interpreter

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

Class description:
Commande 'etendue carte'.

Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre

Implement the Python class `PrmCarte` described below.

Class description:
Commande 'etendue carte'.

Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre
- def interpreter(self, personnage, dic_masques): Interprétation du paramètre

<|skeleton|>
class PrmCarte:
    """Commande 'etendue carte'."""

    def __init__(self):
        """Constructeur du paramètre"""
        <|body_0|>

    def interpreter(self, personnage, dic_masques):
        """Interprétation du paramètre"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        Parametre.__init__(self, 'carte', 'map')
        self.schema = '<cle>'
        self.aide_courte = "affiche la carte de l'étendue"
        self.aide_longue = "Cette commande permet d'afficher un contexte représentant la carte (tronquée) de l'étendue. Les obstacles et les liens peuvent être édités simplement ici. Le contexte en lui-même propose beaucoup d'options et peut être difficile à manipuler pour commencer (certaines étendues sont bien plus grandes que la carte de base et il faut apprendre à naviguer dedans) mais de l'aide est mise à disposition pour vous aider à comprendre les différentes possibilités d'édition."
<|end_body_0|>

<|body_start_1|>
        cle = dic_masques['cle'].cle
        if cle not in type(self).importeur.salle.etendues.keys():
            personnage << "|err|Cette clé {} n'existe pas.|ff|".format(repr(cle))
            return
        etendue = type(self).importeur.salle.etendues[cle]
        if not personnage.salle.coords.valide:
            personnage << "|err|La salle où vous vous trouvez n'a pas de coordonnées valides.|ff|"
            return
        x = int(personnage.salle.coords.x) - 15
        y = int(personnage.salle.coords.y) - 8
        contexte = CarteEtendue(personnage.instance_connexion, x, y)
        contexte.etendue = etendue
        personnage.contexte_actuel.migrer_contexte(contexte)
<|end_body_1|>

Solution:
class PrmCarte:
    """Commande 'etendue carte'."""

    def __init__(self):
        """Constructeur du paramètre"""
        Parametre.__init__(self, 'carte', 'map')
        self.schema = '<cle>'
        self.aide_courte = "affiche la carte de l'étendue"
        self.aide_longue = "Cette commande permet d'afficher un contexte représentant la carte (tronquée) de l'étendue. Les obstacles et les liens peuvent être édités simplement ici. Le contexte en lui-même propose beaucoup d'options et peut être difficile à manipuler pour commencer (certaines étendues sont bien plus grandes que la carte de base et il faut apprendre à naviguer dedans) mais de l'aide est mise à disposition pour vous aider à comprendre les différentes possibilités d'édition."

    def interpreter(self, personnage, dic_masques):
        """Interprétation du paramètre"""
        cle = dic_masques['cle'].cle
        if cle not in type(self).importeur.salle.etendues.keys():
            personnage << "|err|Cette clé {} n'existe pas.|ff|".format(repr(cle))
            return
        etendue = type(self).importeur.salle.etendues[cle]
        if not personnage.salle.coords.valide:
            personnage << "|err|La salle où vous vous trouvez n'a pas de coordonnées valides.|ff|"
            return
        x = int(personnage.salle.coords.x) - 15
        y = int(personnage.salle.coords.y) - 8
        contexte = CarteEtendue(personnage.instance_connexion, x, y)
        contexte.etendue = etendue
        personnage.contexte_actuel.migrer_contexte(contexte)


forward

Prompt:
Implement the Python class `YOLOv1Postprocess` described below.

Class description:
Implement the YOLOv1Postprocess class.

Method signatures and docstrings:
- def __init__(self, num_classes, num_grid, num_bbox, image_shapes, num_threshold): Constructor for YOLOv1Postprocess
- def boxes_decode(self, yolo_bbox, image_shape): Args: yolo_bbox: (Tensor[num_grid**2, 5]) image_shape: Tuple(height, weight) Returns: transformed_boxes: (Tensor[N, num_grid**2, 5])
- def postprocess_detections(self, class_logits, proposals, image_shapes): Args: class_logits: (Tensor[N, num_grid**2, classes]), proposals: (Tensor[N, num_grid**2, 5]) image_shapes: (Tuple[height, width]): size of the image Returns:
- def forward(self, x): Args: x: Tensor(N, out_features) out_features = num_grid**2 * (num_classes + num_bbox * 5) Returns: all_boxes: List(Tensor) all_scores: List(Tensor) all_masks: List(Dict(Tensor))

Implement the Python class `YOLOv1Postprocess` described below.

Class description:
Implement the YOLOv1Postprocess class.

Method signatures and docstrings:
- def __init__(self, num_classes, num_grid, num_bbox, image_shapes, num_threshold): Constructor for YOLOv1Postprocess
- def boxes_decode(self, yolo_bbox, image_shape): Args: yolo_bbox: (Tensor[num_grid**2, 5]) image_shape: Tuple(height, weight) Returns: transformed_boxes: (Tensor[N, num_grid**2, 5])
- def postprocess_detections(self, class_logits, proposals, image_shapes): Args: class_logits: (Tensor[N, num_grid**2, classes]), proposals: (Tensor[N, num_grid**2, 5]) image_shapes: (Tuple[height, width]): size of the image Returns:
- def forward(self, x): Args: x: Tensor(N, out_features) out_features = num_grid**2 * (num_classes + num_bbox * 5) Returns: all_boxes: List(Tensor) all_scores: List(Tensor) all_masks: List(Dict(Tensor))

<|skeleton|>
class YOLOv1Postprocess:

    def __init__(self, num_classes, num_grid, num_bbox, image_shapes, num_threshold):
        """Constructor for YOLOv1Postprocess"""
        <|body_0|>

    def boxes_decode(self, yolo_bbox, image_shape):
        """Args: yolo_bbox: (Tensor[num_grid**2, 5]) image_shape: Tuple(height, weight) Returns: transformed_boxes: (Tensor[N, num_grid**2, 5])"""
        <|body_1|>

    def postprocess_detections(self, class_logits, proposals, image_shapes):
        """Args: class_logits: (Tensor[N, num_grid**2, classes]), proposals: (Tensor[N, num_grid**2, 5]) image_shapes: (Tuple[height, width]): size of the image Returns:"""
        <|body_2|>

    def forward(self, x):
        """Args: x: Tensor(N, out_features) out_features = num_grid**2 * (num_classes + num_bbox * 5) Returns: all_boxes: List(Tensor) all_scores: List(Tensor) all_masks: List(Dict(Tensor))"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.num_classes = num_classes
        self.num_grid = num_grid
        self.num_bbox = num_bbox
        self.image_shapes = image_shapes
        self.num_threshold = num_threshold
<|end_body_0|>

<|body_start_1|>
        dtype = yolo_bbox.dtype
        device = yolo_bbox.device
        H, W = image_shape
        grid_idx = torch.arange(self.num_grid, dtype=dtype, device=device).unsqueeze(1)
        x_grid = grid_idx.repeat(self.num_grid, 1).flatten()
        y_grid = grid_idx.repeat(1, self.num_grid).flatten()
        x0 = ((yolo_bbox[:, 0] + x_grid) / self.num_grid - yolo_bbox[:, 2] / 2).unsqueeze(1) * W
        y0 = ((yolo_bbox[:, 1] + y_grid) / self.num_grid - yolo_bbox[:, 3] / 2).unsqueeze(1) * H
        x1 = ((yolo_bbox[:, 0] + x_grid) / self.num_grid + yolo_bbox[:, 2] / 2).unsqueeze(1) * W
        y1 = ((yolo_bbox[:, 1] + y_grid) / self.num_grid + yolo_bbox[:, 3] / 2).unsqueeze(1) * H
        return torch.cat((x0, y0, x1, y1, yolo_bbox[:, 4].unsqueeze(1)), dim=1)
<|end_body_1|>

<|body_start_2|>
        device = class_logits.device
        num_classes = class_logits.shape[-1]
        pred_probs = F.softmax(class_logits, -1)
        all_masks = []
        all_boxes = []
        image_shape = image_shapes
        for pred_boxes, probs in zip(proposals, pred_probs):
            boxes = self.boxes_decode(pred_boxes, image_shape)
            reserve_mask = boxes[:, -1] > 0.1
            boxes = boxes[reserve_mask]
            probs = probs[reserve_mask]
            boxes, scores = (boxes[:, :4], boxes[:, -1])
            boxes = detection_utils.clip_boxes_to_image(boxes, image_shape)
            labels = torch.argmax(probs, dim=-1)
            boxes = boxes.reshape(-1, 4)
            scores = scores.flatten()
            labels = labels.flatten()
            keep_mask = detection_utils.batched_nms(boxes, scores, labels, self.num_threshold)
            boxes, scores = (boxes[keep_mask], scores[keep_mask])
            mask_dict = {'reserve_mask': reserve_mask, 'keep_mask': keep_mask}
            all_boxes.append(boxes)
            all_masks.append(mask_dict)
        return (all_boxes, all_masks)
<|end_body_2|>

<|body_start_3|>
        N = x.shape[0]
        x = x.view(N, -1, self.num_classes + 5 * self.num_bbox)
        class_logits = x[..., :self.num_classes]
        yolo_bboxes = x[..., self.num_classes:].view(-1, self.num_bbox, 5)
        yolo_bboxes = yolo_bboxes[range(N * self.num_grid ** 2), yolo_bboxes[..., -1].max(-1)[1]].reshape(N, -1, 5)
        pred_boxes, pred_masks = self.postprocess_detections(class_logits, yolo_bboxes, self.image_shapes)
        return (class_logits, yolo_bboxes, pred_boxes, pred_masks)
<|end_body_3|>

Solution:
class YOLOv1Postprocess:
    def __init__(self, num_classes, num_grid, num_bbox, image_shapes, num_threshold):
        """Constructor for YOLOv1Postprocess"""
        super().__init__()
        self.num_classes = num_classes
        self.num_grid = num_grid
        self.num_bbox = num_bbox
        self.image_shapes = image_shapes
        self.num_threshold = num_threshold

    def boxes_decode(self, yolo_bbox, image_shape):
        """Args: yolo_bbox: (Tensor[num_grid**2, 5]) image_shape: Tuple(height, weight) Returns: transformed_boxes: (Tensor[N, num_grid**2, 5])"""
        dtype = yolo_bbox.dtype
        device = yolo_bbox.device
        H, W = image_shape
        grid_idx = torch.arange(self.num_grid, dtype=dtype, device=device).unsqueeze(1)
        x_grid = grid_idx.repeat(self.num_grid, 1).flatten()
        y_grid = grid_idx.repeat(1, self.num_grid).flatten()
        x0 = ((yolo_bbox[:, 0] + x_grid) / self.num_grid - yolo_bbox[:, 2] / 2).unsqueeze(1) * W
        y0 = ((yolo_bbox[:, 1] + y_grid) / self.num_grid - yolo_bbox[:, 3] / 2).unsqueeze(1) * H
        x1 = ((yolo_bbox[:, 0] + x_grid) / self.num_grid + yolo_bbox[:, 2] / 2).unsqueeze(1) * W
        y1 = ((yolo_bbox[:, 1] + y_grid) / self.num_grid + yolo_bbox[:, 3] / 2).unsqueeze(1) * H
        return torch.cat((x0, y0, x1, y1, yolo_bbox[:, 4].unsqueeze(1)), dim=1)

    def postprocess_detections(self, class_logits, proposals, image_shapes):
        """Args: class_logits: (Tensor[N, num_grid**2, classes]), proposals: (Tensor[N, num_grid**2, 5]) image_shapes: (Tuple[height, width]): size of the image Returns:"""
        device = class_logits.device
        num_classes = class_logits.shape[-1]
        pred_probs = F.softmax(class_logits, -1)
        all_masks = []
        all_boxes = []
        image_shape = image_shapes
        for pred_boxes, probs in zip(proposals, pred_probs):
            boxes = self.boxes_decode(pred_boxes, image_shape)
            reserve_mask = boxes[:, -1] > 0.1
            boxes = boxes[reserve_mask]
            probs = probs[reserve_mask]
            boxes, scores = (boxes[:, :4], boxes[:, -1])
            boxes = detection_utils.clip_boxes_to_image(boxes, image_shape)
            labels = torch.argmax(probs, dim=-1)
            boxes = boxes.reshape(-1, 4)
            scores = scores.flatten()
            labels = labels.flatten()
            keep_mask = detection_utils.batched_nms(boxes, scores, labels, self.num_threshold)
            boxes, scores = (boxes[keep_mask], scores[keep_mask])
            mask_dict = {'reserve_mask': reserve_mask, 'keep_mask': keep_mask}
            all_boxes.append(boxes)
            all_masks.append(mask_dict)
        return (all_boxes, all_masks)

    def forward(self, x):
        """Args: x: Tensor(N, out_features) out_features = num_grid**2 * (num_classes + num_bbox * 5) Returns: all_boxes: List(Tensor) all_scores: List(Tensor) all_masks: List(Dict(Tensor))"""
        N = x.shape[0]
        x = x.view(N, -1, self.num_classes + 5 * self.num_bbox)
        class_logits = x[..., :self.num_classes]
        yolo_bboxes = x[..., self.num_classes:].view(-1, self.num_bbox, 5)
        yolo_bboxes = yolo_bboxes[range(N * self.num_grid ** 2), yolo_bboxes[..., -1].max(-1)[1]].reshape(N, -1, 5)
        pred_boxes, pred_masks = self.postprocess_detections(class_logits, yolo_bboxes, self.image_shapes)
        return (class_logits, yolo_bboxes, pred_boxes, pred_masks)


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

Class description:
Form representing sub task

Method signatures and docstrings:
- def __init__(self, *args, **kwargs): choice field values willl be filled during form load
- def save(self, task): save the sub task for a task

Implement the Python class `SubTaskForm` described below.

Class description:
Form representing sub task

Method signatures and docstrings:
- def __init__(self, *args, **kwargs): choice field values willl be filled during form load
- def save(self, task): save the sub task for a task

<|skeleton|>
class SubTaskForm:
    """Form representing sub task"""

    def __init__(self, *args, **kwargs):
        """choice field values willl be filled during form load"""
        <|body_0|>

    def save(self, task):
        """save the sub task for a task"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        task = kwargs.pop('task', None)
        super(self.__class__, self).__init__(*args, **kwargs)
        self.fields['sub_tasks'].queryset = Task.objects.filter(parent=task) if task else Task.objects.none()
<|end_body_0|>

<|body_start_1|>
        sub_tasks = self.cleaned_data['sub_tasks']
        for sub_task in sub_tasks:
            if task.parent == sub_task:
                continue
            sub_task.parent = task
            sub_task.save()
<|end_body_1|>

Solution:
class SubTaskForm:
    """Form representing sub task"""

    def __init__(self, *args, **kwargs):
        """choice field values willl be filled during form load"""
        task = kwargs.pop('task', None)
        super(self.__class__, self).__init__(*args, **kwargs)
        self.fields['sub_tasks'].queryset = Task.objects.filter(parent=task) if task else Task.objects.none()

    def save(self, task):
        """save the sub task for a task"""
        sub_tasks = self.cleaned_data['sub_tasks']
        for sub_task in sub_tasks:
            if task.parent == sub_task:
                continue
            sub_task.parent = task
            sub_task.save()


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

Class description:
Implement the AddFlavourSerializers class.

Method signatures and docstrings:
- def validate_flavour(self, value): Check the duplicate
- def to_representation(self, instance): Convert `flavour` to lowercase.

Implement the Python class `AddFlavourSerializers` described below.

Class description:
Implement the AddFlavourSerializers class.

Method signatures and docstrings:
- def validate_flavour(self, value): Check the duplicate
- def to_representation(self, instance): Convert `flavour` to lowercase.

<|skeleton|>
class AddFlavourSerializers:

    def validate_flavour(self, value):
        """Check the duplicate"""
        <|body_0|>

    def to_representation(self, instance):
        """Convert `flavour` to lowercase."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if Flavour.objects.filter(flavour=value.lower()):
            raise serializers.ValidationError('There already exist such flavour')
        return value
<|end_body_0|>

<|body_start_1|>
        ret = super().to_representation(instance)
        ret['flavour'] = ret['flavour'].lower()
        return ret
<|end_body_1|>

Solution:
class AddFlavourSerializers:
    def validate_flavour(self, value):
        """Check the duplicate"""
        if Flavour.objects.filter(flavour=value.lower()):
            raise serializers.ValidationError('There already exist such flavour')
        return value

    def to_representation(self, instance):
        """Convert `flavour` to lowercase."""
        ret = super().to_representation(instance)
        ret['flavour'] = ret['flavour'].lower()
        return ret


dispatch

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

Class description:
View for changing a reservation (Cannot be UpdateView due to the abstract inheritance of reservations)

Method signatures and docstrings:
- def dispatch(self, request, *args, **kwargs): Redirects the user to it's reservation page if the given reservation cannot be changed :param request: The HTTP request
- def form_valid(self, form, **kwargs): Handles updating the reservation if the form is valid, otherwise render the form view with an error code :param form: The valid form :return HTTP Response

Implement the Python class `ChangeReservationView` described below.

Class description:
View for changing a reservation (Cannot be UpdateView due to the abstract inheritance of reservations)

Method signatures and docstrings:
- def dispatch(self, request, *args, **kwargs): Redirects the user to it's reservation page if the given reservation cannot be changed :param request: The HTTP request
- def form_valid(self, form, **kwargs): Handles updating the reservation if the form is valid, otherwise render the form view with an error code :param form: The valid form :return HTTP Response

<|skeleton|>
class ChangeReservationView:
    """View for changing a reservation (Cannot be UpdateView due to the abstract inheritance of reservations)"""

    def dispatch(self, request, *args, **kwargs):
        """Redirects the user to it's reservation page if the given reservation cannot be changed :param request: The HTTP request"""
        <|body_0|>

    def form_valid(self, form, **kwargs):
        """Handles updating the reservation if the form is valid, otherwise render the form view with an error code :param form: The valid form :return HTTP Response"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not kwargs['reservation'].can_change(request.user) and (not kwargs['reservation'].can_change_end_time(request.user)):
            return redirect('my_reservations')
        return super().dispatch(request, *args, **kwargs)
<|end_body_0|>

<|body_start_1|>
        reservation = kwargs['reservation']
        if reservation.machine != form.cleaned_data['machine']:
            return redirect('my_reservations')
        reservation.comment = form.cleaned_data['comment']
        reservation.start_time = form.cleaned_data['start_time']
        reservation.end_time = form.cleaned_data['end_time']
        if reservation.event:
            reservation.event = form.cleaned_data['event']
        if reservation.special:
            reservation.special_text = form.cleaned_data['special_text']
        return self.validate_and_save(reservation, form)
<|end_body_1|>

Solution:
class ChangeReservationView:
    """View for changing a reservation (Cannot be UpdateView due to the abstract inheritance of reservations)"""

    def dispatch(self, request, *args, **kwargs):
        """Redirects the user to it's reservation page if the given reservation cannot be changed :param request: The HTTP request"""
        if not kwargs['reservation'].can_change(request.user) and (not kwargs['reservation'].can_change_end_time(request.user)):
            return redirect('my_reservations')
        return super().dispatch(request, *args, **kwargs)

    def form_valid(self, form, **kwargs):
        """Handles updating the reservation if the form is valid, otherwise render the form view with an error code :param form: The valid form :return HTTP Response"""
        reservation = kwargs['reservation']
        if reservation.machine != form.cleaned_data['machine']:
            return redirect('my_reservations')
        reservation.comment = form.cleaned_data['comment']
        reservation.start_time = form.cleaned_data['start_time']
        reservation.end_time = form.cleaned_data['end_time']
        if reservation.event:
            reservation.event = form.cleaned_data['event']
        if reservation.special:
            reservation.special_text = form.cleaned_data['special_text']
        return self.validate_and_save(reservation, form)


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

Class description:
Implementation of the 'PhysicalProtectionSource' model. Specifies a Protection Source in a Physical environment. Attributes: agents (list of AgentInformation): Array of Agents on the Physical Protection Source. Specifiles the agents running on the Physical Protection Source and the status information. cluster_source_type (string): Specifies the type of cluster resource this source represents. host_name (string): Specifies the hostname. host_type (HostTypeEnum): Specifies the environment type for the host. 'kLinux' indicates the Linux operating system. 'kWindows' indicates the Microsoft Windows operating system. 'kAix' indicates the IBM AIX operating system. 'kSolaris' indicates the Oracle So

Method signatures and docstrings:
- def __init__(self, agents=None, cluster_source_type=None, host_name=None, host_type=None, id=None, is_proxy_host=None, memory_size_bytes=None, name=None, networking_info=None, num_processors=None, os_name=None, mtype=None, vcs_version=None, volumes=None, vsswriters=None): Constructor for the PhysicalProtectionSource 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 `PhysicalProtectionSource` described below.

Class description:
Implementation of the 'PhysicalProtectionSource' model. Specifies a Protection Source in a Physical environment. Attributes: agents (list of AgentInformation): Array of Agents on the Physical Protection Source. Specifiles the agents running on the Physical Protection Source and the status information. cluster_source_type (string): Specifies the type of cluster resource this source represents. host_name (string): Specifies the hostname. host_type (HostTypeEnum): Specifies the environment type for the host. 'kLinux' indicates the Linux operating system. 'kWindows' indicates the Microsoft Windows operating system. 'kAix' indicates the IBM AIX operating system. 'kSolaris' indicates the Oracle So

Method signatures and docstrings:
- def __init__(self, agents=None, cluster_source_type=None, host_name=None, host_type=None, id=None, is_proxy_host=None, memory_size_bytes=None, name=None, networking_info=None, num_processors=None, os_name=None, mtype=None, vcs_version=None, volumes=None, vsswriters=None): Constructor for the PhysicalProtectionSource 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 PhysicalProtectionSource:
    """Implementation of the 'PhysicalProtectionSource' model. Specifies a Protection Source in a Physical environment. Attributes: agents (list of AgentInformation): Array of Agents on the Physical Protection Source. Specifiles the agents running on the Physical Protection Source and the status information. cluster_source_type (string): Specifies the type of cluster resource this source represents. host_name (string): Specifies the hostname. host_type (HostTypeEnum): Specifies the environment type for the host. 'kLinux' indicates the Linux operating system. 'kWindows' indicates the Microsoft Windows operating system. 'kAix' indicates the IBM AIX operating system. 'kSolaris' indicates the Oracle So"""

    def __init__(self, agents=None, cluster_source_type=None, host_name=None, host_type=None, id=None, is_proxy_host=None, memory_size_bytes=None, name=None, networking_info=None, num_processors=None, os_name=None, mtype=None, vcs_version=None, volumes=None, vsswriters=None):
        """Constructor for the PhysicalProtectionSource 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.agents = agents
        self.cluster_source_type = cluster_source_type
        self.host_name = host_name
        self.host_type = host_type
        self.id = id
        self.is_proxy_host = is_proxy_host
        self.memory_size_bytes = memory_size_bytes
        self.name = name
        self.networking_info = networking_info
        self.num_processors = num_processors
        self.os_name = os_name
        self.mtype = mtype
        self.vcs_version = vcs_version
        self.volumes = volumes
        self.vsswriters = vsswriters
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        agents = None
        if dictionary.get('agents') != None:
            agents = list()
            for structure in dictionary.get('agents'):
                agents.append(cohesity_management_sdk.models.agent_information.AgentInformation.from_dictionary(structure))
        cluster_source_type = dictionary.get('clusterSourceType')
        host_name = dictionary.get('hostName')
        host_type = dictionary.get('hostType')
        id = cohesity_management_sdk.models.universal_id.UniversalId.from_dictionary(dictionary.get('id')) if dictionary.get('id') else None
        is_proxy_host = dictionary.get('isProxyHost')
        memory_size_bytes = dictionary.get('memorySizeBytes')
        name = dictionary.get('name')
        networking_info = cohesity_management_sdk.models.networking_information.NetworkingInformation.from_dictionary(dictionary.get('networkingInfo')) if dictionary.get('networkingInfo') else None
        num_processors = dictionary.get('numProcessors')
        os_name = dictionary.get('osName')
        mtype = dictionary.get('type')
        vcs_version = dictionary.get('vcsVersion')
        volumes = None
        if dictionary.get('volumes') != None:
            volumes = list()
            for structure in dictionary.get('volumes'):
                volumes.append(cohesity_management_sdk.models.physical_volume.PhysicalVolume.from_dictionary(structure))
        vsswriters = None
        if dictionary.get('vsswriters') != None:
            vsswriters = list()
            for structure in dictionary.get('vsswriters'):
                vsswriters.append(cohesity_management_sdk.models.vss_writer.VssWriter.from_dictionary(structure))
        return cls(agents, cluster_source_type, host_name, host_type, id, is_proxy_host, memory_size_bytes, name, networking_info, num_processors, os_name, mtype, vcs_version, volumes, vsswriters)
<|end_body_1|>

Solution:
class PhysicalProtectionSource:
    """Implementation of the 'PhysicalProtectionSource' model. Specifies a Protection Source in a Physical environment. Attributes: agents (list of AgentInformation): Array of Agents on the Physical Protection Source. Specifiles the agents running on the Physical Protection Source and the status information. cluster_source_type (string): Specifies the type of cluster resource this source represents. host_name (string): Specifies the hostname. host_type (HostTypeEnum): Specifies the environment type for the host. 'kLinux' indicates the Linux operating system. 'kWindows' indicates the Microsoft Windows operating system. 'kAix' indicates the IBM AIX operating system. 'kSolaris' indicates the Oracle So"""

    def __init__(self, agents=None, cluster_source_type=None, host_name=None, host_type=None, id=None, is_proxy_host=None, memory_size_bytes=None, name=None, networking_info=None, num_processors=None, os_name=None, mtype=None, vcs_version=None, volumes=None, vsswriters=None):
        """Constructor for the PhysicalProtectionSource class"""
        self.agents = agents
        self.cluster_source_type = cluster_source_type
        self.host_name = host_name
        self.host_type = host_type
        self.id = id
        self.is_proxy_host = is_proxy_host
        self.memory_size_bytes = memory_size_bytes
        self.name = name
        self.networking_info = networking_info
        self.num_processors = num_processors
        self.os_name = os_name
        self.mtype = mtype
        self.vcs_version = vcs_version
        self.volumes = volumes
        self.vsswriters = vsswriters

    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
        agents = None
        if dictionary.get('agents') != None:
            agents = list()
            for structure in dictionary.get('agents'):
                agents.append(cohesity_management_sdk.models.agent_information.AgentInformation.from_dictionary(structure))
        cluster_source_type = dictionary.get('clusterSourceType')
        host_name = dictionary.get('hostName')
        host_type = dictionary.get('hostType')
        id = cohesity_management_sdk.models.universal_id.UniversalId.from_dictionary(dictionary.get('id')) if dictionary.get('id') else None
        is_proxy_host = dictionary.get('isProxyHost')
        memory_size_bytes = dictionary.get('memorySizeBytes')
        name = dictionary.get('name')
        networking_info = cohesity_management_sdk.models.networking_information.NetworkingInformation.from_dictionary(dictionary.get('networkingInfo')) if dictionary.get('networkingInfo') else None
        num_processors = dictionary.get('numProcessors')
        os_name = dictionary.get('osName')
        mtype = dictionary.get('type')
        vcs_version = dictionary.get('vcsVersion')
        volumes = None
        if dictionary.get('volumes') != None:
            volumes = list()
            for structure in dictionary.get('volumes'):
                volumes.append(cohesity_management_sdk.models.physical_volume.PhysicalVolume.from_dictionary(structure))
        vsswriters = None
        if dictionary.get('vsswriters') != None:
            vsswriters = list()
            for structure in dictionary.get('vsswriters'):
                vsswriters.append(cohesity_management_sdk.models.vss_writer.VssWriter.from_dictionary(structure))
        return cls(agents, cluster_source_type, host_name, host_type, id, is_proxy_host, memory_size_bytes, name, networking_info, num_processors, os_name, mtype, vcs_version, volumes, vsswriters)


backward

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

Class description:
A layer that implements the inner product.

Method signatures and docstrings:
- def __init__(self, **kwargs): Initializes an inner product layer. kwargs: num_output: the number of outputs. reg: the regularizer to be used to add regularization terms. should be a decaf.base.Regularizer instance. Default None. filler: a filler to initialize the weights. Should be a decaf.base.Filler instance. Default None. bias_filler: a filler to initialize the bias. bias: if True, the inner product will contain a bias term. Default True.
- def forward(self, bottom, top): Computes the forward pass.
- def backward(self, bottom, top, propagate_down): Computes the backward pass.
- def update(self): Updates the parameters.

Implement the Python class `InnerProductLayer` described below.

Class description:
A layer that implements the inner product.

Method signatures and docstrings:
- def __init__(self, **kwargs): Initializes an inner product layer. kwargs: num_output: the number of outputs. reg: the regularizer to be used to add regularization terms. should be a decaf.base.Regularizer instance. Default None. filler: a filler to initialize the weights. Should be a decaf.base.Filler instance. Default None. bias_filler: a filler to initialize the bias. bias: if True, the inner product will contain a bias term. Default True.
- def forward(self, bottom, top): Computes the forward pass.
- def backward(self, bottom, top, propagate_down): Computes the backward pass.
- def update(self): Updates the parameters.

<|skeleton|>
class InnerProductLayer:
    """A layer that implements the inner product."""

    def __init__(self, **kwargs):
        """Initializes an inner product layer. kwargs: num_output: the number of outputs. reg: the regularizer to be used to add regularization terms. should be a decaf.base.Regularizer instance. Default None. filler: a filler to initialize the weights. Should be a decaf.base.Filler instance. Default None. bias_filler: a filler to initialize the bias. bias: if True, the inner product will contain a bias term. Default True."""
        <|body_0|>

    def forward(self, bottom, top):
        """Computes the forward pass."""
        <|body_1|>

    def backward(self, bottom, top, propagate_down):
        """Computes the backward pass."""
        <|body_2|>

    def update(self):
        """Updates the parameters."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        base.Layer.__init__(self, **kwargs)
        self._num_output = self.spec.get('num_output', 0)
        if self._num_output <= 0:
            raise base.InvalidLayerError('Incorrect or unspecified num_output for %s' % self.name)
        self._reg = self.spec.get('reg', None)
        self._filler = self.spec.get('filler', None)
        self._weight = base.Blob(filler=self._filler)
        self._has_bias = self.spec.get('bias', True)
        if self._has_bias:
            self._bias_filler = self.spec.get('bias_filler', None)
            self._bias = base.Blob(filler=self._bias_filler)
            self._param = [self._weight, self._bias]
        else:
            self._param = [self._weight]
<|end_body_0|>

<|body_start_1|>
        features = bottom[0].data()
        output = top[0].init_data(features.shape[:-1] + (self._num_output,), features.dtype, setdata=False)
        if not self._weight.has_data():
            self._weight.init_data((features.shape[-1], self._num_output), features.dtype)
        if self._has_bias and (not self._bias.has_data()):
            self._bias.init_data(self._num_output, features.dtype)
        weight = self._weight.data()
        blasdot.dot_lastdim(features, weight, out=output)
        if self._has_bias:
            output += self._bias.data()
<|end_body_1|>

<|body_start_2|>
        top_diff = top[0].diff()
        features = bottom[0].data()
        weight_diff = self._weight.init_diff(setzero=False)
        blasdot.dot_firstdims(features, top_diff, out=weight_diff)
        if self._has_bias:
            bias_diff = self._bias.init_diff(setzero=False)
            bias_diff[:] = top_diff.reshape(np.prod(top_diff.shape[:-1]), top_diff.shape[-1]).sum(0)
        if propagate_down:
            bottom_diff = bottom[0].init_diff(setzero=False)
            blasdot.dot_lastdim(top_diff, self._weight.data().T, out=bottom_diff)
        if self._reg is not None:
            return self._reg.reg(self._weight)
        else:
            return 0.0
<|end_body_2|>

<|body_start_3|>
        self._weight.update()
        if self._has_bias:
            self._bias.update()
<|end_body_3|>

Solution:
class InnerProductLayer:
    """A layer that implements the inner product."""

    def __init__(self, **kwargs):
        """Initializes an inner product layer. kwargs: num_output: the number of outputs. reg: the regularizer to be used to add regularization terms. should be a decaf.base.Regularizer instance. Default None. filler: a filler to initialize the weights. Should be a decaf.base.Filler instance. Default None. bias_filler: a filler to initialize the bias. bias: if True, the inner product will contain a bias term. Default True."""
        base.Layer.__init__(self, **kwargs)
        self._num_output = self.spec.get('num_output', 0)
        if self._num_output <= 0:
            raise base.InvalidLayerError('Incorrect or unspecified num_output for %s' % self.name)
        self._reg = self.spec.get('reg', None)
        self._filler = self.spec.get('filler', None)
        self._weight = base.Blob(filler=self._filler)
        self._has_bias = self.spec.get('bias', True)
        if self._has_bias:
            self._bias_filler = self.spec.get('bias_filler', None)
            self._bias = base.Blob(filler=self._bias_filler)
            self._param = [self._weight, self._bias]
        else:
            self._param = [self._weight]

    def forward(self, bottom, top):
        """Computes the forward pass."""
        features = bottom[0].data()
        output = top[0].init_data(features.shape[:-1] + (self._num_output,), features.dtype, setdata=False)
        if not self._weight.has_data():
            self._weight.init_data((features.shape[-1], self._num_output), features.dtype)
        if self._has_bias and (not self._bias.has_data()):
            self._bias.init_data(self._num_output, features.dtype)
        weight = self._weight.data()
        blasdot.dot_lastdim(features, weight, out=output)
        if self._has_bias:
            output += self._bias.data()

    def backward(self, bottom, top, propagate_down):
        """Computes the backward pass."""
        top_diff = top[0].diff()
        features = bottom[0].data()
        weight_diff = self._weight.init_diff(setzero=False)
        blasdot.dot_firstdims(features, top_diff, out=weight_diff)
        if self._has_bias:
            bias_diff = self._bias.init_diff(setzero=False)
            bias_diff[:] = top_diff.reshape(np.prod(top_diff.shape[:-1]), top_diff.shape[-1]).sum(0)
        if propagate_down:
            bottom_diff = bottom[0].init_diff(setzero=False)
            blasdot.dot_lastdim(top_diff, self._weight.data().T, out=bottom_diff)
        if self._reg is not None:
            return self._reg.reg(self._weight)
        else:
            return 0.0

    def update(self):
        """Updates the parameters."""
        self._weight.update()
        if self._has_bias:
            self._bias.update()


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

Class description:
TestNumberCountAPI

Method signatures and docstrings:
- def setUp(self): setUp
- def test_MoE_number_count_static(self): test_MoE_number_count_static
- def test_MoE_number_count_dygraph(self): test_MoE_number_count_dygraph

Implement the Python class `TestNumberCountAPI` described below.

Class description:
TestNumberCountAPI

Method signatures and docstrings:
- def setUp(self): setUp
- def test_MoE_number_count_static(self): test_MoE_number_count_static
- def test_MoE_number_count_dygraph(self): test_MoE_number_count_dygraph

<|skeleton|>
class TestNumberCountAPI:
    """TestNumberCountAPI"""

    def setUp(self):
        """setUp"""
        <|body_0|>

    def test_MoE_number_count_static(self):
        """test_MoE_number_count_static"""
        <|body_1|>

    def test_MoE_number_count_dygraph(self):
        """test_MoE_number_count_dygraph"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.upper_num = 320
        self.x = np.random.randint(-1, self.upper_num, size=(6000, 200)).astype('int64')
        self.out = count(self.x, self.upper_num)
        self.place = paddle.CUDAPlace(0)
<|end_body_0|>

<|body_start_1|>
        paddle.enable_static()
        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data('x', self.x.shape, dtype='int64')
            out = utils._number_count(x, self.upper_num)
            exe = paddle.static.Executor(self.place)
            res = exe.run(feed={'x': self.x}, fetch_list=[out])
            assert np.allclose(res, self.out)
        print('test_MoE_number_count_static mode passed!')
<|end_body_1|>

<|body_start_2|>
        paddle.disable_static()
        x = paddle.to_tensor(self.x)
        out = utils._number_count(x, self.upper_num)
        assert np.allclose(out.numpy(), self.out)
        print('test_MoE_number_count_dygraph passed!')
<|end_body_2|>

Solution:
class TestNumberCountAPI:
    """TestNumberCountAPI"""

    def setUp(self):
        """setUp"""
        self.upper_num = 320
        self.x = np.random.randint(-1, self.upper_num, size=(6000, 200)).astype('int64')
        self.out = count(self.x, self.upper_num)
        self.place = paddle.CUDAPlace(0)

    def test_MoE_number_count_static(self):
        """test_MoE_number_count_static"""
        paddle.enable_static()
        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data('x', self.x.shape, dtype='int64')
            out = utils._number_count(x, self.upper_num)
            exe = paddle.static.Executor(self.place)
            res = exe.run(feed={'x': self.x}, fetch_list=[out])
            assert np.allclose(res, self.out)
        print('test_MoE_number_count_static mode passed!')

    def test_MoE_number_count_dygraph(self):
        """test_MoE_number_count_dygraph"""
        paddle.disable_static()
        x = paddle.to_tensor(self.x)
        out = utils._number_count(x, self.upper_num)
        assert np.allclose(out.numpy(), self.out)
        print('test_MoE_number_count_dygraph passed!')


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

Class description:
Define an object to retrieve Traccar data.

Method signatures and docstrings:
- def __init__(self, api: ApiClient, hass: HomeAssistant, async_see: AsyncSeeCallback, scan_interval: timedelta, max_accuracy: int, skip_accuracy_on: bool, custom_attributes: list[str], event_types: list[str]) -> None: Initialize.
- async def async_init(self): Further initialize connection to Traccar.
- async def _async_update(self, now=None): Update info from Traccar.
- async def import_device_data(self): Import device data from Traccar.
- async def import_events(self): Import events from Traccar.

Implement the Python class `TraccarScanner` described below.

Class description:
Define an object to retrieve Traccar data.

Method signatures and docstrings:
- def __init__(self, api: ApiClient, hass: HomeAssistant, async_see: AsyncSeeCallback, scan_interval: timedelta, max_accuracy: int, skip_accuracy_on: bool, custom_attributes: list[str], event_types: list[str]) -> None: Initialize.
- async def async_init(self): Further initialize connection to Traccar.
- async def _async_update(self, now=None): Update info from Traccar.
- async def import_device_data(self): Import device data from Traccar.
- async def import_events(self): Import events from Traccar.

<|skeleton|>
class TraccarScanner:
    """Define an object to retrieve Traccar data."""

    def __init__(self, api: ApiClient, hass: HomeAssistant, async_see: AsyncSeeCallback, scan_interval: timedelta, max_accuracy: int, skip_accuracy_on: bool, custom_attributes: list[str], event_types: list[str]) -> None:
        """Initialize."""
        <|body_0|>

    async def async_init(self):
        """Further initialize connection to Traccar."""
        <|body_1|>

    async def _async_update(self, now=None):
        """Update info from Traccar."""
        <|body_2|>

    async def import_device_data(self):
        """Import device data from Traccar."""
        <|body_3|>

    async def import_events(self):
        """Import events from Traccar."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        if EVENT_ALL_EVENTS in event_types:
            event_types = EVENTS
        self._event_types = {camelcase(evt): evt for evt in event_types}
        self._custom_attributes = custom_attributes
        self._scan_interval = scan_interval
        self._async_see = async_see
        self._api = api
        self._hass = hass
        self._max_accuracy = max_accuracy
        self._skip_accuracy_on = skip_accuracy_on
        self._devices: list[DeviceModel] = []
        self._positions: list[PositionModel] = []
        self._geofences: list[GeofenceModel] = []
<|end_body_0|>

<|body_start_1|>
        try:
            await self._api.get_server()
        except TraccarAuthenticationException:
            _LOGGER.error('Authentication for Traccar failed')
            return False
        except TraccarConnectionException as exception:
            _LOGGER.error('Connection with Traccar failed - %s', exception)
            return False
        await self._async_update()
        async_track_time_interval(self._hass, self._async_update, self._scan_interval, cancel_on_shutdown=True)
        return True
<|end_body_1|>

<|body_start_2|>
        _LOGGER.debug('Updating device data')
        try:
            self._devices, self._positions, self._geofences = await asyncio.gather(self._api.get_devices(), self._api.get_positions(), self._api.get_geofences())
        except TraccarException as ex:
            _LOGGER.error('Error while updating device data: %s', ex)
            return
        self._hass.async_create_task(self.import_device_data())
        if self._event_types:
            self._hass.async_create_task(self.import_events())
<|end_body_2|>

<|body_start_3|>
        for position in self._positions:
            device = next((dev for dev in self._devices if dev.id == position.device_id), None)
            if not device:
                continue
            attr = {ATTR_TRACKER: 'traccar', ATTR_ADDRESS: position.address, ATTR_SPEED: position.speed, ATTR_ALTITUDE: position.altitude, ATTR_MOTION: position.attributes.get('motion', False), ATTR_TRACCAR_ID: device.id, ATTR_GEOFENCE: next((geofence.name for geofence in self._geofences if geofence.id in (device.geofence_ids or [])), None), ATTR_CATEGORY: device.category, ATTR_STATUS: device.status}
            skip_accuracy_filter = False
            for custom_attr in self._custom_attributes:
                if device.attributes.get(custom_attr) is not None:
                    attr[custom_attr] = position.attributes[custom_attr]
                    if custom_attr in self._skip_accuracy_on:
                        skip_accuracy_filter = True
                if position.attributes.get(custom_attr) is not None:
                    attr[custom_attr] = position.attributes[custom_attr]
                    if custom_attr in self._skip_accuracy_on:
                        skip_accuracy_filter = True
            accuracy = position.accuracy or 0.0
            if not skip_accuracy_filter and self._max_accuracy > 0 and (accuracy > self._max_accuracy):
                _LOGGER.debug('Excluded position by accuracy filter: %f (%s)', accuracy, attr[ATTR_TRACCAR_ID])
                continue
            await self._async_see(dev_id=slugify(device.name), gps=(position.latitude, position.longitude), gps_accuracy=accuracy, battery=position.attributes.get('batteryLevel', -1), attributes=attr)
<|end_body_3|>

<|body_start_4|>
        start_intervel = dt_util.utcnow().replace(tzinfo=None)
        events = await self._api.get_reports_events(devices=[device.id for device in self._devices], start_time=start_intervel, end_time=start_intervel - self._scan_interval, event_types=self._event_types.keys())
        if events is not None:
            for event in events:
                self._hass.bus.async_fire(f'traccar_{self._event_types.get(event.type)}', {'device_traccar_id': event.device_id, 'device_name': next((dev.name for dev in self._devices if dev.id == event.device_id), None), 'type': event.type, 'serverTime': event.event_time, 'attributes': event.attributes})
<|end_body_4|>

Solution:
class TraccarScanner:
    """Define an object to retrieve Traccar data."""

    def __init__(self, api: ApiClient, hass: HomeAssistant, async_see: AsyncSeeCallback, scan_interval: timedelta, max_accuracy: int, skip_accuracy_on: bool, custom_attributes: list[str], event_types: list[str]) -> None:
        """Initialize."""
        if EVENT_ALL_EVENTS in event_types:
            event_types = EVENTS
        self._event_types = {camelcase(evt): evt for evt in event_types}
        self._custom_attributes = custom_attributes
        self._scan_interval = scan_interval
        self._async_see = async_see
        self._api = api
        self._hass = hass
        self._max_accuracy = max_accuracy
        self._skip_accuracy_on = skip_accuracy_on
        self._devices: list[DeviceModel] = []
        self._positions: list[PositionModel] = []
        self._geofences: list[GeofenceModel] = []

    async def async_init(self):
        """Further initialize connection to Traccar."""
        try:
            await self._api.get_server()
        except TraccarAuthenticationException:
            _LOGGER.error('Authentication for Traccar failed')
            return False
        except TraccarConnectionException as exception:
            _LOGGER.error('Connection with Traccar failed - %s', exception)
            return False
        await self._async_update()
        async_track_time_interval(self._hass, self._async_update, self._scan_interval, cancel_on_shutdown=True)
        return True

    async def _async_update(self, now=None):
        """Update info from Traccar."""
        _LOGGER.debug('Updating device data')
        try:
            self._devices, self._positions, self._geofences = await asyncio.gather(self._api.get_devices(), self._api.get_positions(), self._api.get_geofences())
        except TraccarException as ex:
            _LOGGER.error('Error while updating device data: %s', ex)
            return
        self._hass.async_create_task(self.import_device_data())
        if self._event_types:
            self._hass.async_create_task(self.import_events())

    async def import_device_data(self):
        """Import device data from Traccar."""
        for position in self._positions:
            device = next((dev for dev in self._devices if dev.id == position.device_id), None)
            if not device:
                continue
            attr = {ATTR_TRACKER: 'traccar', ATTR_ADDRESS: position.address, ATTR_SPEED: position.speed, ATTR_ALTITUDE: position.altitude, ATTR_MOTION: position.attributes.get('motion', False), ATTR_TRACCAR_ID: device.id, ATTR_GEOFENCE: next((geofence.name for geofence in self._geofences if geofence.id in (device.geofence_ids or [])), None), ATTR_CATEGORY: device.category, ATTR_STATUS: device.status}
            skip_accuracy_filter = False
            for custom_attr in self._custom_attributes:
                if device.attributes.get(custom_attr) is not None:
                    attr[custom_attr] = position.attributes[custom_attr]
                    if custom_attr in self._skip_accuracy_on:
                        skip_accuracy_filter = True
                if position.attributes.get(custom_attr) is not None:
                    attr[custom_attr] = position.attributes[custom_attr]
                    if custom_attr in self._skip_accuracy_on:
                        skip_accuracy_filter = True
            accuracy = position.accuracy or 0.0
            if not skip_accuracy_filter and self._max_accuracy > 0 and (accuracy > self._max_accuracy):
                _LOGGER.debug('Excluded position by accuracy filter: %f (%s)', accuracy, attr[ATTR_TRACCAR_ID])
                continue
            await self._async_see(dev_id=slugify(device.name), gps=(position.latitude, position.longitude), gps_accuracy=accuracy, battery=position.attributes.get('batteryLevel', -1), attributes=attr)

    async def import_events(self):
        """Import events from Traccar."""
        start_intervel = dt_util.utcnow().replace(tzinfo=None)
        events = await self._api.get_reports_events(devices=[device.id for device in self._devices], start_time=start_intervel, end_time=start_intervel - self._scan_interval, event_types=self._event_types.keys())
        if events is not None:
            for event in events:
                self._hass.bus.async_fire(f'traccar_{self._event_types.get(event.type)}', {'device_traccar_id': event.device_id, 'device_name': next((dev.name for dev in self._devices if dev.id == event.device_id), None), 'type': event.type, 'serverTime': event.event_time, 'attributes': event.attributes})


call

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

Class description:
Use convolution layer to downsample

Method signatures and docstrings:
- def __init__(self, filters, size, apply_batchnorm=True): The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_batchnorm If use batch normalization:
- def call(self, x, training): Calls the model on new inputs. Args: x: The input. training: If training. Returns: output.

Implement the Python class `Downsample` described below.

Class description:
Use convolution layer to downsample

Method signatures and docstrings:
- def __init__(self, filters, size, apply_batchnorm=True): The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_batchnorm If use batch normalization:
- def call(self, x, training): Calls the model on new inputs. Args: x: The input. training: If training. Returns: output.

<|skeleton|>
class Downsample:
    """Use convolution layer to downsample"""

    def __init__(self, filters, size, apply_batchnorm=True):
        """The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_batchnorm If use batch normalization:"""
        <|body_0|>

    def call(self, x, training):
        """Calls the model on new inputs. Args: x: The input. training: If training. Returns: output."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(Downsample, self).__init__()
        self.apply_batchnorm = apply_batchnorm
        initializer = tf.random_normal_initializer(0, 0.02)
        self.conv1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=(size, size), strides=(2, 2), padding='same', kernel_initializer=initializer, use_bias=False)
        if self.apply_batchnorm:
            self.batch_norm = tf.keras.layers.BatchNormalization()
<|end_body_0|>

<|body_start_1|>
        x = self.conv1(x)
        if self.apply_batchnorm:
            x = self.batch_norm(x, training=training)
        x = tf.nn.leaky_relu(x)
        return x
<|end_body_1|>

Solution:
class Downsample:
    """Use convolution layer to downsample"""

    def __init__(self, filters, size, apply_batchnorm=True):
        """The construct function. Args: filters: The convolution filters number. size: The convolution filter size. apply_batchnorm If use batch normalization:"""
        super(Downsample, self).__init__()
        self.apply_batchnorm = apply_batchnorm
        initializer = tf.random_normal_initializer(0, 0.02)
        self.conv1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=(size, size), strides=(2, 2), padding='same', kernel_initializer=initializer, use_bias=False)
        if self.apply_batchnorm:
            self.batch_norm = tf.keras.layers.BatchNormalization()

    def call(self, x, training):
        """Calls the model on new inputs. Args: x: The input. training: If training. Returns: output."""
        x = self.conv1(x)
        if self.apply_batchnorm:
            x = self.batch_norm(x, training=training)
        x = tf.nn.leaky_relu(x)
        return x


write

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

Class description:
inherit res.partner for adds the functionally that customer has a location

Method signatures and docstrings:
- def _set_partner_customer_location(self, cr, uid, partner_id, context=None): creates customer location for partner in arguments
- def create(self, cr, uid, vals, context=None): Check to create customer location
- def write(self, cr, uid, ids, vals, context=None): Check to create customer location

Implement the Python class `res_partner` described below.

Class description:
inherit res.partner for adds the functionally that customer has a location

Method signatures and docstrings:
- def _set_partner_customer_location(self, cr, uid, partner_id, context=None): creates customer location for partner in arguments
- def create(self, cr, uid, vals, context=None): Check to create customer location
- def write(self, cr, uid, ids, vals, context=None): Check to create customer location

<|skeleton|>
class res_partner:
    """inherit res.partner for adds the functionally that customer has a location"""

    def _set_partner_customer_location(self, cr, uid, partner_id, context=None):
        """creates customer location for partner in arguments"""
        <|body_0|>

    def create(self, cr, uid, vals, context=None):
        """Check to create customer location"""
        <|body_1|>

    def write(self, cr, uid, ids, vals, context=None):
        """Check to create customer location"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if context is None:
            context = {}
        partner_id_obj = self.browse(cr, uid, partner_id)
        locations = self.pool.get('stock.location').search(cr, uid, [('partner_id', '=', partner_id)])
        if not locations:
            partner_location_id = self.pool.get('stock.location').create(cr, uid, vals={'location_id': partner_id_obj.property_stock_customer and partner_id_obj.property_stock_customer.id or False, 'name': partner_id_obj.name, 'usage': 'customer', 'partner_id': partner_id, 'company_id': self.pool.get('res.users').browse(cr, uid, uid).company_id.id}, context=context)
            self.write(cr, uid, partner_id, vals={'property_stock_customer': partner_location_id}, context=context)
        return True
<|end_body_0|>

<|body_start_1|>
        if context is None:
            context = {}
        partner_id = super(res_partner, self).create(cr, uid, vals, context=context)
        if vals.get('create_customer_location', False):
            self._set_partner_customer_location(cr, uid, partner_id, context=context)
        return partner_id
<|end_body_1|>

<|body_start_2|>
        if context is None:
            context = {}
        res = super(res_partner, self).write(cr, uid, ids, vals, context=context)
        if vals.get('create_customer_location', False):
            if isinstance(ids, (int, long)):
                ids = [ids]
            for partner_id in ids:
                self._set_partner_customer_location(cr, uid, partner_id, context=context)
        return res
<|end_body_2|>

Solution:
class res_partner:
    """inherit res.partner for adds the functionally that customer has a location"""

    def _set_partner_customer_location(self, cr, uid, partner_id, context=None):
        """creates customer location for partner in arguments"""
        if context is None:
            context = {}
        partner_id_obj = self.browse(cr, uid, partner_id)
        locations = self.pool.get('stock.location').search(cr, uid, [('partner_id', '=', partner_id)])
        if not locations:
            partner_location_id = self.pool.get('stock.location').create(cr, uid, vals={'location_id': partner_id_obj.property_stock_customer and partner_id_obj.property_stock_customer.id or False, 'name': partner_id_obj.name, 'usage': 'customer', 'partner_id': partner_id, 'company_id': self.pool.get('res.users').browse(cr, uid, uid).company_id.id}, context=context)
            self.write(cr, uid, partner_id, vals={'property_stock_customer': partner_location_id}, context=context)
        return True

    def create(self, cr, uid, vals, context=None):
        """Check to create customer location"""
        if context is None:
            context = {}
        partner_id = super(res_partner, self).create(cr, uid, vals, context=context)
        if vals.get('create_customer_location', False):
            self._set_partner_customer_location(cr, uid, partner_id, context=context)
        return partner_id

    def write(self, cr, uid, ids, vals, context=None):
        """Check to create customer location"""
        if context is None:
            context = {}
        res = super(res_partner, self).write(cr, uid, ids, vals, context=context)
        if vals.get('create_customer_location', False):
            if isinstance(ids, (int, long)):
                ids = [ids]
            for partner_id in ids:
                self._set_partner_customer_location(cr, uid, partner_id, context=context)
        return res


load

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

Class description:
Implement the Param class.

Method signatures and docstrings:
- def __init__(self, name, type=str, required=False, default=None, nullable=True, ignore=False, choices=None, location=None, discard=False, help=''): 请求参数对象 Args: name (str): 字段名 type (class): 类型 required (bool): 是否必填 default (any): 默认值 (必填时无效) nullable (bool): 是否非空 ignore (bool): 是否忽略类型 choices (tuple): 可选值 location (str): 访问数据源 args, json, form 默认 GET: args, POST: json discard (bool): 不存在 key, 是否不解析参数 help (str): 参数不匹配时, 返回提示
- def load(self): 解析并检查参数取值 Returns: value(值), valid(是否有效)

Implement the Python class `Param` described below.

Class description:
Implement the Param class.

Method signatures and docstrings:
- def __init__(self, name, type=str, required=False, default=None, nullable=True, ignore=False, choices=None, location=None, discard=False, help=''): 请求参数对象 Args: name (str): 字段名 type (class): 类型 required (bool): 是否必填 default (any): 默认值 (必填时无效) nullable (bool): 是否非空 ignore (bool): 是否忽略类型 choices (tuple): 可选值 location (str): 访问数据源 args, json, form 默认 GET: args, POST: json discard (bool): 不存在 key, 是否不解析参数 help (str): 参数不匹配时, 返回提示
- def load(self): 解析并检查参数取值 Returns: value(值), valid(是否有效)

<|skeleton|>
class Param:

    def __init__(self, name, type=str, required=False, default=None, nullable=True, ignore=False, choices=None, location=None, discard=False, help=''):
        """请求参数对象 Args: name (str): 字段名 type (class): 类型 required (bool): 是否必填 default (any): 默认值 (必填时无效) nullable (bool): 是否非空 ignore (bool): 是否忽略类型 choices (tuple): 可选值 location (str): 访问数据源 args, json, form 默认 GET: args, POST: json discard (bool): 不存在 key, 是否不解析参数 help (str): 参数不匹配时, 返回提示"""
        <|body_0|>

    def load(self):
        """解析并检查参数取值 Returns: value(值), valid(是否有效)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.name = name
        self.type = type
        self.required = required
        self.default = default
        self.ignore = ignore
        self.choices = choices
        self.nullable = nullable
        self.location = location
        self.discard = discard
        self.help = help
<|end_body_0|>

<|body_start_1|>
        if self.location and hasattr(request, self.location):
            req_data = getattr(request, self.location)
        elif request.method == 'GET':
            req_data = request.args
        else:
            req_data = request.json
        value = self.default
        if req_data and self.name in req_data:
            value = req_data[self.name]
        elif self.required:
            raise ApiError(StatusDef.PARAM_ERROR, '`%s` is required! %s' % (self.name, self.help))
        elif self.discard:
            return (None, False)
        if value is None:
            if not self.nullable:
                raise ApiError(StatusDef.PARAM_ERROR, '`%s` is not nullable! %s' % (self.name, self.help))
        elif not self.ignore and (not isinstance(value, self.type)):
            raise ApiError(StatusDef.PARAM_ERROR, 'TypeError: `%s` is %s! %s' % (self.name, self.type, self.help))
        if isinstance(self.choices, abc.Iterable) and value not in self.choices:
            raise ApiError(StatusDef.PARAM_ERROR, 'ValueError: `%s` must in %s!%s' % (self.name, self.choices, self.help))
        return (value, True)
<|end_body_1|>

Solution:
class Param:
    def __init__(self, name, type=str, required=False, default=None, nullable=True, ignore=False, choices=None, location=None, discard=False, help=''):
        """请求参数对象 Args: name (str): 字段名 type (class): 类型 required (bool): 是否必填 default (any): 默认值 (必填时无效) nullable (bool): 是否非空 ignore (bool): 是否忽略类型 choices (tuple): 可选值 location (str): 访问数据源 args, json, form 默认 GET: args, POST: json discard (bool): 不存在 key, 是否不解析参数 help (str): 参数不匹配时, 返回提示"""
        self.name = name
        self.type = type
        self.required = required
        self.default = default
        self.ignore = ignore
        self.choices = choices
        self.nullable = nullable
        self.location = location
        self.discard = discard
        self.help = help

    def load(self):
        """解析并检查参数取值 Returns: value(值), valid(是否有效)"""
        if self.location and hasattr(request, self.location):
            req_data = getattr(request, self.location)
        elif request.method == 'GET':
            req_data = request.args
        else:
            req_data = request.json
        value = self.default
        if req_data and self.name in req_data:
            value = req_data[self.name]
        elif self.required:
            raise ApiError(StatusDef.PARAM_ERROR, '`%s` is required! %s' % (self.name, self.help))
        elif self.discard:
            return (None, False)
        if value is None:
            if not self.nullable:
                raise ApiError(StatusDef.PARAM_ERROR, '`%s` is not nullable! %s' % (self.name, self.help))
        elif not self.ignore and (not isinstance(value, self.type)):
            raise ApiError(StatusDef.PARAM_ERROR, 'TypeError: `%s` is %s! %s' % (self.name, self.type, self.help))
        if isinstance(self.choices, abc.Iterable) and value not in self.choices:
            raise ApiError(StatusDef.PARAM_ERROR, 'ValueError: `%s` must in %s!%s' % (self.name, self.choices, self.help))
        return (value, True)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def rand10(self): :rtype: int
- def rand10_1(self): :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def rand10(self): :rtype: int
- def rand10_1(self): :rtype: int

<|skeleton|>
class Solution:

    def rand10(self):
        """:rtype: int"""
        <|body_0|>

    def rand10_1(self):
        """:rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        while True:
            r = rand7()
            c = rand7()
            n = c + (r - 1) * 7
            if n <= 40:
                break
        return 1 + (n - 1) % 10
<|end_body_0|>

<|body_start_1|>
        while True:
            r = rand7()
            c = rand7()
            n = c + (r - 1) * 7
            if n <= 40:
                return 1 + (n - 1) % 10
            r = n - 40
            c = rand7()
            n = c + (r - 1) * 7
            if n <= 60:
                return 1 + (n - 1) % 10
            r = n - 60
            c = rand7()
            n = c + (r - 1) * 7
            if n <= 20:
                return 1 + (n - 1) % 10
<|end_body_1|>

Solution:
class Solution:
    def rand10(self):
        """:rtype: int"""
        while True:
            r = rand7()
            c = rand7()
            n = c + (r - 1) * 7
            if n <= 40:
                break
        return 1 + (n - 1) % 10

    def rand10_1(self):
        """:rtype: int"""
        while True:
            r = rand7()
            c = rand7()
            n = c + (r - 1) * 7
            if n <= 40:
                return 1 + (n - 1) % 10
            r = n - 40
            c = rand7()
            n = c + (r - 1) * 7
            if n <= 60:
                return 1 + (n - 1) % 10
            r = n - 60
            c = rand7()
            n = c + (r - 1) * 7
            if n <= 20:
                return 1 + (n - 1) % 10


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

Class description:
The Class for handling the data retrieval.

Method signatures and docstrings:
- def __init__(self, irish_rail, station, direction, destination, stops_at): Initialize the data object.
- def update(self): Get the latest data from irishrail.
- def _empty_train_data(self): Generate info for an empty train.

Implement the Python class `IrishRailTransportData` described below.

Class description:
The Class for handling the data retrieval.

Method signatures and docstrings:
- def __init__(self, irish_rail, station, direction, destination, stops_at): Initialize the data object.
- def update(self): Get the latest data from irishrail.
- def _empty_train_data(self): Generate info for an empty train.

<|skeleton|>
class IrishRailTransportData:
    """The Class for handling the data retrieval."""

    def __init__(self, irish_rail, station, direction, destination, stops_at):
        """Initialize the data object."""
        <|body_0|>

    def update(self):
        """Get the latest data from irishrail."""
        <|body_1|>

    def _empty_train_data(self):
        """Generate info for an empty train."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self._ir_api = irish_rail
        self.station = station
        self.direction = direction
        self.destination = destination
        self.stops_at = stops_at
        self.info = self._empty_train_data()
<|end_body_0|>

<|body_start_1|>
        trains = self._ir_api.get_station_by_name(self.station, direction=self.direction, destination=self.destination, stops_at=self.stops_at)
        stops_at = self.stops_at if self.stops_at else ''
        self.info = []
        for train in trains:
            train_data = {ATTR_STATION: self.station, ATTR_ORIGIN: train.get('origin'), ATTR_DESTINATION: train.get('destination'), ATTR_DUE_IN: train.get('due_in_mins'), ATTR_DUE_AT: train.get('scheduled_arrival_time'), ATTR_EXPECT_AT: train.get('expected_departure_time'), ATTR_DIRECTION: train.get('direction'), ATTR_STOPS_AT: stops_at, ATTR_TRAIN_TYPE: train.get('type')}
            self.info.append(train_data)
        if not self.info:
            self.info = self._empty_train_data()
<|end_body_1|>

<|body_start_2|>
        dest = self.destination if self.destination else ''
        direction = self.direction if self.direction else ''
        stops_at = self.stops_at if self.stops_at else ''
        return [{ATTR_STATION: self.station, ATTR_ORIGIN: '', ATTR_DESTINATION: dest, ATTR_DUE_IN: 'n/a', ATTR_DUE_AT: 'n/a', ATTR_EXPECT_AT: 'n/a', ATTR_DIRECTION: direction, ATTR_STOPS_AT: stops_at, ATTR_TRAIN_TYPE: ''}]
<|end_body_2|>

Solution:
class IrishRailTransportData:
    """The Class for handling the data retrieval."""

    def __init__(self, irish_rail, station, direction, destination, stops_at):
        """Initialize the data object."""
        self._ir_api = irish_rail
        self.station = station
        self.direction = direction
        self.destination = destination
        self.stops_at = stops_at
        self.info = self._empty_train_data()

    def update(self):
        """Get the latest data from irishrail."""
        trains = self._ir_api.get_station_by_name(self.station, direction=self.direction, destination=self.destination, stops_at=self.stops_at)
        stops_at = self.stops_at if self.stops_at else ''
        self.info = []
        for train in trains:
            train_data = {ATTR_STATION: self.station, ATTR_ORIGIN: train.get('origin'), ATTR_DESTINATION: train.get('destination'), ATTR_DUE_IN: train.get('due_in_mins'), ATTR_DUE_AT: train.get('scheduled_arrival_time'), ATTR_EXPECT_AT: train.get('expected_departure_time'), ATTR_DIRECTION: train.get('direction'), ATTR_STOPS_AT: stops_at, ATTR_TRAIN_TYPE: train.get('type')}
            self.info.append(train_data)
        if not self.info:
            self.info = self._empty_train_data()

    def _empty_train_data(self):
        """Generate info for an empty train."""
        dest = self.destination if self.destination else ''
        direction = self.direction if self.direction else ''
        stops_at = self.stops_at if self.stops_at else ''
        return [{ATTR_STATION: self.station, ATTR_ORIGIN: '', ATTR_DESTINATION: dest, ATTR_DUE_IN: 'n/a', ATTR_DUE_AT: 'n/a', ATTR_EXPECT_AT: 'n/a', ATTR_DIRECTION: direction, ATTR_STOPS_AT: stops_at, ATTR_TRAIN_TYPE: ''}]


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

Class description:
establish MongoDB connection (per assignment's example)

Method signatures and docstrings:
- def __init__(self, host='127.0.0.1', port=27017): use public ip-address and port
- def __enter__(self): enter
- def __exit__(self, exc_type, exc_val, exc_tb): exit

Implement the Python class `MongoDBConnection` described below.

Class description:
establish MongoDB connection (per assignment's example)

Method signatures and docstrings:
- def __init__(self, host='127.0.0.1', port=27017): use public ip-address and port
- def __enter__(self): enter
- def __exit__(self, exc_type, exc_val, exc_tb): exit

<|skeleton|>
class MongoDBConnection:
    """establish MongoDB connection (per assignment's example)"""

    def __init__(self, host='127.0.0.1', port=27017):
        """use public ip-address and port"""
        <|body_0|>

    def __enter__(self):
        """enter"""
        <|body_1|>

    def __exit__(self, exc_type, exc_val, exc_tb):
        """exit"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.host = host
        self.port = port
        self.connection = None
        self.database = None
        self.product = None
        self.customer = None
        self.rental = None
<|end_body_0|>

<|body_start_1|>
        if self.connection is None:
            try:
                self.connection = MongoClient(self.host, self.port)
                LOGGER.info('connected to mongo')
                LOGGER.info('entering host %s via port %s ', self.host, self.port)
                self.database = self.connection.FlorentinDB
                self.product = self.database['product']
                self.customer = self.database['customer']
                self.rental = self.database['rental']
                LOGGER.info('database %s established on mongo', self.database)
                LOGGER.info('collections %s, %s, %s available in %s', self.product, self.customer, self.rental, self.database)
            except mer.ConnectionFailure as err:
                LOGGER.info('error connecting to mongo\n %s', err)
        return self
<|end_body_1|>

<|body_start_2|>
        if exc_type:
            pp.pprint(f'exc_type: {exc_type}')
            pp.pprint(f'exc_val: {exc_val}')
            pp.pprint(f'exc_tb: {exc_tb}')
        self.connection.close()
        LOGGER.info('disconected from mongo')
<|end_body_2|>

Solution:
class MongoDBConnection:
    """establish MongoDB connection (per assignment's example)"""

    def __init__(self, host='127.0.0.1', port=27017):
        """use public ip-address and port"""
        self.host = host
        self.port = port
        self.connection = None
        self.database = None
        self.product = None
        self.customer = None
        self.rental = None

    def __enter__(self):
        """enter"""
        if self.connection is None:
            try:
                self.connection = MongoClient(self.host, self.port)
                LOGGER.info('connected to mongo')
                LOGGER.info('entering host %s via port %s ', self.host, self.port)
                self.database = self.connection.FlorentinDB
                self.product = self.database['product']
                self.customer = self.database['customer']
                self.rental = self.database['rental']
                LOGGER.info('database %s established on mongo', self.database)
                LOGGER.info('collections %s, %s, %s available in %s', self.product, self.customer, self.rental, self.database)
            except mer.ConnectionFailure as err:
                LOGGER.info('error connecting to mongo\n %s', err)
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        """exit"""
        if exc_type:
            pp.pprint(f'exc_type: {exc_type}')
            pp.pprint(f'exc_val: {exc_val}')
            pp.pprint(f'exc_tb: {exc_tb}')
        self.connection.close()
        LOGGER.info('disconected from mongo')


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

Class description:
Implement the Swish class.

Method signatures and docstrings:
- def __init__(self, eswish=False, swish=False, beta=1.735, flatten=False, pfts=False): Init method.
- def forward(self, input): Forward pass of the function.

Implement the Python class `Swish` described below.

Class description:
Implement the Swish class.

Method signatures and docstrings:
- def __init__(self, eswish=False, swish=False, beta=1.735, flatten=False, pfts=False): Init method.
- def forward(self, input): Forward pass of the function.

<|skeleton|>
class Swish:

    def __init__(self, eswish=False, swish=False, beta=1.735, flatten=False, pfts=False):
        """Init method."""
        <|body_0|>

    def forward(self, input):
        """Forward pass of the function."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(Swish, self).__init__()
        self.swish = swish
        self.eswish = eswish
        self.flatten = flatten
        self.beta = None
        self.param = None
        if eswish is not False:
            self.beta = beta
        if swish is not False:
            self.param = nn.Parameter(torch.randn(1))
            self.param.requires_grad = True
        if flatten is not False:
            if pfts is not False:
                self.const = nn.Parameter(torch.tensor(-0.2))
                self.const.requires_grad = True
            else:
                self.const = -0.2
        if eswish is not False and swish is not False and (flatten is not False):
            raise RuntimeError('Advisable to run either Swish or E-Swish or Flatten T-Swish')
<|end_body_0|>

<|body_start_1|>
        if self.swish is not False:
            return swish_function(input, self.swish, self.eswish, self.beta, self.param)
        if self.eswish is not False:
            return swish_function(input, self.swish, self.eswish, self.beta, self.param)
        if self.flatten is not False:
            return (input >= 0).float() * (input * swish_function(input, self.swish, self.eswish, self.beta, self.param) + self.const) + (input < 0).float() * self.const
<|end_body_1|>

Solution:
class Swish:
    def __init__(self, eswish=False, swish=False, beta=1.735, flatten=False, pfts=False):
        """Init method."""
        super(Swish, self).__init__()
        self.swish = swish
        self.eswish = eswish
        self.flatten = flatten
        self.beta = None
        self.param = None
        if eswish is not False:
            self.beta = beta
        if swish is not False:
            self.param = nn.Parameter(torch.randn(1))
            self.param.requires_grad = True
        if flatten is not False:
            if pfts is not False:
                self.const = nn.Parameter(torch.tensor(-0.2))
                self.const.requires_grad = True
            else:
                self.const = -0.2
        if eswish is not False and swish is not False and (flatten is not False):
            raise RuntimeError('Advisable to run either Swish or E-Swish or Flatten T-Swish')

    def forward(self, input):
        """Forward pass of the function."""
        if self.swish is not False:
            return swish_function(input, self.swish, self.eswish, self.beta, self.param)
        if self.eswish is not False:
            return swish_function(input, self.swish, self.eswish, self.beta, self.param)
        if self.flatten is not False:
            return (input >= 0).float() * (input * swish_function(input, self.swish, self.eswish, self.beta, self.param) + self.const) + (input < 0).float() * self.const


toFieldValue

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

Class description:
A data converter using the field's ``fromUnicode()`` method.

Method signatures and docstrings:
- def toWidgetValue(self, value): Converts the internal stored value into something that a z3c.form widget understands :param value: [required] The internally stored value :type value: Dict :returns: A string with UUIDs separated by
- def toFieldValue(self, value): Passes the value extracted from the widget to the internal structure. In this case, the value expected is already formatted :param value: [required] The data extracted from the widget :type value: Dict :returns: The value to be stored in the tile

Implement the Python class `UUIDSFieldDataConverter` described below.

Class description:
A data converter using the field's ``fromUnicode()`` method.

Method signatures and docstrings:
- def toWidgetValue(self, value): Converts the internal stored value into something that a z3c.form widget understands :param value: [required] The internally stored value :type value: Dict :returns: A string with UUIDs separated by
- def toFieldValue(self, value): Passes the value extracted from the widget to the internal structure. In this case, the value expected is already formatted :param value: [required] The data extracted from the widget :type value: Dict :returns: The value to be stored in the tile

<|skeleton|>
class UUIDSFieldDataConverter:
    """A data converter using the field's ``fromUnicode()`` method."""

    def toWidgetValue(self, value):
        """Converts the internal stored value into something that a z3c.form widget understands :param value: [required] The internally stored value :type value: Dict :returns: A string with UUIDs separated by"""
        <|body_0|>

    def toFieldValue(self, value):
        """Passes the value extracted from the widget to the internal structure. In this case, the value expected is already formatted :param value: [required] The data extracted from the widget :type value: Dict :returns: The value to be stored in the tile"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        ordered_uuids = [(k, v) for k, v in value.items()]
        ordered_uuids.sort(key=lambda x: x[1]['order'])
        return '\r\n'.join([i[0] for i in ordered_uuids])
<|end_body_0|>

<|body_start_1|>
        if not len(value) or not isinstance(value, dict):
            return self.field.missing_value
        return value
<|end_body_1|>

Solution:
class UUIDSFieldDataConverter:
    """A data converter using the field's ``fromUnicode()`` method."""

    def toWidgetValue(self, value):
        """Converts the internal stored value into something that a z3c.form widget understands :param value: [required] The internally stored value :type value: Dict :returns: A string with UUIDs separated by"""
        ordered_uuids = [(k, v) for k, v in value.items()]
        ordered_uuids.sort(key=lambda x: x[1]['order'])
        return '\r\n'.join([i[0] for i in ordered_uuids])

    def toFieldValue(self, value):
        """Passes the value extracted from the widget to the internal structure. In this case, the value expected is already formatted :param value: [required] The data extracted from the widget :type value: Dict :returns: The value to be stored in the tile"""
        if not len(value) or not isinstance(value, dict):
            return self.field.missing_value
        return value


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

Class description:
Gh representation

Method signatures and docstrings:
- def __init__(self, test_inputs=None): Default constructor
- def calculate(self): Main calcualtion function of the class

Implement the Python class `Gh` described below.

Class description:
Gh representation

Method signatures and docstrings:
- def __init__(self, test_inputs=None): Default constructor
- def calculate(self): Main calcualtion function of the class

<|skeleton|>
class Gh:
    """Gh representation"""

    def __init__(self, test_inputs=None):
        """Default constructor"""
        <|body_0|>

    def calculate(self):
        """Main calcualtion function of the class"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        it = iter(test_inputs.split('\n')) if test_inputs else None

        def uinput():
            return next(it) if it else sys.stdin.readline().rstrip()
        [self.n] = map(int, uinput().split())
        self.numa = [s == '>' for s in uinput()]
        self.numb = list(map(int, uinput().split()))
<|end_body_0|>

<|body_start_1|>
        result = 'FINITE'
        pos = 0
        vis = set([])
        while 0 <= pos < self.n:
            vis.add(pos)
            if self.numa[pos]:
                pos += self.numb[pos]
            else:
                pos -= self.numb[pos]
            if pos in vis:
                result = 'IN' + result
                break
        return str(result)
<|end_body_1|>

Solution:
class Gh:
    """Gh representation"""

    def __init__(self, test_inputs=None):
        """Default constructor"""
        it = iter(test_inputs.split('\n')) if test_inputs else None

        def uinput():
            return next(it) if it else sys.stdin.readline().rstrip()
        [self.n] = map(int, uinput().split())
        self.numa = [s == '>' for s in uinput()]
        self.numb = list(map(int, uinput().split()))

    def calculate(self):
        """Main calcualtion function of the class"""
        result = 'FINITE'
        pos = 0
        vis = set([])
        while 0 <= pos < self.n:
            vis.add(pos)
            if self.numa[pos]:
                pos += self.numb[pos]
            else:
                pos -= self.numb[pos]
            if pos in vis:
                result = 'IN' + result
                break
        return str(result)


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

Class description:
Defines types of action a user can perform on a team and or project

Method signatures and docstrings:
- def from_str(role_str): Convert a string into a Role object Args: role_str: (str) String representation of a Role Returns: (Role)
- def to_str(role): Convert a Role object into a string. Args: role: (Role) Returns: (str)

Implement the Python class `Role` described below.

Class description:
Defines types of action a user can perform on a team and or project

Method signatures and docstrings:
- def from_str(role_str): Convert a string into a Role object Args: role_str: (str) String representation of a Role Returns: (Role)
- def to_str(role): Convert a Role object into a string. Args: role: (Role) Returns: (str)

<|skeleton|>
class Role:
    """Defines types of action a user can perform on a team and or project"""

    def from_str(role_str):
        """Convert a string into a Role object Args: role_str: (str) String representation of a Role Returns: (Role)"""
        <|body_0|>

    def to_str(role):
        """Convert a Role object into a string. Args: role: (Role) Returns: (str)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if role_str == 'edit':
            return Role.edit
        if role_str == 'install':
            return Role.install
        if role_str == 'view':
            return Role.view
        return Role.denied
<|end_body_0|>

<|body_start_1|>
        if role == Role.edit:
            return 'edit'
        if role == Role.install:
            return 'install'
        if role == Role.view:
            return 'view'
        return 'denied'
<|end_body_1|>

Solution:
class Role:
    """Defines types of action a user can perform on a team and or project"""

    def from_str(role_str):
        """Convert a string into a Role object Args: role_str: (str) String representation of a Role Returns: (Role)"""
        if role_str == 'edit':
            return Role.edit
        if role_str == 'install':
            return Role.install
        if role_str == 'view':
            return Role.view
        return Role.denied

    def to_str(role):
        """Convert a Role object into a string. Args: role: (Role) Returns: (str)"""
        if role == Role.edit:
            return 'edit'
        if role == Role.install:
            return 'install'
        if role == Role.view:
            return 'view'
        return 'denied'


run

Prompt:
Implement the Python class `WriteHDF5Thread` described below.

Class description:
Copies small hdf5 feature tables into one large hdf5 feature table

Method signatures and docstrings:
- def __init__(self, h5_filename_queue): param h5_filename_queue: a queue of temporary hdf5 files
- def run(self): While queue is not empty and stop_write_thread has not been set to true, the thread will open temporary hdf5 tables and copy them into the main hdf5 table and then delete the temporary file.

Implement the Python class `WriteHDF5Thread` described below.

Class description:
Copies small hdf5 feature tables into one large hdf5 feature table

Method signatures and docstrings:
- def __init__(self, h5_filename_queue): param h5_filename_queue: a queue of temporary hdf5 files
- def run(self): While queue is not empty and stop_write_thread has not been set to true, the thread will open temporary hdf5 tables and copy them into the main hdf5 table and then delete the temporary file.

<|skeleton|>
class WriteHDF5Thread:
    """Copies small hdf5 feature tables into one large hdf5 feature table"""

    def __init__(self, h5_filename_queue):
        """param h5_filename_queue: a queue of temporary hdf5 files"""
        <|body_0|>

    def run(self):
        """While queue is not empty and stop_write_thread has not been set to true, the thread will open temporary hdf5 tables and copy them into the main hdf5 table and then delete the temporary file."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.h5_filename_queue = h5_filename_queue
        tables.open_file(table_path, 'w').close()
        super(WriteHDF5Thread, self).__init__()
<|end_body_0|>

<|body_start_1|>
        while True:
            if not self.h5_filename_queue.empty():
                temp_path = self.h5_filename_queue.get()
                log.debug('Writing %s to %s' % (temp_path, table_path))
                try:
                    with tables.open_file(temp_path, 'a') as hdf5temp:
                        with tables.open_file(table_path, 'a') as hdf5:
                            temp_table = hdf5temp.root.values
                            temp_status_table = hdf5temp.root.status
                            if not hasattr(hdf5.root, 'values'):
                                temp_table.copy(hdf5.root, 'values')
                                temp_status_table.copy(hdf5.root, 'status')
                            else:
                                table = hdf5.root.values
                                status_table = hdf5.root.status
                                table.append(temp_table[:])
                                status_table.append(temp_status_table[:])
                                table.flush()
                                status_table.flush()
                except StandardError as e:
                    log.exception('Could not read hdf5 file')
                finally:
                    log.debug('Clean up: removing %s' % temp_path)
                    if os.path.exists(temp_path):
                        os.remove(temp_path)
            if stop_write_thread is True:
                log.debug('Ending HDF5 write thread')
                break
<|end_body_1|>

Solution:
class WriteHDF5Thread:
    """Copies small hdf5 feature tables into one large hdf5 feature table"""

    def __init__(self, h5_filename_queue):
        """param h5_filename_queue: a queue of temporary hdf5 files"""
        self.h5_filename_queue = h5_filename_queue
        tables.open_file(table_path, 'w').close()
        super(WriteHDF5Thread, self).__init__()

    def run(self):
        """While queue is not empty and stop_write_thread has not been set to true, the thread will open temporary hdf5 tables and copy them into the main hdf5 table and then delete the temporary file."""
        while True:
            if not self.h5_filename_queue.empty():
                temp_path = self.h5_filename_queue.get()
                log.debug('Writing %s to %s' % (temp_path, table_path))
                try:
                    with tables.open_file(temp_path, 'a') as hdf5temp:
                        with tables.open_file(table_path, 'a') as hdf5:
                            temp_table = hdf5temp.root.values
                            temp_status_table = hdf5temp.root.status
                            if not hasattr(hdf5.root, 'values'):
                                temp_table.copy(hdf5.root, 'values')
                                temp_status_table.copy(hdf5.root, 'status')
                            else:
                                table = hdf5.root.values
                                status_table = hdf5.root.status
                                table.append(temp_table[:])
                                status_table.append(temp_status_table[:])
                                table.flush()
                                status_table.flush()
                except StandardError as e:
                    log.exception('Could not read hdf5 file')
                finally:
                    log.debug('Clean up: removing %s' % temp_path)
                    if os.path.exists(temp_path):
                        os.remove(temp_path)
            if stop_write_thread is True:
                log.debug('Ending HDF5 write thread')
                break


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

Class description:
Self attention model using a RNN cell.

Method signatures and docstrings:
- def __init__(self, options, is_training=False): Initializes the graph network. Args: options: proto to store the configs. is_training: if True, build the training graph.
- def _build_graph(self, graphs_tuple, regularizer): Builds graph network. Args: graphs_tuple: A GraphTuple instance. regularizer: Regularizer to be used in linear layers. Returns: output_graphs_tuple: A updated GraphTuple instance.

Implement the Python class `MessagePassing` described below.

Class description:
Self attention model using a RNN cell.

Method signatures and docstrings:
- def __init__(self, options, is_training=False): Initializes the graph network. Args: options: proto to store the configs. is_training: if True, build the training graph.
- def _build_graph(self, graphs_tuple, regularizer): Builds graph network. Args: graphs_tuple: A GraphTuple instance. regularizer: Regularizer to be used in linear layers. Returns: output_graphs_tuple: A updated GraphTuple instance.

<|skeleton|>
class MessagePassing:
    """Self attention model using a RNN cell."""

    def __init__(self, options, is_training=False):
        """Initializes the graph network. Args: options: proto to store the configs. is_training: if True, build the training graph."""
        <|body_0|>

    def _build_graph(self, graphs_tuple, regularizer):
        """Builds graph network. Args: graphs_tuple: A GraphTuple instance. regularizer: Regularizer to be used in linear layers. Returns: output_graphs_tuple: A updated GraphTuple instance."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(MessagePassing, self).__init__(options, is_training)
        if not isinstance(options, graph_network_pb2.MessagePassing):
            raise ValueError('Options has to be an MessagePassing proto.')
        self.use_reverse_edges = options.use_reverse_edges
        self.add_bi_directional_edges = options.add_bi_directional_edges
        self.add_self_loop_edges = True
<|end_body_0|>

<|body_start_1|>
        original_nodes = graphs_tuple.nodes
        original_edges = graphs_tuple.edges
        network = GNetMPNN()
        for _ in range(self.options.n_layer):
            graphs_tuple = network(graphs_tuple, hidden_size=self.options.hidden_size, regularizer=regularizer, attn_scale=self.options.attn_scale, attn_dropout_keep_prob=self.options.attn_dropout_keep_prob, is_training=self.is_training)
        if self.options.n_layer:
            graphs_tuple = graphs_tuple.replace(nodes=original_nodes + graphs_tuple.nodes, edges=original_edges + graphs_tuple.edges)
        return graphs_tuple
<|end_body_1|>

Solution:
class MessagePassing:
    """Self attention model using a RNN cell."""

    def __init__(self, options, is_training=False):
        """Initializes the graph network. Args: options: proto to store the configs. is_training: if True, build the training graph."""
        super(MessagePassing, self).__init__(options, is_training)
        if not isinstance(options, graph_network_pb2.MessagePassing):
            raise ValueError('Options has to be an MessagePassing proto.')
        self.use_reverse_edges = options.use_reverse_edges
        self.add_bi_directional_edges = options.add_bi_directional_edges
        self.add_self_loop_edges = True

    def _build_graph(self, graphs_tuple, regularizer):
        """Builds graph network. Args: graphs_tuple: A GraphTuple instance. regularizer: Regularizer to be used in linear layers. Returns: output_graphs_tuple: A updated GraphTuple instance."""
        original_nodes = graphs_tuple.nodes
        original_edges = graphs_tuple.edges
        network = GNetMPNN()
        for _ in range(self.options.n_layer):
            graphs_tuple = network(graphs_tuple, hidden_size=self.options.hidden_size, regularizer=regularizer, attn_scale=self.options.attn_scale, attn_dropout_keep_prob=self.options.attn_dropout_keep_prob, is_training=self.is_training)
        if self.options.n_layer:
            graphs_tuple = graphs_tuple.replace(nodes=original_nodes + graphs_tuple.nodes, edges=original_edges + graphs_tuple.edges)
        return graphs_tuple


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

Class description:
Implement the BlogInfo class.

Method signatures and docstrings:
- def get(self, request, user_id): 获取博客
- def patch(self, request, user_id): 删除博客

Implement the Python class `BlogInfo` described below.

Class description:
Implement the BlogInfo class.

Method signatures and docstrings:
- def get(self, request, user_id): 获取博客
- def patch(self, request, user_id): 删除博客

<|skeleton|>
class BlogInfo:

    def get(self, request, user_id):
        """获取博客"""
        <|body_0|>

    def patch(self, request, user_id):
        """删除博客"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        blog_ids = Blog.objects.filter(user=request.my_user).values_list('id', flat=True)
        blog_list = update.get_blog_info(list(blog_ids))
        return success(blog_list)
<|end_body_0|>

<|body_start_1|>
        blog_id = json.loads(request.body).get('blog_id')
        if update.update_status(blog_id, '3'):
            return success()
        return error(404, '删除失败')
<|end_body_1|>

Solution:
class BlogInfo:
    def get(self, request, user_id):
        """获取博客"""
        blog_ids = Blog.objects.filter(user=request.my_user).values_list('id', flat=True)
        blog_list = update.get_blog_info(list(blog_ids))
        return success(blog_list)

    def patch(self, request, user_id):
        """删除博客"""
        blog_id = json.loads(request.body).get('blog_id')
        if update.update_status(blog_id, '3'):
            return success()
        return error(404, '删除失败')