEnrollmentLimitConfiguration
        from .device_enrollment_platform_restrictions_configuration import DeviceEnrollmentPlatformRestrictionsConfiguration
        from .device_enrollment_windows_hello_for_business_configuration import DeviceEnrollmentWindowsHelloForBusinessConfiguration
        from .enrollment_configuration_assignment import EnrollmentConfigurationAssignment
        from .entity import Entity
        fields: Dict[str, Callable[[Any], None]] = {'assignments': lambda n: setattr(self, 'assignments', n.get_collection_of_object_values(EnrollmentConfigurationAssignment)), 'createdDateTime': lambda n: setattr(self, 'created_date_time', n.get_datetime_value()), 'description': lambda n: setattr(self, 'description', n.get_str_value()), 'displayName': lambda n: setattr(self, 'display_name', n.get_str_value()), 'lastModifiedDateTime': lambda n: setattr(self, 'last_modified_date_time', n.get_datetime_value()), 'priority': lambda n: setattr(self, 'priority', n.get_int_value()), 'version': lambda n: setattr(self, 'version', n.get_int_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_collection_of_object_values('assignments', self.assignments)
        writer.write_datetime_value('createdDateTime', self.created_date_time)
        writer.write_str_value('description', self.description)
        writer.write_str_value('displayName', self.display_name)
        writer.write_datetime_value('lastModifiedDateTime', self.last_modified_date_time)
        writer.write_int_value('priority', self.priority)
        writer.write_int_value('version', self.version)


Prompt:
Implement the Python class `Day20` described below.

Class description:
AoC 2021 Day 20

Method signatures and docstrings:
- def part1(filename: str) -> int: Given a filename, solve 2021 day 20 part 1
- def part2(filename: str) -> int: Given a filename, solve 2021 day 20 part 2

Implement the Python class `Day20` described below.

Class description:
AoC 2021 Day 20

Method signatures and docstrings:
- def part1(filename: str) -> int: Given a filename, solve 2021 day 20 part 1
- def part2(filename: str) -> int: Given a filename, solve 2021 day 20 part 2

<|skeleton|>
class Day20:
    """AoC 2021 Day 20"""

    def part1(filename: str) -> int:
        """Given a filename, solve 2021 day 20 part 1"""
        <|body_0|>

    def part2(filename: str) -> int:
        """Given a filename, solve 2021 day 20 part 2"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        b1, b2 = parse(filename)
        b2 = np.array(b2, dtype=np.int16)
        b2 = np.pad(b2, (4, 4), 'constant', constant_values=(0, 0))
        b2 = enhance(b1, b2)
        b2 = enhance(b1, b2)
        return np.count_nonzero(b2)
<|end_body_0|>

<|body_start_1|>
        data = parse(filename)
        b1, b2 = parse(filename)
        b2 = np.array(b2, dtype=np.int16)
        b2 = np.pad(b2, (3, 3), 'constant', constant_values=(0, 0))
        for _ in range(50):
            b2 = enhance(b1, b2)
            pad = b2[0][0]
            b2 = np.pad(b2, (2, 2), 'constant', constant_values=(pad, pad))
        return np.count_nonzero(b2)
<|end_body_1|>

Solution:
class Day20:
    """AoC 2021 Day 20"""

    def part1(filename: str) -> int:
        """Given a filename, solve 2021 day 20 part 1"""
        b1, b2 = parse(filename)
        b2 = np.array(b2, dtype=np.int16)
        b2 = np.pad(b2, (4, 4), 'constant', constant_values=(0, 0))
        b2 = enhance(b1, b2)
        b2 = enhance(b1, b2)
        return np.count_nonzero(b2)

    def part2(filename: str) -> int:
        """Given a filename, solve 2021 day 20 part 2"""
        data = parse(filename)
        b1, b2 = parse(filename)
        b2 = np.array(b2, dtype=np.int16)
        b2 = np.pad(b2, (3, 3), 'constant', constant_values=(0, 0))
        for _ in range(50):
            b2 = enhance(b1, b2)
            pad = b2[0][0]
            b2 = np.pad(b2, (2, 2), 'constant', constant_values=(pad, pad))
        return np.count_nonzero(b2)


generate

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

Class description:
Spectrogram data from the Vox Celeb Dataset.

Method signatures and docstrings:
- def __init__(self, secs, path, concat=True): Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. Multiple .wav files will be combined if necessary. path : string Path to folder containing .wav file(s) concat : bool Whether or not to concatenate multiple files. If false, only the first file in the directory will be used.
- def generate(self, seg_length=0.02, overlap=0.3, normalize=True, **spectrogram_kwargs): Generate spectrogram from collected audio samples. Parameters ---------- seg_length : float Length of FFT segment (in seconds) for computing the spectrogram. Defaults to 20 milliseconds. overlap : float Float between 0 and 1 specifying the fraction of segments which should overlap. Defaults to 0.3. normalize : boolean If true, divide each frequency bin by its standard deviation. spectrogram_kwargs : keyword arguments Optional arguments to scipy.signal.spectrogram(). Note that these keyword arguments should not contain noverlap, nseg, fs, or return_onesided, as these are specified directly as parameters to generate(). Returns ------- S : array Spectrogram audio : array Raw audio samples

Implement the Python class `VoxCeleb` described below.

Class description:
Spectrogram data from the Vox Celeb Dataset.

Method signatures and docstrings:
- def __init__(self, secs, path, concat=True): Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. Multiple .wav files will be combined if necessary. path : string Path to folder containing .wav file(s) concat : bool Whether or not to concatenate multiple files. If false, only the first file in the directory will be used.
- def generate(self, seg_length=0.02, overlap=0.3, normalize=True, **spectrogram_kwargs): Generate spectrogram from collected audio samples. Parameters ---------- seg_length : float Length of FFT segment (in seconds) for computing the spectrogram. Defaults to 20 milliseconds. overlap : float Float between 0 and 1 specifying the fraction of segments which should overlap. Defaults to 0.3. normalize : boolean If true, divide each frequency bin by its standard deviation. spectrogram_kwargs : keyword arguments Optional arguments to scipy.signal.spectrogram(). Note that these keyword arguments should not contain noverlap, nseg, fs, or return_onesided, as these are specified directly as parameters to generate(). Returns ------- S : array Spectrogram audio : array Raw audio samples

<|skeleton|>
class VoxCeleb:
    """Spectrogram data from the Vox Celeb Dataset."""

    def __init__(self, secs, path, concat=True):
        """Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. Multiple .wav files will be combined if necessary. path : string Path to folder containing .wav file(s) concat : bool Whether or not to concatenate multiple files. If false, only the first file in the directory will be used."""
        <|body_0|>

    def generate(self, seg_length=0.02, overlap=0.3, normalize=True, **spectrogram_kwargs):
        """Generate spectrogram from collected audio samples. Parameters ---------- seg_length : float Length of FFT segment (in seconds) for computing the spectrogram. Defaults to 20 milliseconds. overlap : float Float between 0 and 1 specifying the fraction of segments which should overlap. Defaults to 0.3. normalize : boolean If true, divide each frequency bin by its standard deviation. spectrogram_kwargs : keyword arguments Optional arguments to scipy.signal.spectrogram(). Note that these keyword arguments should not contain noverlap, nseg, fs, or return_onesided, as these are specified directly as parameters to generate(). Returns ------- S : array Spectrogram audio : array Raw audio samples"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        audio = np.empty((1,))
        secs_loaded = 0
        files_loaded = 0
        files = glob.glob(path + '*.wav')
        for file in files:
            sr, samples = wavfile.read(file)
            audio = np.concatenate((audio, samples))
            dur = len(samples) / sr
            secs_loaded = secs_loaded + dur
            files_loaded = files_loaded + 1
            if secs_loaded >= secs:
                break
            if not concat:
                break
        total_samples = int(round(secs * sr))
        if total_samples > len(audio):
            warnings.warn('Found fewer than %.2f seconds of audio. Returning %.2f seconds of audio.' % (secs, len(audio) / sr))
        audio = audio[0:total_samples]
        self.audio = audio
        self.sampling_rate = sr
<|end_body_0|>

<|body_start_1|>
        nperseg = round(int(seg_length * self.sampling_rate))
        noverlap = round(int(nperseg * overlap))
        spectrogram_kwargs['fs'] = self.sampling_rate
        spectrogram_kwargs['nperseg'] = nperseg
        spectrogram_kwargs['noverlap'] = noverlap
        _, _, S = signal.spectrogram(self.audio, **spectrogram_kwargs)
        if normalize:
            scaler = preprocessing.StandardScaler(with_mean=False)
            S = scaler.fit_transform(S.T).T
        return (S, self.audio)
<|end_body_1|>

Solution:
class VoxCeleb:
    """Spectrogram data from the Vox Celeb Dataset."""

    def __init__(self, secs, path, concat=True):
        """Create a VoxCeleb dataset object. Parameters ---------- secs : int Number of seconds of the dataset to be generated. Multiple .wav files will be combined if necessary. path : string Path to folder containing .wav file(s) concat : bool Whether or not to concatenate multiple files. If false, only the first file in the directory will be used."""
        audio = np.empty((1,))
        secs_loaded = 0
        files_loaded = 0
        files = glob.glob(path + '*.wav')
        for file in files:
            sr, samples = wavfile.read(file)
            audio = np.concatenate((audio, samples))
            dur = len(samples) / sr
            secs_loaded = secs_loaded + dur
            files_loaded = files_loaded + 1
            if secs_loaded >= secs:
                break
            if not concat:
                break
        total_samples = int(round(secs * sr))
        if total_samples > len(audio):
            warnings.warn('Found fewer than %.2f seconds of audio. Returning %.2f seconds of audio.' % (secs, len(audio) / sr))
        audio = audio[0:total_samples]
        self.audio = audio
        self.sampling_rate = sr

    def generate(self, seg_length=0.02, overlap=0.3, normalize=True, **spectrogram_kwargs):
        """Generate spectrogram from collected audio samples. Parameters ---------- seg_length : float Length of FFT segment (in seconds) for computing the spectrogram. Defaults to 20 milliseconds. overlap : float Float between 0 and 1 specifying the fraction of segments which should overlap. Defaults to 0.3. normalize : boolean If true, divide each frequency bin by its standard deviation. spectrogram_kwargs : keyword arguments Optional arguments to scipy.signal.spectrogram(). Note that these keyword arguments should not contain noverlap, nseg, fs, or return_onesided, as these are specified directly as parameters to generate(). Returns ------- S : array Spectrogram audio : array Raw audio samples"""
        nperseg = round(int(seg_length * self.sampling_rate))
        noverlap = round(int(nperseg * overlap))
        spectrogram_kwargs['fs'] = self.sampling_rate
        spectrogram_kwargs['nperseg'] = nperseg
        spectrogram_kwargs['noverlap'] = noverlap
        _, _, S = signal.spectrogram(self.audio, **spectrogram_kwargs)
        if normalize:
            scaler = preprocessing.StandardScaler(with_mean=False)
            S = scaler.fit_transform(S.T).T
        return (S, self.audio)


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

Class description:
Implement the FeaturesClient class.

Method signatures and docstrings:
- async def get_features(self, *, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, pretty: t.Optional[bool]=None) -> ObjectApiResponse[t.Any]: Gets a list of features which can be included in snapshots using the feature_states field when creating a snapshot `<https://www.elastic.co/guide/en/elasticsearch/reference/master/get-features-api.html>`_
- async def reset_features(self, *, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, pretty: t.Optional[bool]=None) -> ObjectApiResponse[t.Any]: Resets the internal state of features, usually by deleting system indices `<https://www.elastic.co/guide/en/elasticsearch/reference/master/modules-snapshots.html>`_

Implement the Python class `FeaturesClient` described below.

Class description:
Implement the FeaturesClient class.

Method signatures and docstrings:
- async def get_features(self, *, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, pretty: t.Optional[bool]=None) -> ObjectApiResponse[t.Any]: Gets a list of features which can be included in snapshots using the feature_states field when creating a snapshot `<https://www.elastic.co/guide/en/elasticsearch/reference/master/get-features-api.html>`_
- async def reset_features(self, *, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, pretty: t.Optional[bool]=None) -> ObjectApiResponse[t.Any]: Resets the internal state of features, usually by deleting system indices `<https://www.elastic.co/guide/en/elasticsearch/reference/master/modules-snapshots.html>`_

<|skeleton|>
class FeaturesClient:

    async def get_features(self, *, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, pretty: t.Optional[bool]=None) -> ObjectApiResponse[t.Any]:
        """Gets a list of features which can be included in snapshots using the feature_states field when creating a snapshot `<https://www.elastic.co/guide/en/elasticsearch/reference/master/get-features-api.html>`_"""
        <|body_0|>

    async def reset_features(self, *, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, pretty: t.Optional[bool]=None) -> ObjectApiResponse[t.Any]:
        """Resets the internal state of features, usually by deleting system indices `<https://www.elastic.co/guide/en/elasticsearch/reference/master/modules-snapshots.html>`_"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        __path = '/_features'
        __query: t.Dict[str, t.Any] = {}
        if error_trace is not None:
            __query['error_trace'] = error_trace
        if filter_path is not None:
            __query['filter_path'] = filter_path
        if human is not None:
            __query['human'] = human
        if pretty is not None:
            __query['pretty'] = pretty
        __headers = {'accept': 'application/json'}
        return await self.perform_request('GET', __path, params=__query, headers=__headers)
<|end_body_0|>

<|body_start_1|>
        __path = '/_features/_reset'
        __query: t.Dict[str, t.Any] = {}
        if error_trace is not None:
            __query['error_trace'] = error_trace
        if filter_path is not None:
            __query['filter_path'] = filter_path
        if human is not None:
            __query['human'] = human
        if pretty is not None:
            __query['pretty'] = pretty
        __headers = {'accept': 'application/json'}
        return await self.perform_request('POST', __path, params=__query, headers=__headers)
<|end_body_1|>

Solution:
class FeaturesClient:
    async def get_features(self, *, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, pretty: t.Optional[bool]=None) -> ObjectApiResponse[t.Any]:
        """Gets a list of features which can be included in snapshots using the feature_states field when creating a snapshot `<https://www.elastic.co/guide/en/elasticsearch/reference/master/get-features-api.html>`_"""
        __path = '/_features'
        __query: t.Dict[str, t.Any] = {}
        if error_trace is not None:
            __query['error_trace'] = error_trace
        if filter_path is not None:
            __query['filter_path'] = filter_path
        if human is not None:
            __query['human'] = human
        if pretty is not None:
            __query['pretty'] = pretty
        __headers = {'accept': 'application/json'}
        return await self.perform_request('GET', __path, params=__query, headers=__headers)

    async def reset_features(self, *, error_trace: t.Optional[bool]=None, filter_path: t.Optional[t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]=None, human: t.Optional[bool]=None, pretty: t.Optional[bool]=None) -> ObjectApiResponse[t.Any]:
        """Resets the internal state of features, usually by deleting system indices `<https://www.elastic.co/guide/en/elasticsearch/reference/master/modules-snapshots.html>`_"""
        __path = '/_features/_reset'
        __query: t.Dict[str, t.Any] = {}
        if error_trace is not None:
            __query['error_trace'] = error_trace
        if filter_path is not None:
            __query['filter_path'] = filter_path
        if human is not None:
            __query['human'] = human
        if pretty is not None:
            __query['pretty'] = pretty
        __headers = {'accept': 'application/json'}
        return await self.perform_request('POST', __path, params=__query, headers=__headers)


delete

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

Class description:
Implement the ObservacionCyTG class.

Method signatures and docstrings:
- def get(self, id): To fetch an observation (CyTG (resultados))
- def put(self, id): To update an observation (CyTG (resultados))
- def delete(self, id): To delete an observation (CyTG (resultados))

Implement the Python class `ObservacionCyTG` described below.

Class description:
Implement the ObservacionCyTG class.

Method signatures and docstrings:
- def get(self, id): To fetch an observation (CyTG (resultados))
- def put(self, id): To update an observation (CyTG (resultados))
- def delete(self, id): To delete an observation (CyTG (resultados))

<|skeleton|>
class ObservacionCyTG:

    def get(self, id):
        """To fetch an observation (CyTG (resultados))"""
        <|body_0|>

    def put(self, id):
        """To update an observation (CyTG (resultados))"""
        <|body_1|>

    def delete(self, id):
        """To delete an observation (CyTG (resultados))"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        try:
            verify_token(request.headers)
        except Exception as err:
            ns.abort(401, message=err)
        try:
            obs = observaciones_ires_cytg.read(id)
        except psycopg2.Error as err:
            ns.abort(400, message=get_msg_pgerror(err))
        except EmptySetError:
            ns.abort(404, message=ObservacionCyTG.obs_not_found)
        except Exception as err:
            ns.abort(400, message=err)
        return obs
<|end_body_0|>

<|body_start_1|>
        try:
            verify_token(request.headers)
        except Exception as err:
            ns.abort(401, message=err)
        try:
            obs = observaciones_ires_cytg.update(id, **api.payload)
        except psycopg2.Error as err:
            ns.abort(400, message=get_msg_pgerror(err))
        except KeyError as err:
            ns.abort(400, message='Review the keys in your payload: {}'.format(err))
        except EmptySetError as err:
            ns.abort(404, message=ObservacionCyTG.obs_not_found + '. ' + str(err))
        except Exception as err:
            ns.abort(400, message=err)
        return obs
<|end_body_1|>

<|body_start_2|>
        try:
            verify_token(request.headers)
        except Exception as err:
            ns.abort(401, message=err)
        try:
            obs = observaciones_ires_cytg.delete(id)
        except psycopg2.Error as err:
            ns.abort(400, message=get_msg_pgerror(err))
        except EmptySetError:
            ns.abort(404, message=ObservacionCyTG.obs_not_found)
        except Exception as err:
            ns.abort(400, message=err)
        return obs
<|end_body_2|>

Solution:
class ObservacionCyTG:
    def get(self, id):
        """To fetch an observation (CyTG (resultados))"""
        try:
            verify_token(request.headers)
        except Exception as err:
            ns.abort(401, message=err)
        try:
            obs = observaciones_ires_cytg.read(id)
        except psycopg2.Error as err:
            ns.abort(400, message=get_msg_pgerror(err))
        except EmptySetError:
            ns.abort(404, message=ObservacionCyTG.obs_not_found)
        except Exception as err:
            ns.abort(400, message=err)
        return obs

    def put(self, id):
        """To update an observation (CyTG (resultados))"""
        try:
            verify_token(request.headers)
        except Exception as err:
            ns.abort(401, message=err)
        try:
            obs = observaciones_ires_cytg.update(id, **api.payload)
        except psycopg2.Error as err:
            ns.abort(400, message=get_msg_pgerror(err))
        except KeyError as err:
            ns.abort(400, message='Review the keys in your payload: {}'.format(err))
        except EmptySetError as err:
            ns.abort(404, message=ObservacionCyTG.obs_not_found + '. ' + str(err))
        except Exception as err:
            ns.abort(400, message=err)
        return obs

    def delete(self, id):
        """To delete an observation (CyTG (resultados))"""
        try:
            verify_token(request.headers)
        except Exception as err:
            ns.abort(401, message=err)
        try:
            obs = observaciones_ires_cytg.delete(id)
        except psycopg2.Error as err:
            ns.abort(400, message=get_msg_pgerror(err))
        except EmptySetError:
            ns.abort(404, message=ObservacionCyTG.obs_not_found)
        except Exception as err:
            ns.abort(400, message=err)
        return obs


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

Class description:
Class for generating Keypair modulus for a given seed and bit size.

Method signatures and docstrings:
- def __init__(self, seed: bytes): Initializes the PRNG given an initial seed. Args: seed: the initial seed used in the PRNG.
- def generate_prime(self, p_size_bits: int) -> int: Generates a prime using Keypair PRNG. The prime is assembled by concatenating AES blocks. Original implementation is at https://github.com/juliangruber/keypair/blob/master/index.js. Args: p_size_bits: bit size of the prime to be generated. Returns: The generated prime.
- def generate_key(self, bits: int) -> tuple[int, int]: Generates the primes as in an RSA key. Args: bits: bit size of the RSA key/modulus. Returns: a tuple containing the two primes of the RSA key/modulus.

Implement the Python class `Generator` described below.

Class description:
Class for generating Keypair modulus for a given seed and bit size.

Method signatures and docstrings:
- def __init__(self, seed: bytes): Initializes the PRNG given an initial seed. Args: seed: the initial seed used in the PRNG.
- def generate_prime(self, p_size_bits: int) -> int: Generates a prime using Keypair PRNG. The prime is assembled by concatenating AES blocks. Original implementation is at https://github.com/juliangruber/keypair/blob/master/index.js. Args: p_size_bits: bit size of the prime to be generated. Returns: The generated prime.
- def generate_key(self, bits: int) -> tuple[int, int]: Generates the primes as in an RSA key. Args: bits: bit size of the RSA key/modulus. Returns: a tuple containing the two primes of the RSA key/modulus.

<|skeleton|>
class Generator:
    """Class for generating Keypair modulus for a given seed and bit size."""

    def __init__(self, seed: bytes):
        """Initializes the PRNG given an initial seed. Args: seed: the initial seed used in the PRNG."""
        <|body_0|>

    def generate_prime(self, p_size_bits: int) -> int:
        """Generates a prime using Keypair PRNG. The prime is assembled by concatenating AES blocks. Original implementation is at https://github.com/juliangruber/keypair/blob/master/index.js. Args: p_size_bits: bit size of the prime to be generated. Returns: The generated prime."""
        <|body_1|>

    def generate_key(self, bits: int) -> tuple[int, int]:
        """Generates the primes as in an RSA key. Args: bits: bit size of the RSA key/modulus. Returns: a tuple containing the two primes of the RSA key/modulus."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        t = hashlib.sha1(seed).digest()
        key = hashlib.sha1(t).digest()
        self.orig_key = key
        seed = hashlib.sha1(key).digest()
        self.key = key[:16]
        self.seed = seed[:16]
<|end_body_0|>

<|body_start_1|>
        p_size_bytes = p_size_bits // 8
        while True:
            prime_bytes = b''
            idx = 0
            while len(prime_bytes) <= p_size_bytes:
                encryptor = ciphers.Cipher(algorithms.AES(self.key), modes.ECB()).encryptor()
                prime_bytes += encryptor.update(self.seed)
                seed_inc = (int.from_bytes(self.seed, 'big') + 1).to_bytes(16, 'big')
                self.key = encryptor.update(seed_inc)
                encryptor = ciphers.Cipher(algorithms.AES(self.key), modes.ECB()).encryptor()
                self.seed = encryptor.update(seed_inc)
            prime_bytes = prime_bytes[1:p_size_bytes + 1]
            p = int.from_bytes(prime_bytes, 'big')
            p |= 1 << p_size_bits - 1
            p += 31 - p % 30
            while not gmpy.is_prime(p, 1):
                p += GCD_30_DELTA[idx % 8]
                idx += 1
            if gmpy.is_prime(p, 10):
                return p
<|end_body_1|>

<|body_start_2|>
        p_size_bits = bits // 2
        p = self.generate_prime(p_size_bits)
        q = self.generate_prime(p_size_bits)
        while True:
            if q > p:
                p, q = (q, p)
            n = p * q
            if n.bit_length() == bits:
                return (p, q)
            q = self.generate_prime(p_size_bits)
<|end_body_2|>

Solution:
class Generator:
    """Class for generating Keypair modulus for a given seed and bit size."""

    def __init__(self, seed: bytes):
        """Initializes the PRNG given an initial seed. Args: seed: the initial seed used in the PRNG."""
        t = hashlib.sha1(seed).digest()
        key = hashlib.sha1(t).digest()
        self.orig_key = key
        seed = hashlib.sha1(key).digest()
        self.key = key[:16]
        self.seed = seed[:16]

    def generate_prime(self, p_size_bits: int) -> int:
        """Generates a prime using Keypair PRNG. The prime is assembled by concatenating AES blocks. Original implementation is at https://github.com/juliangruber/keypair/blob/master/index.js. Args: p_size_bits: bit size of the prime to be generated. Returns: The generated prime."""
        p_size_bytes = p_size_bits // 8
        while True:
            prime_bytes = b''
            idx = 0
            while len(prime_bytes) <= p_size_bytes:
                encryptor = ciphers.Cipher(algorithms.AES(self.key), modes.ECB()).encryptor()
                prime_bytes += encryptor.update(self.seed)
                seed_inc = (int.from_bytes(self.seed, 'big') + 1).to_bytes(16, 'big')
                self.key = encryptor.update(seed_inc)
                encryptor = ciphers.Cipher(algorithms.AES(self.key), modes.ECB()).encryptor()
                self.seed = encryptor.update(seed_inc)
            prime_bytes = prime_bytes[1:p_size_bytes + 1]
            p = int.from_bytes(prime_bytes, 'big')
            p |= 1 << p_size_bits - 1
            p += 31 - p % 30
            while not gmpy.is_prime(p, 1):
                p += GCD_30_DELTA[idx % 8]
                idx += 1
            if gmpy.is_prime(p, 10):
                return p

    def generate_key(self, bits: int) -> tuple[int, int]:
        """Generates the primes as in an RSA key. Args: bits: bit size of the RSA key/modulus. Returns: a tuple containing the two primes of the RSA key/modulus."""
        p_size_bits = bits // 2
        p = self.generate_prime(p_size_bits)
        q = self.generate_prime(p_size_bits)
        while True:
            if q > p:
                p, q = (q, p)
            n = p * q
            if n.bit_length() == bits:
                return (p, q)
            q = self.generate_prime(p_size_bits)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numFactoredBinaryTrees(self, A): :type A: List[int] :rtype: int
- def numFactoredBinaryTrees2(self, A): :type A: List[int] :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numFactoredBinaryTrees(self, A): :type A: List[int] :rtype: int
- def numFactoredBinaryTrees2(self, A): :type A: List[int] :rtype: int

<|skeleton|>
class Solution:

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

    def numFactoredBinaryTrees2(self, A):
        """:type A: List[int] :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        MOD = 10 ** 9 + 7
        N = len(A)
        A.sort()
        dp = [1] * N
        index = {x: i for i, x in enumerate(A)}
        for i, x in enumerate(A):
            for j in range(i):
                if x % A[j] == 0:
                    right = x // A[j]
                    if right in index:
                        dp[i] += dp[j] * dp[index[right]]
                        dp[i] %= MOD
        return sum(dp) % MOD
<|end_body_0|>

<|body_start_1|>
        M = 10 ** 9 + 7
        A.sort()
        n, count = (len(A), {})
        res = 0
        for i in range(n):
            cnt = 1
            for j in range(i):
                if A[i] % A[j] == 0 and A[i] // A[j] in count:
                    cnt += count[A[j]] * count[A[i] // A[j]]
            count[A[i]] = cnt % M
            res = (res + count[A[i]]) % M
        return res
<|end_body_1|>

Solution:
class Solution:
    def numFactoredBinaryTrees(self, A):
        """:type A: List[int] :rtype: int"""
        MOD = 10 ** 9 + 7
        N = len(A)
        A.sort()
        dp = [1] * N
        index = {x: i for i, x in enumerate(A)}
        for i, x in enumerate(A):
            for j in range(i):
                if x % A[j] == 0:
                    right = x // A[j]
                    if right in index:
                        dp[i] += dp[j] * dp[index[right]]
                        dp[i] %= MOD
        return sum(dp) % MOD

    def numFactoredBinaryTrees2(self, A):
        """:type A: List[int] :rtype: int"""
        M = 10 ** 9 + 7
        A.sort()
        n, count = (len(A), {})
        res = 0
        for i in range(n):
            cnt = 1
            for j in range(i):
                if A[i] % A[j] == 0 and A[i] // A[j] in count:
                    cnt += count[A[j]] * count[A[i] // A[j]]
            count[A[i]] = cnt % M
            res = (res + count[A[i]]) % M
        return res


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

Class description:
Implementation of the 'CreateApplePayHeaderRequest' model. The ApplePay header request Attributes: public_key_hash (string): SHA–256 hash, Base64 string codified ephemeral_public_key (string): X.509 encoded key bytes, Base64 encoded as a string transaction_id (string): Transaction identifier, generated on Device

Method signatures and docstrings:
- def __init__(self, ephemeral_public_key=None, public_key_hash=None, transaction_id=None): Constructor for the CreateApplePayHeaderRequest 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 `CreateApplePayHeaderRequest` described below.

Class description:
Implementation of the 'CreateApplePayHeaderRequest' model. The ApplePay header request Attributes: public_key_hash (string): SHA–256 hash, Base64 string codified ephemeral_public_key (string): X.509 encoded key bytes, Base64 encoded as a string transaction_id (string): Transaction identifier, generated on Device

Method signatures and docstrings:
- def __init__(self, ephemeral_public_key=None, public_key_hash=None, transaction_id=None): Constructor for the CreateApplePayHeaderRequest 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 CreateApplePayHeaderRequest:
    """Implementation of the 'CreateApplePayHeaderRequest' model. The ApplePay header request Attributes: public_key_hash (string): SHA–256 hash, Base64 string codified ephemeral_public_key (string): X.509 encoded key bytes, Base64 encoded as a string transaction_id (string): Transaction identifier, generated on Device"""

    def __init__(self, ephemeral_public_key=None, public_key_hash=None, transaction_id=None):
        """Constructor for the CreateApplePayHeaderRequest 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.public_key_hash = public_key_hash
        self.ephemeral_public_key = ephemeral_public_key
        self.transaction_id = transaction_id
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        ephemeral_public_key = dictionary.get('ephemeral_public_key')
        public_key_hash = dictionary.get('public_key_hash')
        transaction_id = dictionary.get('transaction_id')
        return cls(ephemeral_public_key, public_key_hash, transaction_id)
<|end_body_1|>

Solution:
class CreateApplePayHeaderRequest:
    """Implementation of the 'CreateApplePayHeaderRequest' model. The ApplePay header request Attributes: public_key_hash (string): SHA–256 hash, Base64 string codified ephemeral_public_key (string): X.509 encoded key bytes, Base64 encoded as a string transaction_id (string): Transaction identifier, generated on Device"""

    def __init__(self, ephemeral_public_key=None, public_key_hash=None, transaction_id=None):
        """Constructor for the CreateApplePayHeaderRequest class"""
        self.public_key_hash = public_key_hash
        self.ephemeral_public_key = ephemeral_public_key
        self.transaction_id = transaction_id

    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
        ephemeral_public_key = dictionary.get('ephemeral_public_key')
        public_key_hash = dictionary.get('public_key_hash')
        transaction_id = dictionary.get('transaction_id')
        return cls(ephemeral_public_key, public_key_hash, transaction_id)