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

Class description:
This class adds a flag to the rosemount temperatures if the two temperatures disagree by more than a given absolute value. This is given by the module constant TEMPERATURE_THRESHOLD.

Method signatures and docstrings:
- def _get_flag(self): Get the flag value for the new flag
- def _flag(self, test=False): Entry point for the flagging module.

Implement the Python class `RosemountTempDeltaFlag` described below.

Class description:
This class adds a flag to the rosemount temperatures if the two temperatures disagree by more than a given absolute value. This is given by the module constant TEMPERATURE_THRESHOLD.

Method signatures and docstrings:
- def _get_flag(self): Get the flag value for the new flag
- def _flag(self, test=False): Entry point for the flagging module.

<|skeleton|>
class RosemountTempDeltaFlag:
    """This class adds a flag to the rosemount temperatures if the two temperatures disagree by more than a given absolute value. This is given by the module constant TEMPERATURE_THRESHOLD."""

    def _get_flag(self):
        """Get the flag value for the new flag"""
        <|body_0|>

    def _flag(self, test=False):
        """Entry point for the flagging module."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        _diff = np.abs(self.dataset['TAT_DI_R'].array - self.dataset['TAT_ND_R'].array)
        mask = pd.Series(np.zeros_like(self.dataset['TAT_DI_R'].array), index=self.dataset['TAT_DI_R'].index)
        mask.loc[_diff > TEMPERATURE_THRESHOLD] = MASKED
        return mask
<|end_body_0|>

<|body_start_1|>
        if test:
            flag = self.test_flag
        else:
            flag = self._get_flag()
        for var in TEMPERATURE_VARIABLES:
            self.add_mask(var, flag, 'discrepancy threshold exceeded', f'The discrepancy between the deiced and non-deiced temperature sensors is greater than {TEMPERATURE_THRESHOLD} K.')
<|end_body_1|>

Solution:
class RosemountTempDeltaFlag:
    """This class adds a flag to the rosemount temperatures if the two temperatures disagree by more than a given absolute value. This is given by the module constant TEMPERATURE_THRESHOLD."""

    def _get_flag(self):
        """Get the flag value for the new flag"""
        _diff = np.abs(self.dataset['TAT_DI_R'].array - self.dataset['TAT_ND_R'].array)
        mask = pd.Series(np.zeros_like(self.dataset['TAT_DI_R'].array), index=self.dataset['TAT_DI_R'].index)
        mask.loc[_diff > TEMPERATURE_THRESHOLD] = MASKED
        return mask

    def _flag(self, test=False):
        """Entry point for the flagging module."""
        if test:
            flag = self.test_flag
        else:
            flag = self._get_flag()
        for var in TEMPERATURE_VARIABLES:
            self.add_mask(var, flag, 'discrepancy threshold exceeded', f'The discrepancy between the deiced and non-deiced temperature sensors is greater than {TEMPERATURE_THRESHOLD} K.')


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

Class description:
Implement the CheckPalindrome class.

Method signatures and docstrings:
- def check_palindrome_using_stack(self): Function to check whether elements in linked list form a palindrome of not. It traverses the linked list twice, once to push all elements in stack and other to verify elements in the stack :return: Bool
- def compare_list(first, second): Function to compare elements of the two list :param first: Node :param second: Node :return: Bool
- def check_palindrome_using_reverse(self): Function to check whether elements in the liked list form a palindrome or not. This function gets to the middle of linked list and reverses the second half to check it it matches with the first half. It reverses the second half again to form the second half :return: Bool

Implement the Python class `CheckPalindrome` described below.

Class description:
Implement the CheckPalindrome class.

Method signatures and docstrings:
- def check_palindrome_using_stack(self): Function to check whether elements in linked list form a palindrome of not. It traverses the linked list twice, once to push all elements in stack and other to verify elements in the stack :return: Bool
- def compare_list(first, second): Function to compare elements of the two list :param first: Node :param second: Node :return: Bool
- def check_palindrome_using_reverse(self): Function to check whether elements in the liked list form a palindrome or not. This function gets to the middle of linked list and reverses the second half to check it it matches with the first half. It reverses the second half again to form the second half :return: Bool

<|skeleton|>
class CheckPalindrome:

    def check_palindrome_using_stack(self):
        """Function to check whether elements in linked list form a palindrome of not. It traverses the linked list twice, once to push all elements in stack and other to verify elements in the stack :return: Bool"""
        <|body_0|>

    def compare_list(first, second):
        """Function to compare elements of the two list :param first: Node :param second: Node :return: Bool"""
        <|body_1|>

    def check_palindrome_using_reverse(self):
        """Function to check whether elements in the liked list form a palindrome or not. This function gets to the middle of linked list and reverses the second half to check it it matches with the first half. It reverses the second half again to form the second half :return: Bool"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        stack = []
        temp = self.head
        while temp:
            stack.append(temp.data)
            temp = temp.next
        temp = self.head
        while temp:
            if temp.data != stack[0]:
                return False
            stack.pop(0)
            temp = temp.next
        return True
<|end_body_0|>

<|body_start_1|>
        temp1 = first
        temp2 = second
        while temp1 and temp2:
            if temp1.data == temp2.data:
                temp1 = temp1.next
                temp2 = temp2.next
            else:
                return False
        if not temp1 and (not temp2):
            return True
        return False
<|end_body_1|>

<|body_start_2|>
        slow = self.head
        fast = self.head
        midnode = None
        prev_to_slow = None
        while fast and fast.next:
            prev_to_slow = slow
            slow = slow.next
            fast = fast.next.next
        if fast:
            midnode = slow
            slow = slow.next
        prev_to_slow.next = None
        second_half = slow
        second_half = LinkedListReverse.iterative_reverse(second_half)
        res = CheckPalindrome.compare_list(self.head, second_half)
        second_half = LinkedListReverse.iterative_reverse(second_half)
        if midnode:
            prev_to_slow.next = midnode
            midnode.next = second_half
        else:
            prev_to_slow.next = second_half
        return res
<|end_body_2|>

Solution:
class CheckPalindrome:
    def check_palindrome_using_stack(self):
        """Function to check whether elements in linked list form a palindrome of not. It traverses the linked list twice, once to push all elements in stack and other to verify elements in the stack :return: Bool"""
        stack = []
        temp = self.head
        while temp:
            stack.append(temp.data)
            temp = temp.next
        temp = self.head
        while temp:
            if temp.data != stack[0]:
                return False
            stack.pop(0)
            temp = temp.next
        return True

    def compare_list(first, second):
        """Function to compare elements of the two list :param first: Node :param second: Node :return: Bool"""
        temp1 = first
        temp2 = second
        while temp1 and temp2:
            if temp1.data == temp2.data:
                temp1 = temp1.next
                temp2 = temp2.next
            else:
                return False
        if not temp1 and (not temp2):
            return True
        return False

    def check_palindrome_using_reverse(self):
        """Function to check whether elements in the liked list form a palindrome or not. This function gets to the middle of linked list and reverses the second half to check it it matches with the first half. It reverses the second half again to form the second half :return: Bool"""
        slow = self.head
        fast = self.head
        midnode = None
        prev_to_slow = None
        while fast and fast.next:
            prev_to_slow = slow
            slow = slow.next
            fast = fast.next.next
        if fast:
            midnode = slow
            slow = slow.next
        prev_to_slow.next = None
        second_half = slow
        second_half = LinkedListReverse.iterative_reverse(second_half)
        res = CheckPalindrome.compare_list(self.head, second_half)
        second_half = LinkedListReverse.iterative_reverse(second_half)
        if midnode:
            prev_to_slow.next = midnode
            midnode.next = second_half
        else:
            prev_to_slow.next = second_half
        return res


DrawHandleLines

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

Class description:
Utility class for the noise falloff

Method signatures and docstrings:
- def PointInBox(p, data): Returns if a point is in box. Args: p (c4d.Vector): The point position. data (c4d.BaseContainer): Falloff data information. Returns: True if the point is in box, otherwise False
- def DrawHandleLines(bd, size, i): Helper method to draw a handle. Args: bd (c4d.BaseDraw): The editor's view. size (c4d.Vector): vector size of the object i (int): The Handle Id

Implement the Python class `NoiseFalloffHelper` described below.

Class description:
Utility class for the noise falloff

Method signatures and docstrings:
- def PointInBox(p, data): Returns if a point is in box. Args: p (c4d.Vector): The point position. data (c4d.BaseContainer): Falloff data information. Returns: True if the point is in box, otherwise False
- def DrawHandleLines(bd, size, i): Helper method to draw a handle. Args: bd (c4d.BaseDraw): The editor's view. size (c4d.Vector): vector size of the object i (int): The Handle Id

<|skeleton|>
class NoiseFalloffHelper:
    """Utility class for the noise falloff"""

    def PointInBox(p, data):
        """Returns if a point is in box. Args: p (c4d.Vector): The point position. data (c4d.BaseContainer): Falloff data information. Returns: True if the point is in box, otherwise False"""
        <|body_0|>

    def DrawHandleLines(bd, size, i):
        """Helper method to draw a handle. Args: bd (c4d.BaseDraw): The editor's view. size (c4d.Vector): vector size of the object i (int): The Handle Id"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        res = True
        point = [p.x, p.y, p.z]
        size = data.size * data.nodemat * data.scale
        size -= data.nodemat.off
        size = [size.x, size.y, size.z]
        offset = [data.offset.x, data.offset.y, data.offset.z]
        pos = c4d.Vector() * data.nodemat
        pos = [pos.x, pos.y, pos.z]
        for i in range(3):
            res = pos[i] + offset[i] + size[i] > point[i] > pos[i] + offset[i] - size[i]
            if not res:
                break
        return res
<|end_body_0|>

<|body_start_1|>
        bd.SetPen(c4d.GetViewColor(c4d.VIEWCOLOR_ACTIVEPOINT))
        p1, p2, p3, p4 = (c4d.Vector(), c4d.Vector(), c4d.Vector(), c4d.Vector())
        if i == 0:
            p1 = c4d.Vector(0, -size.y, -size.z)
            p2 = c4d.Vector(0, -size.y, size.z)
            p3 = c4d.Vector(0, size.y, size.z)
            p4 = c4d.Vector(0, size.y, -size.z)
        elif i == 1:
            p1 = c4d.Vector(-size.x, 0, -size.z)
            p2 = c4d.Vector(-size.x, 0, size.z)
            p3 = c4d.Vector(size.x, 0, size.z)
            p4 = c4d.Vector(size.x, 0, -size.z)
        elif i == 2:
            p1 = c4d.Vector(-size.x, -size.y, 0)
            p2 = c4d.Vector(-size.x, size.y, 0)
            p3 = c4d.Vector(size.x, size.y, 0)
            p4 = c4d.Vector(size.x, -size.y, 0)
        bd.DrawLine(p1, p2, 0)
        bd.DrawLine(p2, p3, 0)
        bd.DrawLine(p3, p4, 0)
        bd.DrawLine(p4, p1, 0)
<|end_body_1|>

Solution:
class NoiseFalloffHelper:
    """Utility class for the noise falloff"""

    def PointInBox(p, data):
        """Returns if a point is in box. Args: p (c4d.Vector): The point position. data (c4d.BaseContainer): Falloff data information. Returns: True if the point is in box, otherwise False"""
        res = True
        point = [p.x, p.y, p.z]
        size = data.size * data.nodemat * data.scale
        size -= data.nodemat.off
        size = [size.x, size.y, size.z]
        offset = [data.offset.x, data.offset.y, data.offset.z]
        pos = c4d.Vector() * data.nodemat
        pos = [pos.x, pos.y, pos.z]
        for i in range(3):
            res = pos[i] + offset[i] + size[i] > point[i] > pos[i] + offset[i] - size[i]
            if not res:
                break
        return res

    def DrawHandleLines(bd, size, i):
        """Helper method to draw a handle. Args: bd (c4d.BaseDraw): The editor's view. size (c4d.Vector): vector size of the object i (int): The Handle Id"""
        bd.SetPen(c4d.GetViewColor(c4d.VIEWCOLOR_ACTIVEPOINT))
        p1, p2, p3, p4 = (c4d.Vector(), c4d.Vector(), c4d.Vector(), c4d.Vector())
        if i == 0:
            p1 = c4d.Vector(0, -size.y, -size.z)
            p2 = c4d.Vector(0, -size.y, size.z)
            p3 = c4d.Vector(0, size.y, size.z)
            p4 = c4d.Vector(0, size.y, -size.z)
        elif i == 1:
            p1 = c4d.Vector(-size.x, 0, -size.z)
            p2 = c4d.Vector(-size.x, 0, size.z)
            p3 = c4d.Vector(size.x, 0, size.z)
            p4 = c4d.Vector(size.x, 0, -size.z)
        elif i == 2:
            p1 = c4d.Vector(-size.x, -size.y, 0)
            p2 = c4d.Vector(-size.x, size.y, 0)
            p3 = c4d.Vector(size.x, size.y, 0)
            p4 = c4d.Vector(size.x, -size.y, 0)
        bd.DrawLine(p1, p2, 0)
        bd.DrawLine(p2, p3, 0)
        bd.DrawLine(p3, p4, 0)
        bd.DrawLine(p4, p1, 0)


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

Class description:
Implement the FinitePage class.

Method signatures and docstrings:
- def next_link(self): URL for the next page of results (or None).
- def previous_link(self): URL for the previous page of results (or None).
- def start_index(self): Returns the 1-based index of the first object on this page, relative to total objects in the paginator.

Implement the Python class `FinitePage` described below.

Class description:
Implement the FinitePage class.

Method signatures and docstrings:
- def next_link(self): URL for the next page of results (or None).
- def previous_link(self): URL for the previous page of results (or None).
- def start_index(self): Returns the 1-based index of the first object on this page, relative to total objects in the paginator.

<|skeleton|>
class FinitePage:

    def next_link(self):
        """URL for the next page of results (or None)."""
        <|body_0|>

    def previous_link(self):
        """URL for the previous page of results (or None)."""
        <|body_1|>

    def start_index(self):
        """Returns the 1-based index of the first object on this page, relative to total objects in the paginator."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if self.has_next():
            return self.paginator.link_template % (self.number + 1)
        return None
<|end_body_0|>

<|body_start_1|>
        if self.has_previous():
            return self.paginator.link_template % (self.number - 1)
        return None
<|end_body_1|>

<|body_start_2|>
        offset = self.paginator.offset
        if offset is None:
            raise ValueError("Can't determine start index of paginator with no offset")
        return offset
<|end_body_2|>

Solution:
class FinitePage:
    def next_link(self):
        """URL for the next page of results (or None)."""
        if self.has_next():
            return self.paginator.link_template % (self.number + 1)
        return None

    def previous_link(self):
        """URL for the previous page of results (or None)."""
        if self.has_previous():
            return self.paginator.link_template % (self.number - 1)
        return None

    def start_index(self):
        """Returns the 1-based index of the first object on this page, relative to total objects in the paginator."""
        offset = self.paginator.offset
        if offset is None:
            raise ValueError("Can't determine start index of paginator with no offset")
        return offset


validate

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

Class description:
Serializer for handling query parameters.

Method signatures and docstrings:
- def validate(self, data): Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if field inputs are invalid
- def validate_group_by(self, value): Validate incoming group_by data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if group_by field inputs are invalid

Implement the Python class `AWSQueryParamSerializer` described below.

Class description:
Serializer for handling query parameters.

Method signatures and docstrings:
- def validate(self, data): Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if field inputs are invalid
- def validate_group_by(self, value): Validate incoming group_by data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if group_by field inputs are invalid

<|skeleton|>
class AWSQueryParamSerializer:
    """Serializer for handling query parameters."""

    def validate(self, data):
        """Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if field inputs are invalid"""
        <|body_0|>

    def validate_group_by(self, value):
        """Validate incoming group_by data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if group_by field inputs are invalid"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().validate(data)
        if not data.get('cost_type'):
            data['cost_type'] = get_cost_type(self.context.get('request'))
        error = {}
        if 'delta' in data.get('order_by', {}) and 'delta' not in data:
            error['order_by'] = _('Cannot order by delta without a delta param')
            raise serializers.ValidationError(error)
        return data
<|end_body_0|>

<|body_start_1|>
        max_value = get_customer_group_by_limit(self.schema)
        if len(value) > max_value:
            error = {'group_by': f'Cost Management supports a max of {max_value} group_by options.'}
            raise serializers.ValidationError(error)
        validate_field(self, 'group_by', self.GROUP_BY_SERIALIZER, value, tag_keys=self.tag_keys)
        group_by_params = self.initial_data.get('group_by', {})
        org_unit_group_keys = ['org_unit_id', 'or:org_unit_id']
        group_by_keys = group_by_params.keys()
        key_used = []
        for acceptable_key in org_unit_group_keys:
            if acceptable_key in group_by_keys:
                key_used.append(acceptable_key)
        if key_used:
            if len(key_used) > 1:
                error = {'or_unit_id': _('Multiple org_unit_id must be represented with the or: prefix.')}
                raise serializers.ValidationError(error)
            key_used = key_used[0]
            request = self.context.get('request')
            if 'costs' not in request.path or self.initial_data.get('group_by', {}).get(key_used, '') == '*':
                error = {'org_unit_id': _('Unsupported parameter or invalid value')}
                raise serializers.ValidationError(error)
            if 'or:' not in key_used:
                if isinstance(group_by_params.get(key_used), list):
                    if len(group_by_params.get(key_used)) > 1:
                        error = {'or_unit_id': _('Multiple org_unit_id must be represented with the or: prefix.')}
                        raise serializers.ValidationError(error)
        return value
<|end_body_1|>

Solution:
class AWSQueryParamSerializer:
    """Serializer for handling query parameters."""

    def validate(self, data):
        """Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if field inputs are invalid"""
        super().validate(data)
        if not data.get('cost_type'):
            data['cost_type'] = get_cost_type(self.context.get('request'))
        error = {}
        if 'delta' in data.get('order_by', {}) and 'delta' not in data:
            error['order_by'] = _('Cannot order by delta without a delta param')
            raise serializers.ValidationError(error)
        return data

    def validate_group_by(self, value):
        """Validate incoming group_by data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if group_by field inputs are invalid"""
        max_value = get_customer_group_by_limit(self.schema)
        if len(value) > max_value:
            error = {'group_by': f'Cost Management supports a max of {max_value} group_by options.'}
            raise serializers.ValidationError(error)
        validate_field(self, 'group_by', self.GROUP_BY_SERIALIZER, value, tag_keys=self.tag_keys)
        group_by_params = self.initial_data.get('group_by', {})
        org_unit_group_keys = ['org_unit_id', 'or:org_unit_id']
        group_by_keys = group_by_params.keys()
        key_used = []
        for acceptable_key in org_unit_group_keys:
            if acceptable_key in group_by_keys:
                key_used.append(acceptable_key)
        if key_used:
            if len(key_used) > 1:
                error = {'or_unit_id': _('Multiple org_unit_id must be represented with the or: prefix.')}
                raise serializers.ValidationError(error)
            key_used = key_used[0]
            request = self.context.get('request')
            if 'costs' not in request.path or self.initial_data.get('group_by', {}).get(key_used, '') == '*':
                error = {'org_unit_id': _('Unsupported parameter or invalid value')}
                raise serializers.ValidationError(error)
            if 'or:' not in key_used:
                if isinstance(group_by_params.get(key_used), list):
                    if len(group_by_params.get(key_used)) > 1:
                        error = {'or_unit_id': _('Multiple org_unit_id must be represented with the or: prefix.')}
                        raise serializers.ValidationError(error)
        return value


forward

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

Class description:
Implement the KernelVar class.

Method signatures and docstrings:
- def __init__(self, embd_dim, hidden_dim, kernel_dim): Currently, this creates a 2-hidden-layer network with ELU non-linearities.
- def forward(self, words): Given words, returns batch_kernel of dimension [-1, kernel_dim]

Implement the Python class `KernelVar` described below.

Class description:
Implement the KernelVar class.

Method signatures and docstrings:
- def __init__(self, embd_dim, hidden_dim, kernel_dim): Currently, this creates a 2-hidden-layer network with ELU non-linearities.
- def forward(self, words): Given words, returns batch_kernel of dimension [-1, kernel_dim]

<|skeleton|>
class KernelVar:

    def __init__(self, embd_dim, hidden_dim, kernel_dim):
        """Currently, this creates a 2-hidden-layer network with ELU non-linearities."""
        <|body_0|>

    def forward(self, words):
        """Given words, returns batch_kernel of dimension [-1, kernel_dim]"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(KernelVar, self).__init__()
        self.embd_dim = embd_dim
        self.hidden_dim = hidden_dim
        self.kernel_dim = kernel_dim
        self.layer1 = nn.Linear(2 * embd_dim, hidden_dim)
        self.layer2 = nn.Linear(hidden_dim, hidden_dim)
        self.layer3 = nn.Linear(hidden_dim, kernel_dim)
        self.net = nn.Sequential(self.layer1, nn.ReLU(), self.layer2, nn.ReLU(), self.layer3)
        self.s_ix = []
        self.e_ix = []
        self.lengths = None
<|end_body_0|>

<|body_start_1|>
        batch_size, max_set_size, embd_dim = words.size()
        self.lengths = words.sum(2, keepdim=True).abs().sign().sum(1, keepdim=True)
        context = (words.sum(1, keepdim=True) / self.lengths.expand_as(words.sum(1, keepdim=True))).expand_as(words)
        mask = words.data.sum(2, keepdim=True).abs().sign().expand_as(words).byte()
        words = words.masked_select(Variable(mask)).view(-1, embd_dim)
        context = context.masked_select(Variable(mask)).view(-1, embd_dim)
        batch_x = torch.cat([words, context], dim=1)
        batch_kernel = self.net(batch_x)
        self.s_ix = list(self.lengths.squeeze().cumsum(0).long().data - self.lengths.squeeze().long().data)
        self.e_ix = list(self.lengths.squeeze().cumsum(0).long().data)
        return (batch_kernel, words)
<|end_body_1|>

Solution:
class KernelVar:
    def __init__(self, embd_dim, hidden_dim, kernel_dim):
        """Currently, this creates a 2-hidden-layer network with ELU non-linearities."""
        super(KernelVar, self).__init__()
        self.embd_dim = embd_dim
        self.hidden_dim = hidden_dim
        self.kernel_dim = kernel_dim
        self.layer1 = nn.Linear(2 * embd_dim, hidden_dim)
        self.layer2 = nn.Linear(hidden_dim, hidden_dim)
        self.layer3 = nn.Linear(hidden_dim, kernel_dim)
        self.net = nn.Sequential(self.layer1, nn.ReLU(), self.layer2, nn.ReLU(), self.layer3)
        self.s_ix = []
        self.e_ix = []
        self.lengths = None

    def forward(self, words):
        """Given words, returns batch_kernel of dimension [-1, kernel_dim]"""
        batch_size, max_set_size, embd_dim = words.size()
        self.lengths = words.sum(2, keepdim=True).abs().sign().sum(1, keepdim=True)
        context = (words.sum(1, keepdim=True) / self.lengths.expand_as(words.sum(1, keepdim=True))).expand_as(words)
        mask = words.data.sum(2, keepdim=True).abs().sign().expand_as(words).byte()
        words = words.masked_select(Variable(mask)).view(-1, embd_dim)
        context = context.masked_select(Variable(mask)).view(-1, embd_dim)
        batch_x = torch.cat([words, context], dim=1)
        batch_kernel = self.net(batch_x)
        self.s_ix = list(self.lengths.squeeze().cumsum(0).long().data - self.lengths.squeeze().long().data)
        self.e_ix = list(self.lengths.squeeze().cumsum(0).long().data)
        return (batch_kernel, words)


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

Class description:
Returns rate quote request and rate quote view results.

Method signatures and docstrings:
- def get_results(self, term=None, amortization=None): Returns rate quote results. Every result object includes the par_lender and matching scenarios. :param term: str, term :param amortization: str, amortization :return: rate quotes :rtype: `dict`
- def get_unique_results(self): Return best rate quote scenarios for each term and amortization. - Standard Scenarios: * 30 Year, Fixed * 15 Year, Fixed * 7 Year, Variable * 5 Year, Variable * amortization: Fixed, Variable :return: `dict` rate quotes :rtype: DRF Response

Implement the Python class `RateQuoteMixin` described below.

Class description:
Returns rate quote request and rate quote view results.

Method signatures and docstrings:
- def get_results(self, term=None, amortization=None): Returns rate quote results. Every result object includes the par_lender and matching scenarios. :param term: str, term :param amortization: str, amortization :return: rate quotes :rtype: `dict`
- def get_unique_results(self): Return best rate quote scenarios for each term and amortization. - Standard Scenarios: * 30 Year, Fixed * 15 Year, Fixed * 7 Year, Variable * 5 Year, Variable * amortization: Fixed, Variable :return: `dict` rate quotes :rtype: DRF Response

<|skeleton|>
class RateQuoteMixin:
    """Returns rate quote request and rate quote view results."""

    def get_results(self, term=None, amortization=None):
        """Returns rate quote results. Every result object includes the par_lender and matching scenarios. :param term: str, term :param amortization: str, amortization :return: rate quotes :rtype: `dict`"""
        <|body_0|>

    def get_unique_results(self):
        """Return best rate quote scenarios for each term and amortization. - Standard Scenarios: * 30 Year, Fixed * 15 Year, Fixed * 7 Year, Variable * 5 Year, Variable * amortization: Fixed, Variable :return: `dict` rate quotes :rtype: DRF Response"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        lender = self.rate_quote_request.get_par_lender(term, amortization)
        scenarios = self.rate_quote_request.get_scenarios(term, amortization)
        par_lender = self.serializer(lender).data
        data = self.serializer(scenarios, many=True).data
        results = {'par_lender': par_lender if par_lender else None, 'request_uuid': self.rate_quote_request.uuid, 'results': data if data else None, 'term': par_lender['term'] if par_lender else None, 'amortization_type': par_lender['amortization_type'] if par_lender else None}
        return response.Response(results, status=status.HTTP_200_OK)
<|end_body_0|>

<|body_start_1|>
        options = {'30 Year': 'Fixed', '15 Year': 'Fixed', '7 Year': 'Variable', '5 Year': 'Variable'}
        lenders = []
        for term, amortization in options.iteritems():
            lender = self.rate_quote_request.get_par_lender(term, amortization)
            lenders.append(lender if lender else None)
        lenders = self.serializer(lenders, many=True).data
        results = {'request_uuid': self.rate_quote_request.uuid, 'results': lenders if lenders else None}
        return response.Response(results, status=status.HTTP_200_OK)
<|end_body_1|>

Solution:
class RateQuoteMixin:
    """Returns rate quote request and rate quote view results."""

    def get_results(self, term=None, amortization=None):
        """Returns rate quote results. Every result object includes the par_lender and matching scenarios. :param term: str, term :param amortization: str, amortization :return: rate quotes :rtype: `dict`"""
        lender = self.rate_quote_request.get_par_lender(term, amortization)
        scenarios = self.rate_quote_request.get_scenarios(term, amortization)
        par_lender = self.serializer(lender).data
        data = self.serializer(scenarios, many=True).data
        results = {'par_lender': par_lender if par_lender else None, 'request_uuid': self.rate_quote_request.uuid, 'results': data if data else None, 'term': par_lender['term'] if par_lender else None, 'amortization_type': par_lender['amortization_type'] if par_lender else None}
        return response.Response(results, status=status.HTTP_200_OK)

    def get_unique_results(self):
        """Return best rate quote scenarios for each term and amortization. - Standard Scenarios: * 30 Year, Fixed * 15 Year, Fixed * 7 Year, Variable * 5 Year, Variable * amortization: Fixed, Variable :return: `dict` rate quotes :rtype: DRF Response"""
        options = {'30 Year': 'Fixed', '15 Year': 'Fixed', '7 Year': 'Variable', '5 Year': 'Variable'}
        lenders = []
        for term, amortization in options.iteritems():
            lender = self.rate_quote_request.get_par_lender(term, amortization)
            lenders.append(lender if lender else None)
        lenders = self.serializer(lenders, many=True).data
        results = {'request_uuid': self.rate_quote_request.uuid, 'results': lenders if lenders else None}
        return response.Response(results, status=status.HTTP_200_OK)


parse

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

Class description:
Implement the ChiIlMedicalDistrictSpider class.

Method signatures and docstrings:
- def parse(self, response): `parse` should always `yield` Meeting items. Change the `_parse_title`, `_parse_start`, etc methods to fit your scraping needs.
- def _parse_title(self, links): Parse or generate meeting title.
- def _parse_classification(self, links): Parse or generate classification from allowed options.
- def _parse_start(self, start_str, year=None): Parse start datetime as a naive datetime object.
- def _parse_upcoming(self, response): Return a list of naive datetimes to upcoming meetings
- def _parse_link_date_map(self, response): Generate a defaultdict mapping of meeting dates and associated links

Implement the Python class `ChiIlMedicalDistrictSpider` described below.

Class description:
Implement the ChiIlMedicalDistrictSpider class.

Method signatures and docstrings:
- def parse(self, response): `parse` should always `yield` Meeting items. Change the `_parse_title`, `_parse_start`, etc methods to fit your scraping needs.
- def _parse_title(self, links): Parse or generate meeting title.
- def _parse_classification(self, links): Parse or generate classification from allowed options.
- def _parse_start(self, start_str, year=None): Parse start datetime as a naive datetime object.
- def _parse_upcoming(self, response): Return a list of naive datetimes to upcoming meetings
- def _parse_link_date_map(self, response): Generate a defaultdict mapping of meeting dates and associated links

<|skeleton|>
class ChiIlMedicalDistrictSpider:

    def parse(self, response):
        """`parse` should always `yield` Meeting items. Change the `_parse_title`, `_parse_start`, etc methods to fit your scraping needs."""
        <|body_0|>

    def _parse_title(self, links):
        """Parse or generate meeting title."""
        <|body_1|>

    def _parse_classification(self, links):
        """Parse or generate classification from allowed options."""
        <|body_2|>

    def _parse_start(self, start_str, year=None):
        """Parse start datetime as a naive datetime object."""
        <|body_3|>

    def _parse_upcoming(self, response):
        """Return a list of naive datetimes to upcoming meetings"""
        <|body_4|>

    def _parse_link_date_map(self, response):
        """Generate a defaultdict mapping of meeting dates and associated links"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        link_date_map = self._parse_link_date_map(response)
        meeting_dt_list = self._parse_upcoming(response)
        meeting_dates = [dt.date() for dt in meeting_dt_list]
        for link_date in link_date_map.keys():
            if link_date not in meeting_dates:
                meeting_dt_list.append(datetime.combine(link_date, time(0)))
        for meeting_dt in set(meeting_dt_list):
            meeting_links = link_date_map[meeting_dt.date()]
            meeting = Meeting(title=self._parse_title(meeting_links), description='', classification=self._parse_classification(meeting_links), start=meeting_dt, end=None, all_day=False, time_notes='', location=self.location, links=meeting_links, source=response.url)
            meeting['status'] = self._get_status(meeting)
            meeting['id'] = self._get_id(meeting)
            yield meeting
<|end_body_0|>

<|body_start_1|>
        for link in links:
            if 'hearing' in link['title'].lower():
                return link['title'].replace('Notice', '').strip()
            if 'special' in link['title'].lower():
                return 'Special Meeting'
        return 'Illinois Medical District Commission'
<|end_body_1|>

<|body_start_2|>
        for link in links:
            if 'hearing' in link['title'].lower():
                return FORUM
        return COMMISSION
<|end_body_2|>

<|body_start_3|>
        date_re = '\\w+\\s+\\d{1,2}' if year else '\\w+\\s+\\d{1,2},\\s+\\d{4}'
        date_match = re.search(date_re, start_str)
        if not date_match:
            return
        date_str = date_match.group().replace(',', '')
        if year:
            date_str += ' {}'.format(year)
        time_match = re.search('\\d{1,2}:\\d{2}\\s+[APM\\.]{2,4}', start_str)
        if not year or year == '2019':
            time_str = '8:00 AM'
        else:
            time_str = '12:00 AM'
        if time_match:
            time_str = time_match.group().replace('.', '').strip()
        return datetime.strptime('{} {}'.format(date_str, time_str), '%B %d %Y %I:%M %p')
<|end_body_3|>

<|body_start_4|>
        upcoming_dts = []
        for upcoming in response.css('.vc_col-sm-4.column_container:nth-child(1) .mk-text-block.indent16 p *::text'):
            start = self._parse_start(upcoming.extract())
            if start:
                upcoming_dts.append(start)
        return upcoming_dts
<|end_body_4|>

<|body_start_5|>
        link_date_map = defaultdict(list)
        for link in response.css('.vc_col-sm-4.column_container:nth-child(1) .mk-text-block.indent16')[:1].css('a'):
            link_str = link.xpath('./text()').extract_first()
            link_start = self._parse_start(link_str)
            if link_start:
                link_date_map[link_start.date()].append({'title': re.sub('\\s+', ' ', link_str.split(' – ')[-1]).strip(), 'href': link.attrib['href']})
        for section in response.css('.vc_col-sm-4.column_container:nth-child(1) .vc_tta-panel'):
            year_str = section.css('.vc_tta-title-text::text').extract_first().strip()
            for section_link in section.css('p > a'):
                link_str = section_link.xpath('./text()').extract_first()
                link_dt = self._parse_start(link_str, year=year_str)
                if link_dt:
                    link_date_map[link_dt.date()].append({'title': re.sub('\\s+', ' ', link_str.split(' – ')[-1]).strip(), 'href': section_link.xpath('@href').extract_first()})
        return link_date_map
<|end_body_5|>

Solution:
class ChiIlMedicalDistrictSpider:
    def parse(self, response):
        """`parse` should always `yield` Meeting items. Change the `_parse_title`, `_parse_start`, etc methods to fit your scraping needs."""
        link_date_map = self._parse_link_date_map(response)
        meeting_dt_list = self._parse_upcoming(response)
        meeting_dates = [dt.date() for dt in meeting_dt_list]
        for link_date in link_date_map.keys():
            if link_date not in meeting_dates:
                meeting_dt_list.append(datetime.combine(link_date, time(0)))
        for meeting_dt in set(meeting_dt_list):
            meeting_links = link_date_map[meeting_dt.date()]
            meeting = Meeting(title=self._parse_title(meeting_links), description='', classification=self._parse_classification(meeting_links), start=meeting_dt, end=None, all_day=False, time_notes='', location=self.location, links=meeting_links, source=response.url)
            meeting['status'] = self._get_status(meeting)
            meeting['id'] = self._get_id(meeting)
            yield meeting

    def _parse_title(self, links):
        """Parse or generate meeting title."""
        for link in links:
            if 'hearing' in link['title'].lower():
                return link['title'].replace('Notice', '').strip()
            if 'special' in link['title'].lower():
                return 'Special Meeting'
        return 'Illinois Medical District Commission'

    def _parse_classification(self, links):
        """Parse or generate classification from allowed options."""
        for link in links:
            if 'hearing' in link['title'].lower():
                return FORUM
        return COMMISSION

    def _parse_start(self, start_str, year=None):
        """Parse start datetime as a naive datetime object."""
        date_re = '\\w+\\s+\\d{1,2}' if year else '\\w+\\s+\\d{1,2},\\s+\\d{4}'
        date_match = re.search(date_re, start_str)
        if not date_match:
            return
        date_str = date_match.group().replace(',', '')
        if year:
            date_str += ' {}'.format(year)
        time_match = re.search('\\d{1,2}:\\d{2}\\s+[APM\\.]{2,4}', start_str)
        if not year or year == '2019':
            time_str = '8:00 AM'
        else:
            time_str = '12:00 AM'
        if time_match:
            time_str = time_match.group().replace('.', '').strip()
        return datetime.strptime('{} {}'.format(date_str, time_str), '%B %d %Y %I:%M %p')

    def _parse_upcoming(self, response):
        """Return a list of naive datetimes to upcoming meetings"""
        upcoming_dts = []
        for upcoming in response.css('.vc_col-sm-4.column_container:nth-child(1) .mk-text-block.indent16 p *::text'):
            start = self._parse_start(upcoming.extract())
            if start:
                upcoming_dts.append(start)
        return upcoming_dts

    def _parse_link_date_map(self, response):
        """Generate a defaultdict mapping of meeting dates and associated links"""
        link_date_map = defaultdict(list)
        for link in response.css('.vc_col-sm-4.column_container:nth-child(1) .mk-text-block.indent16')[:1].css('a'):
            link_str = link.xpath('./text()').extract_first()
            link_start = self._parse_start(link_str)
            if link_start:
                link_date_map[link_start.date()].append({'title': re.sub('\\s+', ' ', link_str.split(' – ')[-1]).strip(), 'href': link.attrib['href']})
        for section in response.css('.vc_col-sm-4.column_container:nth-child(1) .vc_tta-panel'):
            year_str = section.css('.vc_tta-title-text::text').extract_first().strip()
            for section_link in section.css('p > a'):
                link_str = section_link.xpath('./text()').extract_first()
                link_dt = self._parse_start(link_str, year=year_str)
                if link_dt:
                    link_date_map[link_dt.date()].append({'title': re.sub('\\s+', ' ', link_str.split(' – ')[-1]).strip(), 'href': section_link.xpath('@href').extract_first()})
        return link_date_map


call

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

Class description:
The architecture of generator is a modified U-Net.

Method signatures and docstrings:
- def __init__(self): The construct function.
- def call(self, x, training=True): Calls the model on new inputs. Args: x: The origin image before translation. training: If training. Returns: The generated image.

Implement the Python class `Generator` described below.

Class description:
The architecture of generator is a modified U-Net.

Method signatures and docstrings:
- def __init__(self): The construct function.
- def call(self, x, training=True): Calls the model on new inputs. Args: x: The origin image before translation. training: If training. Returns: The generated image.

<|skeleton|>
class Generator:
    """The architecture of generator is a modified U-Net."""

    def __init__(self):
        """The construct function."""
        <|body_0|>

    def call(self, x, training=True):
        """Calls the model on new inputs. Args: x: The origin image before translation. training: If training. Returns: The generated image."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(Generator, self).__init__()
        initializer = tf.random_normal_initializer(0.0, 0.02)
        self.down1 = Downsample(64, 4, apply_batchnorm=False)
        self.down2 = Downsample(128, 4)
        self.down3 = Downsample(256, 4)
        self.down4 = Downsample(512, 4)
        self.down5 = Downsample(512, 4)
        self.down6 = Downsample(512, 4)
        self.down7 = Downsample(512, 4)
        self.down8 = Downsample(512, 4)
        self.up1 = Upsample(512, 4, apply_dropout=True)
        self.up2 = Upsample(512, 4, apply_dropout=True)
        self.up3 = Upsample(512, 4, apply_dropout=True)
        self.up4 = Upsample(512, 4)
        self.up5 = Upsample(256, 4)
        self.up6 = Upsample(128, 4)
        self.up7 = Upsample(64, 4)
        self.last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, (4, 4), strides=2, padding='same', kernel_initializer=initializer)
<|end_body_0|>

<|body_start_1|>
        x1 = self.down1(x, training=training)
        x2 = self.down2(x1, training=training)
        x3 = self.down3(x2, training=training)
        x4 = self.down4(x3, training=training)
        x5 = self.down5(x4, training=training)
        x6 = self.down6(x5, training=training)
        x7 = self.down7(x6, training=training)
        x8 = self.down8(x7, training=training)
        x9 = self.up1(x8, x7, training=training)
        x10 = self.up2(x9, x6, training=training)
        x11 = self.up3(x10, x5, training=training)
        x12 = self.up4(x11, x4, training=training)
        x13 = self.up5(x12, x3, training=training)
        x14 = self.up6(x13, x2, training=training)
        x15 = self.up7(x14, x1, training=training)
        x16 = self.last(x15)
        x16 = tf.nn.tanh(x16)
        return x16
<|end_body_1|>

Solution:
class Generator:
    """The architecture of generator is a modified U-Net."""

    def __init__(self):
        """The construct function."""
        super(Generator, self).__init__()
        initializer = tf.random_normal_initializer(0.0, 0.02)
        self.down1 = Downsample(64, 4, apply_batchnorm=False)
        self.down2 = Downsample(128, 4)
        self.down3 = Downsample(256, 4)
        self.down4 = Downsample(512, 4)
        self.down5 = Downsample(512, 4)
        self.down6 = Downsample(512, 4)
        self.down7 = Downsample(512, 4)
        self.down8 = Downsample(512, 4)
        self.up1 = Upsample(512, 4, apply_dropout=True)
        self.up2 = Upsample(512, 4, apply_dropout=True)
        self.up3 = Upsample(512, 4, apply_dropout=True)
        self.up4 = Upsample(512, 4)
        self.up5 = Upsample(256, 4)
        self.up6 = Upsample(128, 4)
        self.up7 = Upsample(64, 4)
        self.last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, (4, 4), strides=2, padding='same', kernel_initializer=initializer)

    def call(self, x, training=True):
        """Calls the model on new inputs. Args: x: The origin image before translation. training: If training. Returns: The generated image."""
        x1 = self.down1(x, training=training)
        x2 = self.down2(x1, training=training)
        x3 = self.down3(x2, training=training)
        x4 = self.down4(x3, training=training)
        x5 = self.down5(x4, training=training)
        x6 = self.down6(x5, training=training)
        x7 = self.down7(x6, training=training)
        x8 = self.down8(x7, training=training)
        x9 = self.up1(x8, x7, training=training)
        x10 = self.up2(x9, x6, training=training)
        x11 = self.up3(x10, x5, training=training)
        x12 = self.up4(x11, x4, training=training)
        x13 = self.up5(x12, x3, training=training)
        x14 = self.up6(x13, x2, training=training)
        x15 = self.up7(x14, x1, training=training)
        x16 = self.last(x15)
        x16 = tf.nn.tanh(x16)
        return x16


MutateFeedItemTargets

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

Class description:
Proto file describing the FeedItemTarget service. Service to manage feed item targets.

Method signatures and docstrings:
- def GetFeedItemTarget(self, request, context): Returns the requested feed item targets in full detail.
- def MutateFeedItemTargets(self, request, context): Creates or removes feed item targets. Operation statuses are returned.

Implement the Python class `FeedItemTargetServiceServicer` described below.

Class description:
Proto file describing the FeedItemTarget service. Service to manage feed item targets.

Method signatures and docstrings:
- def GetFeedItemTarget(self, request, context): Returns the requested feed item targets in full detail.
- def MutateFeedItemTargets(self, request, context): Creates or removes feed item targets. Operation statuses are returned.

<|skeleton|>
class FeedItemTargetServiceServicer:
    """Proto file describing the FeedItemTarget service. Service to manage feed item targets."""

    def GetFeedItemTarget(self, request, context):
        """Returns the requested feed item targets in full detail."""
        <|body_0|>

    def MutateFeedItemTargets(self, request, context):
        """Creates or removes feed item targets. Operation statuses are returned."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')
<|end_body_0|>

<|body_start_1|>
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')
<|end_body_1|>

Solution:
class FeedItemTargetServiceServicer:
    """Proto file describing the FeedItemTarget service. Service to manage feed item targets."""

    def GetFeedItemTarget(self, request, context):
        """Returns the requested feed item targets in full detail."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def MutateFeedItemTargets(self, request, context):
        """Creates or removes feed item targets. Operation statuses are returned."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')


put

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

Class description:
Handles user facebook event upload

Method signatures and docstrings:
- def post(self, uid): Creates a new event with Facebook event JSON (that is obtained from Facebook Graph API). --- tags: - events parameters: - in: body name: body schema: id: UserEventJSON type: object required: - description - end_time - start_time - place - id properties: description: type: string example: "Advance Sale begins Friday, 6/1 at 11AM PDT www.coachella.com" end_time: type: string example: "2019-04-14T23:59:00-0700" start_time: type: string example: "2019-04-12T12:00:00-0700" place: type: object properties: name: type: string example: "Coachella" location: type: object properties: latitude: type: number example: 33.679974 longitude: type: number example: -116.237221 id: example: "20281766647" id: ty
- def put(self, uid): Creates many new events with a list of Facebook event JSON's (that are obtained from Facebook Graph API). --- tags: - events parameters: - in: body name: body schema: properties: data: type: array items: $ref: "#/definitions/UserEventJSON" :param uid: :return:

Implement the Python class `UserEventService` described below.

Class description:
Handles user facebook event upload

Method signatures and docstrings:
- def post(self, uid): Creates a new event with Facebook event JSON (that is obtained from Facebook Graph API). --- tags: - events parameters: - in: body name: body schema: id: UserEventJSON type: object required: - description - end_time - start_time - place - id properties: description: type: string example: "Advance Sale begins Friday, 6/1 at 11AM PDT www.coachella.com" end_time: type: string example: "2019-04-14T23:59:00-0700" start_time: type: string example: "2019-04-12T12:00:00-0700" place: type: object properties: name: type: string example: "Coachella" location: type: object properties: latitude: type: number example: 33.679974 longitude: type: number example: -116.237221 id: example: "20281766647" id: ty
- def put(self, uid): Creates many new events with a list of Facebook event JSON's (that are obtained from Facebook Graph API). --- tags: - events parameters: - in: body name: body schema: properties: data: type: array items: $ref: "#/definitions/UserEventJSON" :param uid: :return:

<|skeleton|>
class UserEventService:
    """Handles user facebook event upload"""

    def post(self, uid):
        """Creates a new event with Facebook event JSON (that is obtained from Facebook Graph API). --- tags: - events parameters: - in: body name: body schema: id: UserEventJSON type: object required: - description - end_time - start_time - place - id properties: description: type: string example: "Advance Sale begins Friday, 6/1 at 11AM PDT www.coachella.com" end_time: type: string example: "2019-04-14T23:59:00-0700" start_time: type: string example: "2019-04-12T12:00:00-0700" place: type: object properties: name: type: string example: "Coachella" location: type: object properties: latitude: type: number example: 33.679974 longitude: type: number example: -116.237221 id: example: "20281766647" id: ty"""
        <|body_0|>

    def put(self, uid):
        """Creates many new events with a list of Facebook event JSON's (that are obtained from Facebook Graph API). --- tags: - events parameters: - in: body name: body schema: properties: data: type: array items: $ref: "#/definitions/UserEventJSON" :param uid: :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        json_data = request.get_json()
        b = event_builders.FbEventBuilder()
        b.build_with_fb_dict(json_data)
        e: event_models.SocialEvent = b.export_as_class(event_models.SocialEvent)
        ref: DocumentReference = EventDao().create_fb_event(e)
        e.set_firestore_ref(ref)
        dict_view = e.to_dict_view()
        dict_view['eventId'] = ref.id
        UserDao().add_user_event_dict(uid, dict_view['fbEventId'], dict_view)
        return {'id': e.get_firestore_ref().id}
<|end_body_0|>

<|body_start_1|>
        json_data = request.get_json()
        event_dicts = json_data['data']
        ids = list()
        for event_dict in event_dicts:
            event_id = create_fb_event(event_dict, uid)
            ids.append(event_id)
        return {'ids': ids}
<|end_body_1|>

Solution:
class UserEventService:
    """Handles user facebook event upload"""

    def post(self, uid):
        """Creates a new event with Facebook event JSON (that is obtained from Facebook Graph API). --- tags: - events parameters: - in: body name: body schema: id: UserEventJSON type: object required: - description - end_time - start_time - place - id properties: description: type: string example: "Advance Sale begins Friday, 6/1 at 11AM PDT www.coachella.com" end_time: type: string example: "2019-04-14T23:59:00-0700" start_time: type: string example: "2019-04-12T12:00:00-0700" place: type: object properties: name: type: string example: "Coachella" location: type: object properties: latitude: type: number example: 33.679974 longitude: type: number example: -116.237221 id: example: "20281766647" id: ty"""
        json_data = request.get_json()
        b = event_builders.FbEventBuilder()
        b.build_with_fb_dict(json_data)
        e: event_models.SocialEvent = b.export_as_class(event_models.SocialEvent)
        ref: DocumentReference = EventDao().create_fb_event(e)
        e.set_firestore_ref(ref)
        dict_view = e.to_dict_view()
        dict_view['eventId'] = ref.id
        UserDao().add_user_event_dict(uid, dict_view['fbEventId'], dict_view)
        return {'id': e.get_firestore_ref().id}

    def put(self, uid):
        """Creates many new events with a list of Facebook event JSON's (that are obtained from Facebook Graph API). --- tags: - events parameters: - in: body name: body schema: properties: data: type: array items: $ref: "#/definitions/UserEventJSON" :param uid: :return:"""
        json_data = request.get_json()
        event_dicts = json_data['data']
        ids = list()
        for event_dict in event_dicts:
            event_id = create_fb_event(event_dict, uid)
            ids.append(event_id)
        return {'ids': ids}


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

Class description:
Chebyshev distance kernel. Given matrices X and RV, this is computed as:: K(x, rv) = max(|x - rv|) for each pair of rows in X and in RV. Attributes ---------- class_type : 'chebyshev-dist' Examples -------- >>> from secml.array import CArray >>> from secml.ml.kernels import CKernelChebyshevDistance >>> x = CArray([[1,2],[3,4]]) >>> v = CArray([[5,6],[7,8]]) >>> print(CKernelChebyshevDistance().k(x,v)) CArray([[-4. -6.] [-2. -4.]]) >>> print(CKernelChebyshevDistance().k(x)) CArray([[-0. -2.] [-2. -0.]])

Method signatures and docstrings:
- def _forward(self, x): Compute (negative) Chebyshev distances between x and cached rv. Parameters ---------- x : CArray or array_like Array of shape (n_x, n_features). Returns ------- kernel : CArray Kernel between x and cached rv, shape (n_x, n_rv).
- def _backward(self, w=None): Calculate gradients of Chebyshev kernel wrt cached vector 'x'. The gradient of the negative Chebyshev distance is given by:: dK(rv,x)/dx = - sign(rv - x) Parameters ---------- w : CArray of shape (1, n_rv) or None if CArray, it is pre-multiplied to the gradient of the module, as in standard reverse-mode autodiff. Returns ------- kernel_gradient : CArray Kernel gradient of rv with respect to vector x, shape (n_rv, n_features) if n_rv > 1 and w is None, else (1, n_features).

Implement the Python class `CKernelChebyshevDistance` described below.

Class description:
Chebyshev distance kernel. Given matrices X and RV, this is computed as:: K(x, rv) = max(|x - rv|) for each pair of rows in X and in RV. Attributes ---------- class_type : 'chebyshev-dist' Examples -------- >>> from secml.array import CArray >>> from secml.ml.kernels import CKernelChebyshevDistance >>> x = CArray([[1,2],[3,4]]) >>> v = CArray([[5,6],[7,8]]) >>> print(CKernelChebyshevDistance().k(x,v)) CArray([[-4. -6.] [-2. -4.]]) >>> print(CKernelChebyshevDistance().k(x)) CArray([[-0. -2.] [-2. -0.]])

Method signatures and docstrings:
- def _forward(self, x): Compute (negative) Chebyshev distances between x and cached rv. Parameters ---------- x : CArray or array_like Array of shape (n_x, n_features). Returns ------- kernel : CArray Kernel between x and cached rv, shape (n_x, n_rv).
- def _backward(self, w=None): Calculate gradients of Chebyshev kernel wrt cached vector 'x'. The gradient of the negative Chebyshev distance is given by:: dK(rv,x)/dx = - sign(rv - x) Parameters ---------- w : CArray of shape (1, n_rv) or None if CArray, it is pre-multiplied to the gradient of the module, as in standard reverse-mode autodiff. Returns ------- kernel_gradient : CArray Kernel gradient of rv with respect to vector x, shape (n_rv, n_features) if n_rv > 1 and w is None, else (1, n_features).

<|skeleton|>
class CKernelChebyshevDistance:
    """Chebyshev distance kernel. Given matrices X and RV, this is computed as:: K(x, rv) = max(|x - rv|) for each pair of rows in X and in RV. Attributes ---------- class_type : 'chebyshev-dist' Examples -------- >>> from secml.array import CArray >>> from secml.ml.kernels import CKernelChebyshevDistance >>> x = CArray([[1,2],[3,4]]) >>> v = CArray([[5,6],[7,8]]) >>> print(CKernelChebyshevDistance().k(x,v)) CArray([[-4. -6.] [-2. -4.]]) >>> print(CKernelChebyshevDistance().k(x)) CArray([[-0. -2.] [-2. -0.]])"""

    def _forward(self, x):
        """Compute (negative) Chebyshev distances between x and cached rv. Parameters ---------- x : CArray or array_like Array of shape (n_x, n_features). Returns ------- kernel : CArray Kernel between x and cached rv, shape (n_x, n_rv)."""
        <|body_0|>

    def _backward(self, w=None):
        """Calculate gradients of Chebyshev kernel wrt cached vector 'x'. The gradient of the negative Chebyshev distance is given by:: dK(rv,x)/dx = - sign(rv - x) Parameters ---------- w : CArray of shape (1, n_rv) or None if CArray, it is pre-multiplied to the gradient of the module, as in standard reverse-mode autodiff. Returns ------- kernel_gradient : CArray Kernel gradient of rv with respect to vector x, shape (n_rv, n_features) if n_rv > 1 and w is None, else (1, n_features)."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if x.issparse is True or self._rv.issparse is True:
            raise TypeError('Chebyshev Kernel not available for sparse data.See `sklearn.metrics.pairwise_distances`.')
        return -CArray(metrics.pairwise.pairwise_distances(x.get_data(), self._rv.get_data(), metric='chebyshev'))
<|end_body_0|>

<|body_start_1|>
        if not self._cached_x.is_vector_like:
            raise ValueError('kernel gradient can be computed only wrt vector-like arrays.')
        if self._rv is None:
            raise ValueError('Please run forward with caching=True or set `rv` first.')
        if self._cached_x.issparse is True or self._rv.issparse is True:
            raise TypeError('Chebyshev Kernel not available for sparse data.See `sklearn.metrics.pairwise_distances`.')
        diff = self._rv - self._cached_x
        m = abs(diff).max(axis=1)
        grad = CArray.zeros(shape=diff.shape)
        grad[diff >= m] = 1
        grad[diff <= -m] = -1
        return grad if w is None else w.dot(grad)
<|end_body_1|>

Solution:
class CKernelChebyshevDistance:
    """Chebyshev distance kernel. Given matrices X and RV, this is computed as:: K(x, rv) = max(|x - rv|) for each pair of rows in X and in RV. Attributes ---------- class_type : 'chebyshev-dist' Examples -------- >>> from secml.array import CArray >>> from secml.ml.kernels import CKernelChebyshevDistance >>> x = CArray([[1,2],[3,4]]) >>> v = CArray([[5,6],[7,8]]) >>> print(CKernelChebyshevDistance().k(x,v)) CArray([[-4. -6.] [-2. -4.]]) >>> print(CKernelChebyshevDistance().k(x)) CArray([[-0. -2.] [-2. -0.]])"""

    def _forward(self, x):
        """Compute (negative) Chebyshev distances between x and cached rv. Parameters ---------- x : CArray or array_like Array of shape (n_x, n_features). Returns ------- kernel : CArray Kernel between x and cached rv, shape (n_x, n_rv)."""
        if x.issparse is True or self._rv.issparse is True:
            raise TypeError('Chebyshev Kernel not available for sparse data.See `sklearn.metrics.pairwise_distances`.')
        return -CArray(metrics.pairwise.pairwise_distances(x.get_data(), self._rv.get_data(), metric='chebyshev'))

    def _backward(self, w=None):
        """Calculate gradients of Chebyshev kernel wrt cached vector 'x'. The gradient of the negative Chebyshev distance is given by:: dK(rv,x)/dx = - sign(rv - x) Parameters ---------- w : CArray of shape (1, n_rv) or None if CArray, it is pre-multiplied to the gradient of the module, as in standard reverse-mode autodiff. Returns ------- kernel_gradient : CArray Kernel gradient of rv with respect to vector x, shape (n_rv, n_features) if n_rv > 1 and w is None, else (1, n_features)."""
        if not self._cached_x.is_vector_like:
            raise ValueError('kernel gradient can be computed only wrt vector-like arrays.')
        if self._rv is None:
            raise ValueError('Please run forward with caching=True or set `rv` first.')
        if self._cached_x.issparse is True or self._rv.issparse is True:
            raise TypeError('Chebyshev Kernel not available for sparse data.See `sklearn.metrics.pairwise_distances`.')
        diff = self._rv - self._cached_x
        m = abs(diff).max(axis=1)
        grad = CArray.zeros(shape=diff.shape)
        grad[diff >= m] = 1
        grad[diff <= -m] = -1
        return grad if w is None else w.dot(grad)


run

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

Class description:
Implement the PaddleCLSConnectionHandler class.

Method signatures and docstrings:
- def __init__(self, cls_engine): The PaddleSpeech CLS Server Connection Handler This connection process every cls server request Args: cls_engine (CLSEngine): The CLS engine
- def run(self, audio_data): engine run Args: audio_data (bytes): base64.b64decode
- def postprocess(self, topk: int): postprocess

Implement the Python class `PaddleCLSConnectionHandler` described below.

Class description:
Implement the PaddleCLSConnectionHandler class.

Method signatures and docstrings:
- def __init__(self, cls_engine): The PaddleSpeech CLS Server Connection Handler This connection process every cls server request Args: cls_engine (CLSEngine): The CLS engine
- def run(self, audio_data): engine run Args: audio_data (bytes): base64.b64decode
- def postprocess(self, topk: int): postprocess

<|skeleton|>
class PaddleCLSConnectionHandler:

    def __init__(self, cls_engine):
        """The PaddleSpeech CLS Server Connection Handler This connection process every cls server request Args: cls_engine (CLSEngine): The CLS engine"""
        <|body_0|>

    def run(self, audio_data):
        """engine run Args: audio_data (bytes): base64.b64decode"""
        <|body_1|>

    def postprocess(self, topk: int):
        """postprocess"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        logger.debug('Create PaddleCLSConnectionHandler to process the cls request')
        self._inputs = OrderedDict()
        self._outputs = OrderedDict()
        self.cls_engine = cls_engine
        self.executor = self.cls_engine.executor
        self._conf = self.executor._conf
        self._label_list = self.executor._label_list
        self.model = self.executor.model
<|end_body_0|>

<|body_start_1|>
        self.preprocess(io.BytesIO(audio_data))
        st = time.time()
        self.infer()
        infer_time = time.time() - st
        logger.debug('inference time: {}'.format(infer_time))
        logger.info('cls engine type: python')
<|end_body_1|>

<|body_start_2|>
        assert topk <= len(self._label_list), 'Value of topk is larger than number of labels.'
        result = self._outputs['logits'].squeeze(0).numpy()
        topk_idx = (-result).argsort()[:topk]
        topk_results = []
        for idx in topk_idx:
            res = {}
            label, score = (self._label_list[idx], result[idx])
            res['class_name'] = label
            res['prob'] = score
            topk_results.append(res)
        return topk_results
<|end_body_2|>

Solution:
class PaddleCLSConnectionHandler:
    def __init__(self, cls_engine):
        """The PaddleSpeech CLS Server Connection Handler This connection process every cls server request Args: cls_engine (CLSEngine): The CLS engine"""
        super().__init__()
        logger.debug('Create PaddleCLSConnectionHandler to process the cls request')
        self._inputs = OrderedDict()
        self._outputs = OrderedDict()
        self.cls_engine = cls_engine
        self.executor = self.cls_engine.executor
        self._conf = self.executor._conf
        self._label_list = self.executor._label_list
        self.model = self.executor.model

    def run(self, audio_data):
        """engine run Args: audio_data (bytes): base64.b64decode"""
        self.preprocess(io.BytesIO(audio_data))
        st = time.time()
        self.infer()
        infer_time = time.time() - st
        logger.debug('inference time: {}'.format(infer_time))
        logger.info('cls engine type: python')

    def postprocess(self, topk: int):
        """postprocess"""
        assert topk <= len(self._label_list), 'Value of topk is larger than number of labels.'
        result = self._outputs['logits'].squeeze(0).numpy()
        topk_idx = (-result).argsort()[:topk]
        topk_results = []
        for idx in topk_idx:
            res = {}
            label, score = (self._label_list[idx], result[idx])
            res['class_name'] = label
            res['prob'] = score
            topk_results.append(res)
        return topk_results


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

Class description:
Implement the Group class.

Method signatures and docstrings:
- def api_get(request): 响应GET
- def get(request): 响应GET
- def api_put(request): 响应PUT
- def api_post(request): 响应POST
- def api_delete(request): 响应DELETE

Implement the Python class `Group` described below.

Class description:
Implement the Group class.

Method signatures and docstrings:
- def api_get(request): 响应GET
- def get(request): 响应GET
- def api_put(request): 响应PUT
- def api_post(request): 响应POST
- def api_delete(request): 响应DELETE

<|skeleton|>
class Group:

    def api_get(request):
        """响应GET"""
        <|body_0|>

    def get(request):
        """响应GET"""
        <|body_1|>

    def api_put(request):
        """响应PUT"""
        <|body_2|>

    def api_post(request):
        """响应POST"""
        <|body_3|>

    def api_delete(request):
        """响应DELETE"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        cert_setting = WxCertSettings.objects.filter(owner_id=request.manager.id)
        response = create_response(200)
        if cert_setting.count() > 0:
            response.data = {'flag': True}
        else:
            response.data = {'flag': False}
        return response.get_response()
<|end_body_0|>

<|body_start_1|>
        cert_setting = WxCertSettings.objects.filter(owner_id=request.manager.id)
        if not cert_setting.count() > 0:
            return HttpResponseRedirect('/mall2/weixin_certificate/')
        if 'id' in request.GET:
            project_id = 'new_app:group:%s' % request.GET.get('related_page_id', 0)
            try:
                group = app_models.Group.objects.get(id=request.GET['id'])
            except:
                c = RequestContext(request, {'first_nav_name': FIRST_NAV, 'second_navs': mall_export.get_promotion_and_apps_second_navs(request), 'second_nav_name': mall_export.MALL_APPS_SECOND_NAV, 'third_nav_name': 'groups', 'is_deleted_data': True})
                return render_to_response('group/templates/editor/workbench.html', c)
            is_create_new_data = False
        else:
            group = None
            is_create_new_data = True
            project_id = 'new_app:group:0'
        _, app_name, real_project_id = project_id.split(':')
        if real_project_id != '0':
            pagestore = pagestore_manager.get_pagestore('mongo')
            pages = pagestore.get_page_components(real_project_id)
            if not pages:
                c = RequestContext(request, {'first_nav_name': FIRST_NAV, 'second_navs': mall_export.get_promotion_and_apps_second_navs(request), 'second_nav_name': mall_export.MALL_APPS_SECOND_NAV, 'third_nav_name': 'groups', 'is_deleted_data': True})
                return render_to_response('group/templates/editor/workbench.html', c)
        c = RequestContext(request, {'first_nav_name': FIRST_NAV, 'second_navs': mall_export.get_promotion_and_apps_second_navs(request), 'second_nav_name': mall_export.MALL_APPS_SECOND_NAV, 'third_nav_name': 'groups', 'group': group, 'is_create_new_data': is_create_new_data, 'project_id': project_id})
        return render_to_response('group/templates/editor/workbench.html', c)
<|end_body_1|>

<|body_start_2|>
        data = request_util.get_fields_to_be_save(request)
        group = app_models.Group(**data)
        group.save()
        error_msg = None
        data = json.loads(group.to_json())
        data['id'] = data['_id']['$oid']
        if error_msg:
            data['error_msg'] = error_msg
        response = create_response(200)
        response.data = data
        return response.get_response()
<|end_body_2|>

<|body_start_3|>
        data = request_util.get_fields_to_be_save(request)
        update_data = {}
        update_fields = set(['name', 'start_time', 'end_time', 'group_dict', 'status', 'product_id', 'product_img', 'product_name', 'product_price', 'product_socks', 'product_sales', 'product_usercode', 'product_create_at', 'rules', 'material_image', 'share_description'])
        for key, value in data.items():
            if key in update_fields:
                update_data['set__' + key] = value
        app_models.Group.objects(id=request.POST['id']).update(**update_data)
        response = create_response(200)
        return response.get_response()
<|end_body_3|>

<|body_start_4|>
        app_models.Group.objects(id=request.POST['id']).update(set__is_use=app_models.IS_USE_NO)
        response = create_response(200)
        return response.get_response()
<|end_body_4|>

Solution:
class Group:
    def api_get(request):
        """响应GET"""
        cert_setting = WxCertSettings.objects.filter(owner_id=request.manager.id)
        response = create_response(200)
        if cert_setting.count() > 0:
            response.data = {'flag': True}
        else:
            response.data = {'flag': False}
        return response.get_response()

    def get(request):
        """响应GET"""
        cert_setting = WxCertSettings.objects.filter(owner_id=request.manager.id)
        if not cert_setting.count() > 0:
            return HttpResponseRedirect('/mall2/weixin_certificate/')
        if 'id' in request.GET:
            project_id = 'new_app:group:%s' % request.GET.get('related_page_id', 0)
            try:
                group = app_models.Group.objects.get(id=request.GET['id'])
            except:
                c = RequestContext(request, {'first_nav_name': FIRST_NAV, 'second_navs': mall_export.get_promotion_and_apps_second_navs(request), 'second_nav_name': mall_export.MALL_APPS_SECOND_NAV, 'third_nav_name': 'groups', 'is_deleted_data': True})
                return render_to_response('group/templates/editor/workbench.html', c)
            is_create_new_data = False
        else:
            group = None
            is_create_new_data = True
            project_id = 'new_app:group:0'
        _, app_name, real_project_id = project_id.split(':')
        if real_project_id != '0':
            pagestore = pagestore_manager.get_pagestore('mongo')
            pages = pagestore.get_page_components(real_project_id)
            if not pages:
                c = RequestContext(request, {'first_nav_name': FIRST_NAV, 'second_navs': mall_export.get_promotion_and_apps_second_navs(request), 'second_nav_name': mall_export.MALL_APPS_SECOND_NAV, 'third_nav_name': 'groups', 'is_deleted_data': True})
                return render_to_response('group/templates/editor/workbench.html', c)
        c = RequestContext(request, {'first_nav_name': FIRST_NAV, 'second_navs': mall_export.get_promotion_and_apps_second_navs(request), 'second_nav_name': mall_export.MALL_APPS_SECOND_NAV, 'third_nav_name': 'groups', 'group': group, 'is_create_new_data': is_create_new_data, 'project_id': project_id})
        return render_to_response('group/templates/editor/workbench.html', c)

    def api_put(request):
        """响应PUT"""
        data = request_util.get_fields_to_be_save(request)
        group = app_models.Group(**data)
        group.save()
        error_msg = None
        data = json.loads(group.to_json())
        data['id'] = data['_id']['$oid']
        if error_msg:
            data['error_msg'] = error_msg
        response = create_response(200)
        response.data = data
        return response.get_response()

    def api_post(request):
        """响应POST"""
        data = request_util.get_fields_to_be_save(request)
        update_data = {}
        update_fields = set(['name', 'start_time', 'end_time', 'group_dict', 'status', 'product_id', 'product_img', 'product_name', 'product_price', 'product_socks', 'product_sales', 'product_usercode', 'product_create_at', 'rules', 'material_image', 'share_description'])
        for key, value in data.items():
            if key in update_fields:
                update_data['set__' + key] = value
        app_models.Group.objects(id=request.POST['id']).update(**update_data)
        response = create_response(200)
        return response.get_response()

    def api_delete(request):
        """响应DELETE"""
        app_models.Group.objects(id=request.POST['id']).update(set__is_use=app_models.IS_USE_NO)
        response = create_response(200)
        return response.get_response()


interact

Prompt:
Implement the Python class `S3OutputMove` described below.

Class description:
Implement the S3OutputMove class.

Method signatures and docstrings:
- def __init__(self, foldername: str): Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to S3 bucket
- def get_folder_choice(self): Goes through the folder and gets the choice on which folder should be moved Returns ------- str The folder which is chosen to be moved
- def interact(self): Interacts with the user by providing various options
- def ask_deletion() -> str: Since this is deletion, we want confirmation, just to be sure whether to keep the deleted folder locally or to remove it Returns ------- str An yes or no answer to the question

Implement the Python class `S3OutputMove` described below.

Class description:
Implement the S3OutputMove class.

Method signatures and docstrings:
- def __init__(self, foldername: str): Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to S3 bucket
- def get_folder_choice(self): Goes through the folder and gets the choice on which folder should be moved Returns ------- str The folder which is chosen to be moved
- def interact(self): Interacts with the user by providing various options
- def ask_deletion() -> str: Since this is deletion, we want confirmation, just to be sure whether to keep the deleted folder locally or to remove it Returns ------- str An yes or no answer to the question

<|skeleton|>
class S3OutputMove:

    def __init__(self, foldername: str):
        """Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to S3 bucket"""
        <|body_0|>

    def get_folder_choice(self):
        """Goes through the folder and gets the choice on which folder should be moved Returns ------- str The folder which is chosen to be moved"""
        <|body_1|>

    def interact(self):
        """Interacts with the user by providing various options"""
        <|body_2|>

    def ask_deletion() -> str:
        """Since this is deletion, we want confirmation, just to be sure whether to keep the deleted folder locally or to remove it Returns ------- str An yes or no answer to the question"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.foldername = foldername
        self.s3_config_json_filename = os.path.join(AWS_CRED_DIR, 'aws_s3_credentials.json')
        self.s3_util = S3Util(aws_cred_config_json_filename=self.s3_config_json_filename)
        self.msg_printer = wasabi.Printer()
        self.interact()
<|end_body_0|>

<|body_start_1|>
        choices = []
        path = pathlib.Path(self.foldername)
        for dir in path.iterdir():
            choices.append(Choice(str(dir)))
        choices.append(Choice('exit'))
        folder_chose_question = questionary.select('Folder in the directory. Chose one to move to s3', qmark='❓', choices=choices)
        folder_type_answer = folder_chose_question.ask()
        return folder_type_answer
<|end_body_1|>

<|body_start_2|>
        while True:
            answer = self.get_folder_choice()
            if answer == 'exit':
                break
            else:
                with self.msg_printer.loading(f'Uploading {answer} to s3'):
                    folder_name = answer
                    base_folder_name = pathlib.Path(answer).name
                    self.s3_util.upload_folder(folder_name=answer, base_folder_name=base_folder_name)
                self.msg_printer.good(f'Moved folder {answer} to s3')
                deletion_answer = self.ask_deletion()
                if deletion_answer == 'yes':
                    folder_path = pathlib.Path(answer)
                    shutil.rmtree(folder_path)
<|end_body_2|>

<|body_start_3|>
        deletion_question = questionary.rawselect('Do you also want to delete the file locally. Caution! File will be removed locally', qmark='❓', choices=[Choice('yes'), Choice('no')])
        deletion_answer = deletion_question.ask()
        return deletion_answer
<|end_body_3|>

Solution:
class S3OutputMove:
    def __init__(self, foldername: str):
        """Provides an interactive way to move some folders to s3 Parameters ---------- foldername : str The folder name which will be moved to S3 bucket"""
        self.foldername = foldername
        self.s3_config_json_filename = os.path.join(AWS_CRED_DIR, 'aws_s3_credentials.json')
        self.s3_util = S3Util(aws_cred_config_json_filename=self.s3_config_json_filename)
        self.msg_printer = wasabi.Printer()
        self.interact()

    def get_folder_choice(self):
        """Goes through the folder and gets the choice on which folder should be moved Returns ------- str The folder which is chosen to be moved"""
        choices = []
        path = pathlib.Path(self.foldername)
        for dir in path.iterdir():
            choices.append(Choice(str(dir)))
        choices.append(Choice('exit'))
        folder_chose_question = questionary.select('Folder in the directory. Chose one to move to s3', qmark='❓', choices=choices)
        folder_type_answer = folder_chose_question.ask()
        return folder_type_answer

    def interact(self):
        """Interacts with the user by providing various options"""
        while True:
            answer = self.get_folder_choice()
            if answer == 'exit':
                break
            else:
                with self.msg_printer.loading(f'Uploading {answer} to s3'):
                    folder_name = answer
                    base_folder_name = pathlib.Path(answer).name
                    self.s3_util.upload_folder(folder_name=answer, base_folder_name=base_folder_name)
                self.msg_printer.good(f'Moved folder {answer} to s3')
                deletion_answer = self.ask_deletion()
                if deletion_answer == 'yes':
                    folder_path = pathlib.Path(answer)
                    shutil.rmtree(folder_path)

    def ask_deletion() -> str:
        """Since this is deletion, we want confirmation, just to be sure whether to keep the deleted folder locally or to remove it Returns ------- str An yes or no answer to the question"""
        deletion_question = questionary.rawselect('Do you also want to delete the file locally. Caution! File will be removed locally', qmark='❓', choices=[Choice('yes'), Choice('no')])
        deletion_answer = deletion_question.ask()
        return deletion_answer


forward

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

Class description:
Region of interest feature map pooler that supports pooling from one or more feature maps.

Method signatures and docstrings:
- def __init__(self, output_size, scales, sampling_ratio, pooler_type, canonical_box_size=224, canonical_level=4): Args: output_size (int, tuple[int] or list[int]): output size of the pooled region, e.g., 14 x 14. If tuple or list is given, the length must be 2. scales (list[float]): The scale for each low-level pooling op relative to the input image. For a feature map with stride s relative to the input image, scale is defined as 1/s. The stride must be power of 2. When there are multiple scales, they must form a pyramid, i.e. they must be a monotically decreasing geometric sequence with a factor of 1/2. sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. pooler_type (string): Name of the type of pooling operation that should be applied. For instance, "ROIPool" or "ROIAlignV2". can
- def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]): Args: x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those used to construct this module. box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. The box coordinates are defined on the original image and will be scaled by the `scales` argument of :class:`ROIPooler`. Returns: Tensor: A tensor of shape (M, C, output_size, output_size) where M is the total number of boxes aggregated over all N batch images and C is the number of channels in `x`.

Implement the Python class `ROIPooler` described below.

Class description:
Region of interest feature map pooler that supports pooling from one or more feature maps.

Method signatures and docstrings:
- def __init__(self, output_size, scales, sampling_ratio, pooler_type, canonical_box_size=224, canonical_level=4): Args: output_size (int, tuple[int] or list[int]): output size of the pooled region, e.g., 14 x 14. If tuple or list is given, the length must be 2. scales (list[float]): The scale for each low-level pooling op relative to the input image. For a feature map with stride s relative to the input image, scale is defined as 1/s. The stride must be power of 2. When there are multiple scales, they must form a pyramid, i.e. they must be a monotically decreasing geometric sequence with a factor of 1/2. sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. pooler_type (string): Name of the type of pooling operation that should be applied. For instance, "ROIPool" or "ROIAlignV2". can
- def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]): Args: x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those used to construct this module. box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. The box coordinates are defined on the original image and will be scaled by the `scales` argument of :class:`ROIPooler`. Returns: Tensor: A tensor of shape (M, C, output_size, output_size) where M is the total number of boxes aggregated over all N batch images and C is the number of channels in `x`.

<|skeleton|>
class ROIPooler:
    """Region of interest feature map pooler that supports pooling from one or more feature maps."""

    def __init__(self, output_size, scales, sampling_ratio, pooler_type, canonical_box_size=224, canonical_level=4):
        """Args: output_size (int, tuple[int] or list[int]): output size of the pooled region, e.g., 14 x 14. If tuple or list is given, the length must be 2. scales (list[float]): The scale for each low-level pooling op relative to the input image. For a feature map with stride s relative to the input image, scale is defined as 1/s. The stride must be power of 2. When there are multiple scales, they must form a pyramid, i.e. they must be a monotically decreasing geometric sequence with a factor of 1/2. sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. pooler_type (string): Name of the type of pooling operation that should be applied. For instance, "ROIPool" or "ROIAlignV2". can"""
        <|body_0|>

    def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]):
        """Args: x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those used to construct this module. box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. The box coordinates are defined on the original image and will be scaled by the `scales` argument of :class:`ROIPooler`. Returns: Tensor: A tensor of shape (M, C, output_size, output_size) where M is the total number of boxes aggregated over all N batch images and C is the number of channels in `x`."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        if isinstance(output_size, int):
            output_size = (output_size, output_size)
        assert len(output_size) == 2
        assert isinstance(output_size[0], int) and isinstance(output_size[1], int)
        self.output_size = output_size
        if pooler_type == 'ROIAlign':
            self.level_poolers = nn.ModuleList((ROIAlign(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False) for scale in scales))
        elif pooler_type == 'ROIAlignV2':
            self.level_poolers = nn.ModuleList((ROIAlign(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True) for scale in scales))
        elif pooler_type == 'ROIPool':
            self.level_poolers = nn.ModuleList((RoIPool(output_size, spatial_scale=scale) for scale in scales))
        elif pooler_type == 'ROIAlignRotated':
            self.level_poolers = nn.ModuleList((ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio) for scale in scales))
        else:
            raise ValueError('Unknown pooler type: {}'.format(pooler_type))
        min_level = -math.log2(scales[0])
        max_level = -math.log2(scales[-1])
        assert math.isclose(min_level, int(min_level)) and math.isclose(max_level, int(max_level)), 'Featuremap stride is not power of 2!'
        self.min_level = int(min_level)
        self.max_level = int(max_level)
        assert len(scales) == self.max_level - self.min_level + 1, '[ROIPooler] Sizes of input featuremaps do not form a pyramid!'
        assert 0 <= self.min_level and self.min_level <= self.max_level
        self.canonical_level = canonical_level
        assert canonical_box_size > 0
        self.canonical_box_size = canonical_box_size
<|end_body_0|>

<|body_start_1|>
        num_level_assignments = len(self.level_poolers)
        if not is_fx_tracing():
            torch._assert(isinstance(x, list) and isinstance(box_lists, list), 'Arguments to pooler must be lists')
        assert_fx_safe(len(x) == num_level_assignments, 'unequal value, num_level_assignments={}, but x is list of {} Tensors'.format(num_level_assignments, len(x)))
        assert_fx_safe(len(box_lists) == x[0].size(0), 'unequal value, x[0] batch dim 0 is {}, but box_list has length {}'.format(x[0].size(0), len(box_lists)))
        if len(box_lists) == 0:
            return _create_zeros(None, x[0].shape[1], *self.output_size, x[0])
        pooler_fmt_boxes = convert_boxes_to_pooler_format(box_lists)
        if num_level_assignments == 1:
            return self.level_poolers[0](x[0], pooler_fmt_boxes)
        level_assignments = assign_boxes_to_levels(box_lists, self.min_level, self.max_level, self.canonical_box_size, self.canonical_level)
        num_channels = x[0].shape[1]
        output_size = self.output_size[0]
        output = _create_zeros(pooler_fmt_boxes, num_channels, output_size, output_size, x[0])
        for level, pooler in enumerate(self.level_poolers):
            inds = nonzero_tuple(level_assignments == level)[0]
            pooler_fmt_boxes_level = pooler_fmt_boxes[inds]
            output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level))
        return output
<|end_body_1|>

Solution:
class ROIPooler:
    """Region of interest feature map pooler that supports pooling from one or more feature maps."""

    def __init__(self, output_size, scales, sampling_ratio, pooler_type, canonical_box_size=224, canonical_level=4):
        """Args: output_size (int, tuple[int] or list[int]): output size of the pooled region, e.g., 14 x 14. If tuple or list is given, the length must be 2. scales (list[float]): The scale for each low-level pooling op relative to the input image. For a feature map with stride s relative to the input image, scale is defined as 1/s. The stride must be power of 2. When there are multiple scales, they must form a pyramid, i.e. they must be a monotically decreasing geometric sequence with a factor of 1/2. sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. pooler_type (string): Name of the type of pooling operation that should be applied. For instance, "ROIPool" or "ROIAlignV2". can"""
        super().__init__()
        if isinstance(output_size, int):
            output_size = (output_size, output_size)
        assert len(output_size) == 2
        assert isinstance(output_size[0], int) and isinstance(output_size[1], int)
        self.output_size = output_size
        if pooler_type == 'ROIAlign':
            self.level_poolers = nn.ModuleList((ROIAlign(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False) for scale in scales))
        elif pooler_type == 'ROIAlignV2':
            self.level_poolers = nn.ModuleList((ROIAlign(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True) for scale in scales))
        elif pooler_type == 'ROIPool':
            self.level_poolers = nn.ModuleList((RoIPool(output_size, spatial_scale=scale) for scale in scales))
        elif pooler_type == 'ROIAlignRotated':
            self.level_poolers = nn.ModuleList((ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio) for scale in scales))
        else:
            raise ValueError('Unknown pooler type: {}'.format(pooler_type))
        min_level = -math.log2(scales[0])
        max_level = -math.log2(scales[-1])
        assert math.isclose(min_level, int(min_level)) and math.isclose(max_level, int(max_level)), 'Featuremap stride is not power of 2!'
        self.min_level = int(min_level)
        self.max_level = int(max_level)
        assert len(scales) == self.max_level - self.min_level + 1, '[ROIPooler] Sizes of input featuremaps do not form a pyramid!'
        assert 0 <= self.min_level and self.min_level <= self.max_level
        self.canonical_level = canonical_level
        assert canonical_box_size > 0
        self.canonical_box_size = canonical_box_size

    def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]):
        """Args: x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those used to construct this module. box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. The box coordinates are defined on the original image and will be scaled by the `scales` argument of :class:`ROIPooler`. Returns: Tensor: A tensor of shape (M, C, output_size, output_size) where M is the total number of boxes aggregated over all N batch images and C is the number of channels in `x`."""
        num_level_assignments = len(self.level_poolers)
        if not is_fx_tracing():
            torch._assert(isinstance(x, list) and isinstance(box_lists, list), 'Arguments to pooler must be lists')
        assert_fx_safe(len(x) == num_level_assignments, 'unequal value, num_level_assignments={}, but x is list of {} Tensors'.format(num_level_assignments, len(x)))
        assert_fx_safe(len(box_lists) == x[0].size(0), 'unequal value, x[0] batch dim 0 is {}, but box_list has length {}'.format(x[0].size(0), len(box_lists)))
        if len(box_lists) == 0:
            return _create_zeros(None, x[0].shape[1], *self.output_size, x[0])
        pooler_fmt_boxes = convert_boxes_to_pooler_format(box_lists)
        if num_level_assignments == 1:
            return self.level_poolers[0](x[0], pooler_fmt_boxes)
        level_assignments = assign_boxes_to_levels(box_lists, self.min_level, self.max_level, self.canonical_box_size, self.canonical_level)
        num_channels = x[0].shape[1]
        output_size = self.output_size[0]
        output = _create_zeros(pooler_fmt_boxes, num_channels, output_size, output_size, x[0])
        for level, pooler in enumerate(self.level_poolers):
            inds = nonzero_tuple(level_assignments == level)[0]
            pooler_fmt_boxes_level = pooler_fmt_boxes[inds]
            output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level))
        return output


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

Class description:
集群数据处理

Method signatures and docstrings:
- def data_info(self): * 返回字段 ** moid ** name
- def best_host_of_mb(self): 内存最优主机

Implement the Python class `ClusterSerializer` described below.

Class description:
集群数据处理

Method signatures and docstrings:
- def data_info(self): * 返回字段 ** moid ** name
- def best_host_of_mb(self): 内存最优主机

<|skeleton|>
class ClusterSerializer:
    """集群数据处理"""

    def data_info(self):
        """* 返回字段 ** moid ** name"""
        <|body_0|>

    def best_host_of_mb(self):
        """内存最优主机"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        data = self._current_data
        kwargs = super(ClusterSerializer, self).data_info()
        if kwargs and self._detail:
            pass
        return kwargs
<|end_body_0|>

<|body_start_1|>
        data = self._current_data
        hosts = data.host
        best_host = None
        best_perform = 0
        for host in hosts:
            host_serializer = HostSystemSerializer(host).data
            free_mem_mb = host_serializer.get('free_mem_mb')
            if free_mem_mb > best_perform:
                best_perform = free_mem_mb
                best_host = host
        return best_host
<|end_body_1|>

Solution:
class ClusterSerializer:
    """集群数据处理"""

    def data_info(self):
        """* 返回字段 ** moid ** name"""
        data = self._current_data
        kwargs = super(ClusterSerializer, self).data_info()
        if kwargs and self._detail:
            pass
        return kwargs

    def best_host_of_mb(self):
        """内存最优主机"""
        data = self._current_data
        hosts = data.host
        best_host = None
        best_perform = 0
        for host in hosts:
            host_serializer = HostSystemSerializer(host).data
            free_mem_mb = host_serializer.get('free_mem_mb')
            if free_mem_mb > best_perform:
                best_perform = free_mem_mb
                best_host = host
        return best_host