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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def orangesRotting(self, grid): :type grid: List[List[int]] :rtype: int
- def after1min(self, grid): update the grid and return the rotten oranges in next min

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def orangesRotting(self, grid): :type grid: List[List[int]] :rtype: int
- def after1min(self, grid): update the grid and return the rotten oranges in next min

<|skeleton|>
class Solution:

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

    def after1min(self, grid):
        """update the grid and return the rotten oranges in next min"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        freshCnt = 0
        for r in grid:
            freshCnt += r.count(1)
        if freshCnt == 0:
            return 0
        res = 0
        while freshCnt != 0:
            grid, rottenCnt = self.after1min(grid)
            if rottenCnt == 0:
                return -1
            res, freshCnt = (res + 1, freshCnt - rottenCnt)
        return res
<|end_body_0|>

<|body_start_1|>
        rot = set()
        for i in range(len(grid)):
            for j in range(len(grid[0])):
                if grid[i][j] == 2:
                    coordinates = [(0, 1), (1, 0), (-1, 0), (0, -1)]
                    for c in coordinates:
                        if 0 <= i + c[0] and i + c[0] < len(grid) and (0 <= j + c[1]) and (j + c[1] < len(grid[0])):
                            if (i + c[0], j + c[1]) not in rot and grid[i + c[0]][j + c[1]] == 1:
                                rot.add((i + c[0], j + c[1]))
        for c in rot:
            grid[c[0]][c[1]] = 2
        return (grid, len(rot))
<|end_body_1|>

Solution:
class Solution:
    def orangesRotting(self, grid):
        """:type grid: List[List[int]] :rtype: int"""
        freshCnt = 0
        for r in grid:
            freshCnt += r.count(1)
        if freshCnt == 0:
            return 0
        res = 0
        while freshCnt != 0:
            grid, rottenCnt = self.after1min(grid)
            if rottenCnt == 0:
                return -1
            res, freshCnt = (res + 1, freshCnt - rottenCnt)
        return res

    def after1min(self, grid):
        """update the grid and return the rotten oranges in next min"""
        rot = set()
        for i in range(len(grid)):
            for j in range(len(grid[0])):
                if grid[i][j] == 2:
                    coordinates = [(0, 1), (1, 0), (-1, 0), (0, -1)]
                    for c in coordinates:
                        if 0 <= i + c[0] and i + c[0] < len(grid) and (0 <= j + c[1]) and (j + c[1] < len(grid[0])):
                            if (i + c[0], j + c[1]) not in rot and grid[i + c[0]][j + c[1]] == 1:
                                rot.add((i + c[0], j + c[1]))
        for c in rot:
            grid[c[0]][c[1]] = 2
        return (grid, len(rot))


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

Class description:
Implement the ManageCL class.

Method signatures and docstrings:
- def post(self, request): Retrieve checklist with the given clID.
- def delete(self, request): Deletes checklist with the given clID.

Implement the Python class `ManageCL` described below.

Class description:
Implement the ManageCL class.

Method signatures and docstrings:
- def post(self, request): Retrieve checklist with the given clID.
- def delete(self, request): Deletes checklist with the given clID.

<|skeleton|>
class ManageCL:

    def post(self, request):
        """Retrieve checklist with the given clID."""
        <|body_0|>

    def delete(self, request):
        """Deletes checklist with the given clID."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        clID = request.data['clID']
        username = request.data['username']
        token = request.data['token']
        userType = request.data['userType']
        tokenResults = tokenAuthenticate(username, token)
        if len(tokenResults) != 0:
            client.close()
            return Response(tokenResults)
        if not isCM(userType):
            client.close()
            return Response({'error': 'invalid userType'})
        results = retrieveCheckList(clID)
        client.close()
        return Response(results)
<|end_body_0|>

<|body_start_1|>
        username = request.data['username']
        token = request.data['token']
        userType = request.data['userType']
        clID = request.data['clID']
        results = tokenAuthenticate(username, token)
        if len(results) != 0:
            client.close()
            return Response(results)
        if not isCM(userType):
            client.close()
            return Response({'error': 'invalid userType'})
        results = logCheckList(clID, True)
        if 'error' in results:
            return Response(results)
        results = deleteCheckList(clID)
        client.close()
        return Response(results)
<|end_body_1|>

Solution:
class ManageCL:
    def post(self, request):
        """Retrieve checklist with the given clID."""
        clID = request.data['clID']
        username = request.data['username']
        token = request.data['token']
        userType = request.data['userType']
        tokenResults = tokenAuthenticate(username, token)
        if len(tokenResults) != 0:
            client.close()
            return Response(tokenResults)
        if not isCM(userType):
            client.close()
            return Response({'error': 'invalid userType'})
        results = retrieveCheckList(clID)
        client.close()
        return Response(results)

    def delete(self, request):
        """Deletes checklist with the given clID."""
        username = request.data['username']
        token = request.data['token']
        userType = request.data['userType']
        clID = request.data['clID']
        results = tokenAuthenticate(username, token)
        if len(results) != 0:
            client.close()
            return Response(results)
        if not isCM(userType):
            client.close()
            return Response({'error': 'invalid userType'})
        results = logCheckList(clID, True)
        if 'error' in results:
            return Response(results)
        results = deleteCheckList(clID)
        client.close()
        return Response(results)


GetSourcesGEMSS

Docstrings for service methods appear as documentation in the wsdl <b>what fun</b> @param name the name to say hello to @param the number of times to say hello @return the completed array

GetRemoteParameterCode

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

Class description:
Implement the CentralRegistryService class.

Method signatures and docstrings:
- def GetSourcesGEMSS(self): Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array
- def GetTXHISParameters(self): Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array
- def GetHISParamAvailableSources(self, TXHISParameterCode): Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array
- def GetRemoteParameterCode(self, SourceNetworkName, TXHISParameterCode, WSDLLink): Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array

Implement the Python class `CentralRegistryService` described below.

Class description:
Implement the CentralRegistryService class.

Method signatures and docstrings:
- def GetSourcesGEMSS(self): Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array
- def GetTXHISParameters(self): Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array
- def GetHISParamAvailableSources(self, TXHISParameterCode): Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array
- def GetRemoteParameterCode(self, SourceNetworkName, TXHISParameterCode, WSDLLink): Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array

<|skeleton|>
class CentralRegistryService:

    def GetSourcesGEMSS(self):
        """Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array"""
        <|body_0|>

    def GetTXHISParameters(self):
        """Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array"""
        <|body_1|>

    def GetHISParamAvailableSources(self, TXHISParameterCode):
        """Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array"""
        <|body_2|>

    def GetRemoteParameterCode(self, SourceNetworkName, TXHISParameterCode, WSDLLink):
        """Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        sources = db_session.query(models.Sources).all()
        results = [service_detail_from_source(source) for source in sources]
        return results
<|end_body_0|>

<|body_start_1|>
        variables = db_session.query(models.Variables).all()
        results = [parameter_info_from_variable(variable) for variable in variables]
        return results
<|end_body_1|>

<|body_start_2|>
        variable = db_session.query(models.Variables).filter_by(VariableCode=TXHISParameterCode).one()
        results = []
        for mapping in variable.variable_mapping:
            source = mapping.SourceInfo
            source_with_param = SourceWithRemoteParamCode()
            source_with_param.WSDLLink = source.WSDLLink
            source_with_param.SourceName = source.NetworkName
            source_with_param.RemoteParamCode = mapping.RemoteVariableCode
            results.append(source_with_param)
        return results
<|end_body_2|>

<|body_start_3|>
        try:
            source = db_session.query(models.Sources).filter_by(NetworkName=SourceNetworkName, WSDLLink=WSDLLink).one()
        except sa_exceptions.NoResultFound:
            raise Exception('Could not match (network, parameter, wsdllink): (%s, %s, %s)' % (SourceNetworkName, TXHISParameterCode, WSDLLink))
        for mapping in source.availableParameterInfo:
            if mapping.variable.VariableCode == TXHISParameterCode:
                return mapping.RemoteVariableCode
<|end_body_3|>

Solution:
class CentralRegistryService:
    def GetSourcesGEMSS(self):
        """Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array"""
        sources = db_session.query(models.Sources).all()
        results = [service_detail_from_source(source) for source in sources]
        return results

    def GetTXHISParameters(self):
        """Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array"""
        variables = db_session.query(models.Variables).all()
        results = [parameter_info_from_variable(variable) for variable in variables]
        return results

    def GetHISParamAvailableSources(self, TXHISParameterCode):
        """Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array"""
        variable = db_session.query(models.Variables).filter_by(VariableCode=TXHISParameterCode).one()
        results = []
        for mapping in variable.variable_mapping:
            source = mapping.SourceInfo
            source_with_param = SourceWithRemoteParamCode()
            source_with_param.WSDLLink = source.WSDLLink
            source_with_param.SourceName = source.NetworkName
            source_with_param.RemoteParamCode = mapping.RemoteVariableCode
            results.append(source_with_param)
        return results

    def GetRemoteParameterCode(self, SourceNetworkName, TXHISParameterCode, WSDLLink):
        """Docstrings for service methods appear as documentation in the wsdl what fun @param name the name to say hello to @param the number of times to say hello @return the completed array"""
        try:
            source = db_session.query(models.Sources).filter_by(NetworkName=SourceNetworkName, WSDLLink=WSDLLink).one()
        except sa_exceptions.NoResultFound:
            raise Exception('Could not match (network, parameter, wsdllink): (%s, %s, %s)' % (SourceNetworkName, TXHISParameterCode, WSDLLink))
        for mapping in source.availableParameterInfo:
            if mapping.variable.VariableCode == TXHISParameterCode:
                return mapping.RemoteVariableCode


paint

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

Class description:
Implement the DropItem class.

Method signatures and docstrings:
- def __init__(self, itemType=None): Create a draggable item, which can be dropped into the canvas.
- def paint(self, painter, option, widget): Draw the representation.
- def boundingRect(self): Get the bounding rectangle of the item.
- def mousePressEvent(self, event): Handle the mouse events on this item.

Implement the Python class `DropItem` described below.

Class description:
Implement the DropItem class.

Method signatures and docstrings:
- def __init__(self, itemType=None): Create a draggable item, which can be dropped into the canvas.
- def paint(self, painter, option, widget): Draw the representation.
- def boundingRect(self): Get the bounding rectangle of the item.
- def mousePressEvent(self, event): Handle the mouse events on this item.

<|skeleton|>
class DropItem:

    def __init__(self, itemType=None):
        """Create a draggable item, which can be dropped into the canvas."""
        <|body_0|>

    def paint(self, painter, option, widget):
        """Draw the representation."""
        <|body_1|>

    def boundingRect(self):
        """Get the bounding rectangle of the item."""
        <|body_2|>

    def mousePressEvent(self, event):
        """Handle the mouse events on this item."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        super(DropItem, self).__init__()
        if itemType:
            self.device_type = itemType
        self.image = QtGui.QImage(environ['images'] + self.device_type + '.gif')
        if self.image.isNull():
            mainWidgets['log'].append('Unknown node type ' + str(self.device_type))
            return
        self.setCursor(QtCore.Qt.OpenHandCursor)
        if itemType in unimplementedTypes:
            self.setToolTip(self.device_type.center(13) + '\nImplement me.')
            self.setEnabled(False)
        else:
            self.setToolTip(self.device_type.center(21) + '\nDrag onto the canvas.')
<|end_body_0|>

<|body_start_1|>
        painter.setRenderHint(QtGui.QPainter.SmoothPixmapTransform, options['smoothing'])
        if not self.isEnabled():
            transparency = QtGui.QImage(self.image)
            transparency.fill(QtGui.qRgba(0, 0, 0, 50))
            painter.drawImage(QtCore.QPoint(-self.image.width() / 2, -self.image.height() / 2), transparency)
        painter.drawImage(QtCore.QPoint(-self.image.width() / 2, -self.image.height() / 2), self.image)
        device_text = self.device_type
        if options['names']:
            painter.drawText(QtCore.QRectF(-70, self.image.height() / 2, 145, 60), device_text, QtGui.QTextOption(QtCore.Qt.AlignHCenter))
<|end_body_1|>

<|body_start_2|>
        rect = self.image.rect()
        toR = QtCore.QRectF(rect.left() - rect.width() / 2, rect.top() - rect.height() / 2, rect.width(), rect.height())
        return toR
<|end_body_2|>

<|body_start_3|>
        if event.button() != QtCore.Qt.LeftButton:
            event.ignore()
            return
        drag = QtGui.QDrag(event.widget())
        mime = QtCore.QMimeData()
        mime.setText(self.device_type)
        drag.setMimeData(mime)
        drag.setPixmap(QtGui.QPixmap.fromImage(self.image))
        drag.setHotSpot(QtCore.QPoint(15, 30))
        drag.start()
<|end_body_3|>

Solution:
class DropItem:
    def __init__(self, itemType=None):
        """Create a draggable item, which can be dropped into the canvas."""
        super(DropItem, self).__init__()
        if itemType:
            self.device_type = itemType
        self.image = QtGui.QImage(environ['images'] + self.device_type + '.gif')
        if self.image.isNull():
            mainWidgets['log'].append('Unknown node type ' + str(self.device_type))
            return
        self.setCursor(QtCore.Qt.OpenHandCursor)
        if itemType in unimplementedTypes:
            self.setToolTip(self.device_type.center(13) + '\nImplement me.')
            self.setEnabled(False)
        else:
            self.setToolTip(self.device_type.center(21) + '\nDrag onto the canvas.')

    def paint(self, painter, option, widget):
        """Draw the representation."""
        painter.setRenderHint(QtGui.QPainter.SmoothPixmapTransform, options['smoothing'])
        if not self.isEnabled():
            transparency = QtGui.QImage(self.image)
            transparency.fill(QtGui.qRgba(0, 0, 0, 50))
            painter.drawImage(QtCore.QPoint(-self.image.width() / 2, -self.image.height() / 2), transparency)
        painter.drawImage(QtCore.QPoint(-self.image.width() / 2, -self.image.height() / 2), self.image)
        device_text = self.device_type
        if options['names']:
            painter.drawText(QtCore.QRectF(-70, self.image.height() / 2, 145, 60), device_text, QtGui.QTextOption(QtCore.Qt.AlignHCenter))

    def boundingRect(self):
        """Get the bounding rectangle of the item."""
        rect = self.image.rect()
        toR = QtCore.QRectF(rect.left() - rect.width() / 2, rect.top() - rect.height() / 2, rect.width(), rect.height())
        return toR

    def mousePressEvent(self, event):
        """Handle the mouse events on this item."""
        if event.button() != QtCore.Qt.LeftButton:
            event.ignore()
            return
        drag = QtGui.QDrag(event.widget())
        mime = QtCore.QMimeData()
        mime.setText(self.device_type)
        drag.setMimeData(mime)
        drag.setPixmap(QtGui.QPixmap.fromImage(self.image))
        drag.setHotSpot(QtCore.QPoint(15, 30))
        drag.start()


call

Prompt:
Implement the Python class `Pad1D` described below.

Class description:
Pads a (batch, size, channels) tensor.

Method signatures and docstrings:
- def __init__(self, padding, padding_mode, **kwargs): Creates the padding layer. Args: padding: (pad_left, pad_right) pair. How many elements to add to both sides of the second dimension. If this is used for the very first layer of the feelers CNN, this can be thought as the number of feeler entries to add before the first feeler entry and after the last. padding_mode: One of 'zero', 'wrap', or 'repeat'. - zero: For padding out with zeroes. - wrap: For padding wrapping around the tensor. - repeat: For padding with repeated values from the tensor edges. **kwargs: Other kwargs that should be sent to the layer superclass.
- def call(self, inputs): Apply the layer into inputs. Args: inputs: A tensor with shape (batch, size, channels). Returns: Output tensor after applying the layer.

Implement the Python class `Pad1D` described below.

Class description:
Pads a (batch, size, channels) tensor.

Method signatures and docstrings:
- def __init__(self, padding, padding_mode, **kwargs): Creates the padding layer. Args: padding: (pad_left, pad_right) pair. How many elements to add to both sides of the second dimension. If this is used for the very first layer of the feelers CNN, this can be thought as the number of feeler entries to add before the first feeler entry and after the last. padding_mode: One of 'zero', 'wrap', or 'repeat'. - zero: For padding out with zeroes. - wrap: For padding wrapping around the tensor. - repeat: For padding with repeated values from the tensor edges. **kwargs: Other kwargs that should be sent to the layer superclass.
- def call(self, inputs): Apply the layer into inputs. Args: inputs: A tensor with shape (batch, size, channels). Returns: Output tensor after applying the layer.

<|skeleton|>
class Pad1D:
    """Pads a (batch, size, channels) tensor."""

    def __init__(self, padding, padding_mode, **kwargs):
        """Creates the padding layer. Args: padding: (pad_left, pad_right) pair. How many elements to add to both sides of the second dimension. If this is used for the very first layer of the feelers CNN, this can be thought as the number of feeler entries to add before the first feeler entry and after the last. padding_mode: One of 'zero', 'wrap', or 'repeat'. - zero: For padding out with zeroes. - wrap: For padding wrapping around the tensor. - repeat: For padding with repeated values from the tensor edges. **kwargs: Other kwargs that should be sent to the layer superclass."""
        <|body_0|>

    def call(self, inputs):
        """Apply the layer into inputs. Args: inputs: A tensor with shape (batch, size, channels). Returns: Output tensor after applying the layer."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self._padding = padding
        self._padding_mode = padding_mode
        super(Pad1D, self).__init__(**kwargs)
<|end_body_0|>

<|body_start_1|>
        if self._padding_mode == 'zero':
            paddings = ((0, 0), self._padding, (0, 0))
            outputs = tf.pad(inputs, paddings)
        elif self._padding_mode == 'wrap':
            outputs = tf.concat([inputs[:, -self._padding[0]:, :], inputs, inputs[:, :self._padding[1], :]], axis=1)
        elif self._padding_mode == 'repeat':
            outputs = tf.concat([tf.repeat(inputs[:, :1, :], self._padding[0], axis=1), inputs, tf.repeat(inputs[:, -1:, :], self._padding[1], axis=1)], axis=1)
        else:
            raise ValueError(f'Padding mode {self._padding_mode} not supported.')
        return outputs
<|end_body_1|>

Solution:
class Pad1D:
    """Pads a (batch, size, channels) tensor."""

    def __init__(self, padding, padding_mode, **kwargs):
        """Creates the padding layer. Args: padding: (pad_left, pad_right) pair. How many elements to add to both sides of the second dimension. If this is used for the very first layer of the feelers CNN, this can be thought as the number of feeler entries to add before the first feeler entry and after the last. padding_mode: One of 'zero', 'wrap', or 'repeat'. - zero: For padding out with zeroes. - wrap: For padding wrapping around the tensor. - repeat: For padding with repeated values from the tensor edges. **kwargs: Other kwargs that should be sent to the layer superclass."""
        self._padding = padding
        self._padding_mode = padding_mode
        super(Pad1D, self).__init__(**kwargs)

    def call(self, inputs):
        """Apply the layer into inputs. Args: inputs: A tensor with shape (batch, size, channels). Returns: Output tensor after applying the layer."""
        if self._padding_mode == 'zero':
            paddings = ((0, 0), self._padding, (0, 0))
            outputs = tf.pad(inputs, paddings)
        elif self._padding_mode == 'wrap':
            outputs = tf.concat([inputs[:, -self._padding[0]:, :], inputs, inputs[:, :self._padding[1], :]], axis=1)
        elif self._padding_mode == 'repeat':
            outputs = tf.concat([tf.repeat(inputs[:, :1, :], self._padding[0], axis=1), inputs, tf.repeat(inputs[:, -1:, :], self._padding[1], axis=1)], axis=1)
        else:
            raise ValueError(f'Padding mode {self._padding_mode} not supported.')
        return outputs


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

Class description:
Implement the _PolyauthenticationLDAPUser class.

Method signatures and docstrings:
- def ldap_authenticate(self, password): Searches LDAP user and populates his attributes
- def add_user_to_db(self): Creating user id DB

Implement the Python class `_PolyauthenticationLDAPUser` described below.

Class description:
Implement the _PolyauthenticationLDAPUser class.

Method signatures and docstrings:
- def ldap_authenticate(self, password): Searches LDAP user and populates his attributes
- def add_user_to_db(self): Creating user id DB

<|skeleton|>
class _PolyauthenticationLDAPUser:

    def ldap_authenticate(self, password):
        """Searches LDAP user and populates his attributes"""
        <|body_0|>

    def add_user_to_db(self):
        """Creating user id DB"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            self._authenticate_user_dn(password)
            self._check_requirements()
        except self.AuthenticationFailed as e:
            logger.debug(u'Authentication failed for %s: %s' % (self._username, e))
            return False
        except ldap.LDAPError as e:
            logger.warning(u'Caught LDAPError while authenticating %s: %s', self._username, pprint.pformat(e))
            return False
        except Exception:
            logger.exception(u'Caught Exception while authenticating %s', self._username)
            raise
        return True
<|end_body_0|>

<|body_start_1|>
        user = None
        try:
            self._get_or_create_user()
            user = self._user
        except self.AuthenticationFailed as e:
            logger.debug(u'Authentication failed for %s: %s' % (self._username, e))
        except ldap.LDAPError as e:
            logger.warning(u'Caught LDAPError while authenticating %s: %s', self._username, pprint.pformat(e))
        except Exception:
            logger.exception(u'Caught Exception while authenticating %s', self._username)
            raise
        return user
<|end_body_1|>

Solution:
class _PolyauthenticationLDAPUser:
    def ldap_authenticate(self, password):
        """Searches LDAP user and populates his attributes"""
        try:
            self._authenticate_user_dn(password)
            self._check_requirements()
        except self.AuthenticationFailed as e:
            logger.debug(u'Authentication failed for %s: %s' % (self._username, e))
            return False
        except ldap.LDAPError as e:
            logger.warning(u'Caught LDAPError while authenticating %s: %s', self._username, pprint.pformat(e))
            return False
        except Exception:
            logger.exception(u'Caught Exception while authenticating %s', self._username)
            raise
        return True

    def add_user_to_db(self):
        """Creating user id DB"""
        user = None
        try:
            self._get_or_create_user()
            user = self._user
        except self.AuthenticationFailed as e:
            logger.debug(u'Authentication failed for %s: %s' % (self._username, e))
        except ldap.LDAPError as e:
            logger.warning(u'Caught LDAPError while authenticating %s: %s', self._username, pprint.pformat(e))
        except Exception:
            logger.exception(u'Caught Exception while authenticating %s', self._username)
            raise
        return user


计算对齐向量，只有general可以使用

forward

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

Class description:
计算对齐向量，只有general可以使用

Method signatures and docstrings:
- def __init__(self, score_type, hidden_size): Args: score_type: 计算score的方法，'dot', 'general', 'concat' hidden_size: Encoder和Decoder的hidden_size
- def score(self, decoder_rnn_output, encoder_output): 计算Decoder中yt与Encoder中hs的打分。算出所有得分，再softmax就可以算出对齐向量。 下面均是单个batch Args: decoder_rnn_output: [1, h]，Decoder中顶层RNN的输出[1,h] < [1,b,h] encoder_output: [1, h]，Encoder最后的输出[1,h] < [s,b,h]> Returns: energy: 即Yt与Xs的得分
- def forward(self, rnn_outputs, encoder_outputs): ts个时刻，计算ts个与is的对齐向量，也是注意力权值 Args: rnn_outputs: Decoder中GRU的输出[ts, b, h] encoder_outputs: Encoder的最后的输出, [is, b, h] Returns: attn_weights: Yt与所有Xs的注意力权值，[b, ts, is]

Implement the Python class `Attn` described below.

Class description:
计算对齐向量，只有general可以使用

Method signatures and docstrings:
- def __init__(self, score_type, hidden_size): Args: score_type: 计算score的方法，'dot', 'general', 'concat' hidden_size: Encoder和Decoder的hidden_size
- def score(self, decoder_rnn_output, encoder_output): 计算Decoder中yt与Encoder中hs的打分。算出所有得分，再softmax就可以算出对齐向量。 下面均是单个batch Args: decoder_rnn_output: [1, h]，Decoder中顶层RNN的输出[1,h] < [1,b,h] encoder_output: [1, h]，Encoder最后的输出[1,h] < [s,b,h]> Returns: energy: 即Yt与Xs的得分
- def forward(self, rnn_outputs, encoder_outputs): ts个时刻，计算ts个与is的对齐向量，也是注意力权值 Args: rnn_outputs: Decoder中GRU的输出[ts, b, h] encoder_outputs: Encoder的最后的输出, [is, b, h] Returns: attn_weights: Yt与所有Xs的注意力权值，[b, ts, is]

<|skeleton|>
class Attn:
    """计算对齐向量，只有general可以使用"""

    def __init__(self, score_type, hidden_size):
        """Args: score_type: 计算score的方法，'dot', 'general', 'concat' hidden_size: Encoder和Decoder的hidden_size"""
        <|body_0|>

    def score(self, decoder_rnn_output, encoder_output):
        """计算Decoder中yt与Encoder中hs的打分。算出所有得分，再softmax就可以算出对齐向量。 下面均是单个batch Args: decoder_rnn_output: [1, h]，Decoder中顶层RNN的输出[1,h] < [1,b,h] encoder_output: [1, h]，Encoder最后的输出[1,h] < [s,b,h]> Returns: energy: 即Yt与Xs的得分"""
        <|body_1|>

    def forward(self, rnn_outputs, encoder_outputs):
        """ts个时刻，计算ts个与is的对齐向量，也是注意力权值 Args: rnn_outputs: Decoder中GRU的输出[ts, b, h] encoder_outputs: Encoder的最后的输出, [is, b, h] Returns: attn_weights: Yt与所有Xs的注意力权值，[b, ts, is]"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(Attn, self).__init__()
        self.score_type = score_type
        self.hidden_size = hidden_size
        if score_type == 'general':
            self.attn = nn.Linear(hidden_size, hidden_size)
        elif score_type == 'concat':
            self.attn = nn.Linear(hidden_size * 2, hidden_size)
            self.v = nn.Parameter(torch.FloatTensor(1, hidden_size))
<|end_body_0|>

<|body_start_1|>
        if self.score_type == 'dot':
            energy = decoder_rnn_output.squeeze(0).dot(encoder_output.squeeze(0))
        elif self.score_type == 'general':
            energy = self.attn(encoder_output)
            energy = decoder_rnn_output.squeeze(0).dot(energy.squeeze(0))
        elif self.score_type == 'concat':
            h_o = torch.cat((decoder_rnn_output, encoder_output), 1)
            energy = self.attn(h_o)
            energy = self.v.squeeze(0).dot(energy.squeeze(0))
        return energy
<|end_body_1|>

<|body_start_2|>
        target_seqlen = rnn_outputs.size()[0]
        input_seqlen = encoder_outputs.size()[0]
        batch_size = encoder_outputs.size()[1]
        rnn_outputs = rnn_outputs.transpose(0, 1)
        encoder_outputs = encoder_outputs.transpose(0, 1)
        if self.score_type == 'general':
            encoder_outputs = self.attn(encoder_outputs).transpose(1, 2)
            attn_energies = rnn_outputs.bmm(encoder_outputs)
            res = my_log_softmax(attn_energies)
            return res
        attn_energies = get_variable(torch.zeros(batch_size, target_seqlen, input_seqlen))
        for b in range(batch_size):
            decoder_rnn_output = rnn_outputs[b]
            for i in range(seq_len):
                encoder_output = encoder_outputs[i, b, :].squeeze(0)
                attn_energies[b, i] = self.score(decoder_rnn_output, encoder_output)
        attn_weights = get_variable(torch.zeros(this_batch_size, seq_len))
        for b in range(this_batch_size):
            attn_weights[b] = F.softmax(attn_energies[b])
        return attn_weights
<|end_body_2|>

Solution:
class Attn:
    """计算对齐向量，只有general可以使用"""

    def __init__(self, score_type, hidden_size):
        """Args: score_type: 计算score的方法，'dot', 'general', 'concat' hidden_size: Encoder和Decoder的hidden_size"""
        super(Attn, self).__init__()
        self.score_type = score_type
        self.hidden_size = hidden_size
        if score_type == 'general':
            self.attn = nn.Linear(hidden_size, hidden_size)
        elif score_type == 'concat':
            self.attn = nn.Linear(hidden_size * 2, hidden_size)
            self.v = nn.Parameter(torch.FloatTensor(1, hidden_size))

    def score(self, decoder_rnn_output, encoder_output):
        """计算Decoder中yt与Encoder中hs的打分。算出所有得分，再softmax就可以算出对齐向量。 下面均是单个batch Args: decoder_rnn_output: [1, h]，Decoder中顶层RNN的输出[1,h] < [1,b,h] encoder_output: [1, h]，Encoder最后的输出[1,h] < [s,b,h]> Returns: energy: 即Yt与Xs的得分"""
        if self.score_type == 'dot':
            energy = decoder_rnn_output.squeeze(0).dot(encoder_output.squeeze(0))
        elif self.score_type == 'general':
            energy = self.attn(encoder_output)
            energy = decoder_rnn_output.squeeze(0).dot(energy.squeeze(0))
        elif self.score_type == 'concat':
            h_o = torch.cat((decoder_rnn_output, encoder_output), 1)
            energy = self.attn(h_o)
            energy = self.v.squeeze(0).dot(energy.squeeze(0))
        return energy

    def forward(self, rnn_outputs, encoder_outputs):
        """ts个时刻，计算ts个与is的对齐向量，也是注意力权值 Args: rnn_outputs: Decoder中GRU的输出[ts, b, h] encoder_outputs: Encoder的最后的输出, [is, b, h] Returns: attn_weights: Yt与所有Xs的注意力权值，[b, ts, is]"""
        target_seqlen = rnn_outputs.size()[0]
        input_seqlen = encoder_outputs.size()[0]
        batch_size = encoder_outputs.size()[1]
        rnn_outputs = rnn_outputs.transpose(0, 1)
        encoder_outputs = encoder_outputs.transpose(0, 1)
        if self.score_type == 'general':
            encoder_outputs = self.attn(encoder_outputs).transpose(1, 2)
            attn_energies = rnn_outputs.bmm(encoder_outputs)
            res = my_log_softmax(attn_energies)
            return res
        attn_energies = get_variable(torch.zeros(batch_size, target_seqlen, input_seqlen))
        for b in range(batch_size):
            decoder_rnn_output = rnn_outputs[b]
            for i in range(seq_len):
                encoder_output = encoder_outputs[i, b, :].squeeze(0)
                attn_energies[b, i] = self.score(decoder_rnn_output, encoder_output)
        attn_weights = get_variable(torch.zeros(this_batch_size, seq_len))
        for b in range(this_batch_size):
            attn_weights[b] = F.softmax(attn_energies[b])
        return attn_weights


testOutput

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

Class description:
Contains unit tests of the MultiNASA class.

Method signatures and docstrings:
- def setUp(self): A function run before each unit test in this class.
- def testPickle(self): Test that a MultiNASA object can be successfully pickled and unpickled with no loss of information.
- def testOutput(self): Test that we can reconstruct a MultiNASA object from its repr() output with no loss of information.

Implement the Python class `TestMultiNASA` described below.

Class description:
Contains unit tests of the MultiNASA class.

Method signatures and docstrings:
- def setUp(self): A function run before each unit test in this class.
- def testPickle(self): Test that a MultiNASA object can be successfully pickled and unpickled with no loss of information.
- def testOutput(self): Test that we can reconstruct a MultiNASA object from its repr() output with no loss of information.

<|skeleton|>
class TestMultiNASA:
    """Contains unit tests of the MultiNASA class."""

    def setUp(self):
        """A function run before each unit test in this class."""
        <|body_0|>

    def testPickle(self):
        """Test that a MultiNASA object can be successfully pickled and unpickled with no loss of information."""
        <|body_1|>

    def testOutput(self):
        """Test that we can reconstruct a MultiNASA object from its repr() output with no loss of information."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        nasa0 = NASA(coeffs=[11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0], Tmin=(300.0, 'K'), Tmax=(1000.0, 'K'), comment='This data is completely made up and unphysical')
        nasa1 = NASA(coeffs=[21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0], Tmin=(1000.0, 'K'), Tmax=(6000.0, 'K'), comment='This data is also completely made up and unphysical')
        self.thermo = MultiNASA(polynomials=[nasa0, nasa1], Tmin=(300.0, 'K'), Tmax=(6000.0, 'K'), comment='This data is completely made up and unphysical')
<|end_body_0|>

<|body_start_1|>
        import cPickle
        thermo = cPickle.loads(cPickle.dumps(self.thermo))
        self.assertEqual(len(self.thermo.polynomials), len(thermo.polynomials))
        for poly0, poly in zip(self.thermo.polynomials, thermo.polynomials):
            self.assertEqual(poly0.cm2, poly.cm2)
            self.assertEqual(poly0.cm1, poly.cm1)
            self.assertEqual(poly0.c0, poly.c0)
            self.assertEqual(poly0.c1, poly.c1)
            self.assertEqual(poly0.c2, poly.c2)
            self.assertEqual(poly0.c3, poly.c3)
            self.assertEqual(poly0.c4, poly.c4)
            self.assertEqual(poly0.c5, poly.c5)
            self.assertEqual(poly0.c6, poly.c6)
            self.assertEqual(poly0.Tmin.value, poly.Tmin.value)
            self.assertEqual(poly0.Tmin.units, poly.Tmin.units)
            self.assertEqual(poly0.Tmax.value, poly.Tmax.value)
            self.assertEqual(poly0.Tmax.units, poly.Tmax.units)
            self.assertEqual(poly0.comment, poly.comment)
        self.assertEqual(self.thermo.Tmin.value, thermo.Tmin.value)
        self.assertEqual(self.thermo.Tmin.units, thermo.Tmin.units)
        self.assertEqual(self.thermo.Tmax.value, thermo.Tmax.value)
        self.assertEqual(self.thermo.Tmax.units, thermo.Tmax.units)
        self.assertEqual(self.thermo.comment, thermo.comment)
<|end_body_1|>

<|body_start_2|>
        exec('thermo = {0!r}'.format(self.thermo))
        self.assertEqual(len(self.thermo.polynomials), len(thermo.polynomials))
        for poly0, poly in zip(self.thermo.polynomials, thermo.polynomials):
            self.assertEqual(poly0.cm2, poly.cm2)
            self.assertEqual(poly0.cm1, poly.cm1)
            self.assertEqual(poly0.c0, poly.c0)
            self.assertEqual(poly0.c1, poly.c1)
            self.assertEqual(poly0.c2, poly.c2)
            self.assertEqual(poly0.c3, poly.c3)
            self.assertEqual(poly0.c4, poly.c4)
            self.assertEqual(poly0.c5, poly.c5)
            self.assertEqual(poly0.c6, poly.c6)
            self.assertEqual(poly0.Tmin.value, poly.Tmin.value)
            self.assertEqual(poly0.Tmin.units, poly.Tmin.units)
            self.assertEqual(poly0.Tmax.value, poly.Tmax.value)
            self.assertEqual(poly0.Tmax.units, poly.Tmax.units)
            self.assertEqual(poly0.comment, poly.comment)
        self.assertEqual(self.thermo.Tmin.value, thermo.Tmin.value)
        self.assertEqual(self.thermo.Tmin.units, thermo.Tmin.units)
        self.assertEqual(self.thermo.Tmax.value, thermo.Tmax.value)
        self.assertEqual(self.thermo.Tmax.units, thermo.Tmax.units)
        self.assertEqual(self.thermo.comment, thermo.comment)
<|end_body_2|>

Solution:
class TestMultiNASA:
    """Contains unit tests of the MultiNASA class."""

    def setUp(self):
        """A function run before each unit test in this class."""
        nasa0 = NASA(coeffs=[11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0], Tmin=(300.0, 'K'), Tmax=(1000.0, 'K'), comment='This data is completely made up and unphysical')
        nasa1 = NASA(coeffs=[21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0], Tmin=(1000.0, 'K'), Tmax=(6000.0, 'K'), comment='This data is also completely made up and unphysical')
        self.thermo = MultiNASA(polynomials=[nasa0, nasa1], Tmin=(300.0, 'K'), Tmax=(6000.0, 'K'), comment='This data is completely made up and unphysical')

    def testPickle(self):
        """Test that a MultiNASA object can be successfully pickled and unpickled with no loss of information."""
        import cPickle
        thermo = cPickle.loads(cPickle.dumps(self.thermo))
        self.assertEqual(len(self.thermo.polynomials), len(thermo.polynomials))
        for poly0, poly in zip(self.thermo.polynomials, thermo.polynomials):
            self.assertEqual(poly0.cm2, poly.cm2)
            self.assertEqual(poly0.cm1, poly.cm1)
            self.assertEqual(poly0.c0, poly.c0)
            self.assertEqual(poly0.c1, poly.c1)
            self.assertEqual(poly0.c2, poly.c2)
            self.assertEqual(poly0.c3, poly.c3)
            self.assertEqual(poly0.c4, poly.c4)
            self.assertEqual(poly0.c5, poly.c5)
            self.assertEqual(poly0.c6, poly.c6)
            self.assertEqual(poly0.Tmin.value, poly.Tmin.value)
            self.assertEqual(poly0.Tmin.units, poly.Tmin.units)
            self.assertEqual(poly0.Tmax.value, poly.Tmax.value)
            self.assertEqual(poly0.Tmax.units, poly.Tmax.units)
            self.assertEqual(poly0.comment, poly.comment)
        self.assertEqual(self.thermo.Tmin.value, thermo.Tmin.value)
        self.assertEqual(self.thermo.Tmin.units, thermo.Tmin.units)
        self.assertEqual(self.thermo.Tmax.value, thermo.Tmax.value)
        self.assertEqual(self.thermo.Tmax.units, thermo.Tmax.units)
        self.assertEqual(self.thermo.comment, thermo.comment)

    def testOutput(self):
        """Test that we can reconstruct a MultiNASA object from its repr() output with no loss of information."""
        exec('thermo = {0!r}'.format(self.thermo))
        self.assertEqual(len(self.thermo.polynomials), len(thermo.polynomials))
        for poly0, poly in zip(self.thermo.polynomials, thermo.polynomials):
            self.assertEqual(poly0.cm2, poly.cm2)
            self.assertEqual(poly0.cm1, poly.cm1)
            self.assertEqual(poly0.c0, poly.c0)
            self.assertEqual(poly0.c1, poly.c1)
            self.assertEqual(poly0.c2, poly.c2)
            self.assertEqual(poly0.c3, poly.c3)
            self.assertEqual(poly0.c4, poly.c4)
            self.assertEqual(poly0.c5, poly.c5)
            self.assertEqual(poly0.c6, poly.c6)
            self.assertEqual(poly0.Tmin.value, poly.Tmin.value)
            self.assertEqual(poly0.Tmin.units, poly.Tmin.units)
            self.assertEqual(poly0.Tmax.value, poly.Tmax.value)
            self.assertEqual(poly0.Tmax.units, poly.Tmax.units)
            self.assertEqual(poly0.comment, poly.comment)
        self.assertEqual(self.thermo.Tmin.value, thermo.Tmin.value)
        self.assertEqual(self.thermo.Tmin.units, thermo.Tmin.units)
        self.assertEqual(self.thermo.Tmax.value, thermo.Tmax.value)
        self.assertEqual(self.thermo.Tmax.units, thermo.Tmax.units)
        self.assertEqual(self.thermo.comment, thermo.comment)


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

Class description:
A class that represents a gaussian process

Method signatures and docstrings:
- def __init__(self, X_init, Y_init, l=1, sigma_f=1): constructor for class
- def kernel(self, X1, X2): Returns the covariance matrix

Implement the Python class `GaussianProcess` described below.

Class description:
A class that represents a gaussian process

Method signatures and docstrings:
- def __init__(self, X_init, Y_init, l=1, sigma_f=1): constructor for class
- def kernel(self, X1, X2): Returns the covariance matrix

<|skeleton|>
class GaussianProcess:
    """A class that represents a gaussian process"""

    def __init__(self, X_init, Y_init, l=1, sigma_f=1):
        """constructor for class"""
        <|body_0|>

    def kernel(self, X1, X2):
        """Returns the covariance matrix"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.X = X_init
        self.Y = Y_init
        self.l = l
        self.sigma_f = sigma_f
        self.K = self.kernel(self.X, self.X)
<|end_body_0|>

<|body_start_1|>
        first = np.sum(X1 ** 2, 1).reshape(-1, 1)
        second = np.sum(X2 ** 2, 1)
        dist_sq = first + second - 2 * np.dot(X1, X2.T)
        kernel = self.sigma_f ** 2 * np.exp(-0.5 / self.l ** 2 * dist_sq)
        return kernel
<|end_body_1|>

Solution:
class GaussianProcess:
    """A class that represents a gaussian process"""

    def __init__(self, X_init, Y_init, l=1, sigma_f=1):
        """constructor for class"""
        self.X = X_init
        self.Y = Y_init
        self.l = l
        self.sigma_f = sigma_f
        self.K = self.kernel(self.X, self.X)

    def kernel(self, X1, X2):
        """Returns the covariance matrix"""
        first = np.sum(X1 ** 2, 1).reshape(-1, 1)
        second = np.sum(X2 ** 2, 1)
        dist_sq = first + second - 2 * np.dot(X1, X2.T)
        kernel = self.sigma_f ** 2 * np.exp(-0.5 / self.l ** 2 * dist_sq)
        return kernel


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

Class description:
Extract plain text and poll the autoclassifier. In addition to generating classification suggestions, the current implementation of the autoclassifier also generates features (like word counts) and content flags (e.g. possible language issues, line numbers).

Method signatures and docstrings:
- def handle_classifier_exception(self, exc: Exception) -> None: Handle exceptions raised when calling the classifier service.
- def call_classifier(self, content: bytes, trigger: Trigger, emit: Callable) -> None: Send plain text content to the autoclassifier.
- def process_result(self, result: Tuple, trigger: Trigger, emit: Callable) -> None: Process the results returned by the autoclassifier.

Implement the Python class `RunAutoclassifier` described below.

Class description:
Extract plain text and poll the autoclassifier. In addition to generating classification suggestions, the current implementation of the autoclassifier also generates features (like word counts) and content flags (e.g. possible language issues, line numbers).

Method signatures and docstrings:
- def handle_classifier_exception(self, exc: Exception) -> None: Handle exceptions raised when calling the classifier service.
- def call_classifier(self, content: bytes, trigger: Trigger, emit: Callable) -> None: Send plain text content to the autoclassifier.
- def process_result(self, result: Tuple, trigger: Trigger, emit: Callable) -> None: Process the results returned by the autoclassifier.

<|skeleton|>
class RunAutoclassifier:
    """Extract plain text and poll the autoclassifier. In addition to generating classification suggestions, the current implementation of the autoclassifier also generates features (like word counts) and content flags (e.g. possible language issues, line numbers)."""

    def handle_classifier_exception(self, exc: Exception) -> None:
        """Handle exceptions raised when calling the classifier service."""
        <|body_0|>

    def call_classifier(self, content: bytes, trigger: Trigger, emit: Callable) -> None:
        """Send plain text content to the autoclassifier."""
        <|body_1|>

    def process_result(self, result: Tuple, trigger: Trigger, emit: Callable) -> None:
        """Process the results returned by the autoclassifier."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        exc_type = type(exc)
        if exc_type in (exceptions.BadResponse, exceptions.ConnectionFailed):
            raise Recoverable('Encountered %s; try again' % exc) from exc
        elif exc_type is exceptions.RequestFailed:
            if exc.status_code >= 500:
                msg = 'Classifier service choked: %i' % exc.status_code
                raise Recoverable(msg) from exc
            self.fail(exc, 'Unrecoverable exception: %i' % exc.status_code)
        self.fail(exc, 'Unhandled exception')
<|end_body_0|>

<|body_start_1|>
        try:
            self.process_result(Classifier.classify(content), trigger, emit)
        except Exception as exc:
            self.handle_classifier_exception(exc)
<|end_body_1|>

<|body_start_2|>
        suggestions, flags, counts = result
        results = [{'category': suggestion.category, 'probability': suggestion.probability} for suggestion in suggestions]
        emit(AddClassifierResults(creator=self.agent, results=results))
        for flag in flags:
            now = datetime.now(UTC).isoformat()
            comment = 'flag from classification succeeded at %s' % now
            flag_type = self.CLASSIFIER_FLAGS.get(flag.key)
            if flag_type is None:
                continue
            emit(AddContentFlag(creator=self.agent, flag_type=flag_type, flag_data=flag.value, comment=comment))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.CHARACTER_COUNT, feature_value=counts.chars))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.PAGE_COUNT, feature_value=counts.pages))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.STOPWORD_COUNT, feature_value=counts.stops))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.WORD_COUNT, feature_value=counts.words))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.STOPWORD_PERCENT, feature_value=counts.stops / counts.words))
<|end_body_2|>

Solution:
class RunAutoclassifier:
    """Extract plain text and poll the autoclassifier. In addition to generating classification suggestions, the current implementation of the autoclassifier also generates features (like word counts) and content flags (e.g. possible language issues, line numbers)."""

    def handle_classifier_exception(self, exc: Exception) -> None:
        """Handle exceptions raised when calling the classifier service."""
        exc_type = type(exc)
        if exc_type in (exceptions.BadResponse, exceptions.ConnectionFailed):
            raise Recoverable('Encountered %s; try again' % exc) from exc
        elif exc_type is exceptions.RequestFailed:
            if exc.status_code >= 500:
                msg = 'Classifier service choked: %i' % exc.status_code
                raise Recoverable(msg) from exc
            self.fail(exc, 'Unrecoverable exception: %i' % exc.status_code)
        self.fail(exc, 'Unhandled exception')

    def call_classifier(self, content: bytes, trigger: Trigger, emit: Callable) -> None:
        """Send plain text content to the autoclassifier."""
        try:
            self.process_result(Classifier.classify(content), trigger, emit)
        except Exception as exc:
            self.handle_classifier_exception(exc)

    def process_result(self, result: Tuple, trigger: Trigger, emit: Callable) -> None:
        """Process the results returned by the autoclassifier."""
        suggestions, flags, counts = result
        results = [{'category': suggestion.category, 'probability': suggestion.probability} for suggestion in suggestions]
        emit(AddClassifierResults(creator=self.agent, results=results))
        for flag in flags:
            now = datetime.now(UTC).isoformat()
            comment = 'flag from classification succeeded at %s' % now
            flag_type = self.CLASSIFIER_FLAGS.get(flag.key)
            if flag_type is None:
                continue
            emit(AddContentFlag(creator=self.agent, flag_type=flag_type, flag_data=flag.value, comment=comment))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.CHARACTER_COUNT, feature_value=counts.chars))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.PAGE_COUNT, feature_value=counts.pages))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.STOPWORD_COUNT, feature_value=counts.stops))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.WORD_COUNT, feature_value=counts.words))
        emit(AddFeature(creator=self.agent, feature_type=Feature.Type.STOPWORD_PERCENT, feature_value=counts.stops / counts.words))


blocks

Prompt:
Implement the Python class `MobileNetV2Backbone` described below.

Class description:
Backbone of mobilenet v2.

Method signatures and docstrings:
- def __init__(self, load_path=None, data_format='channels_first'): Construct MobileNetV2 class. :param load_path: path for saved model
- def block(self, x, input_filters, output_filters, expansion, stride, training): Mobilenetv2 block.
- def blocks(self, x, expansion, output_filters, repeat, stride, training): Mobilenetv2 blocks.
- def __call__(self, x, training): Do an inference on MobileNetV2. :param x: input tensor :return: output tensor

Implement the Python class `MobileNetV2Backbone` described below.

Class description:
Backbone of mobilenet v2.

Method signatures and docstrings:
- def __init__(self, load_path=None, data_format='channels_first'): Construct MobileNetV2 class. :param load_path: path for saved model
- def block(self, x, input_filters, output_filters, expansion, stride, training): Mobilenetv2 block.
- def blocks(self, x, expansion, output_filters, repeat, stride, training): Mobilenetv2 blocks.
- def __call__(self, x, training): Do an inference on MobileNetV2. :param x: input tensor :return: output tensor

<|skeleton|>
class MobileNetV2Backbone:
    """Backbone of mobilenet v2."""

    def __init__(self, load_path=None, data_format='channels_first'):
        """Construct MobileNetV2 class. :param load_path: path for saved model"""
        <|body_0|>

    def block(self, x, input_filters, output_filters, expansion, stride, training):
        """Mobilenetv2 block."""
        <|body_1|>

    def blocks(self, x, expansion, output_filters, repeat, stride, training):
        """Mobilenetv2 blocks."""
        <|body_2|>

    def __call__(self, x, training):
        """Do an inference on MobileNetV2. :param x: input tensor :return: output tensor"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.data_format = data_format
        self.channel_axis = 1 if data_format == 'channels_first' else 3
        self.load_path = load_path
<|end_body_0|>

<|body_start_1|>
        shortcut = x
        if expansion != 1:
            x = tf.layers.conv2d(x, input_filters * expansion, kernel_size=1, use_bias=False, padding='same', data_format=self.data_format)
            x = tf.layers.batch_normalization(x, axis=self.channel_axis, training=training)
            x = tf.nn.relu6(x)
        x = tf.keras.layers.DepthwiseConv2D(3, strides=stride, padding='same', data_format=self.data_format, use_bias=False)(x)
        x = tf.layers.batch_normalization(x, axis=self.channel_axis, training=training)
        x = tf.nn.relu6(x)
        x = tf.layers.conv2d(x, output_filters, kernel_size=1, use_bias=False, padding='same', data_format=self.data_format)
        x = tf.layers.batch_normalization(x, axis=self.channel_axis, training=training)
        if stride == 1 and input_filters == output_filters:
            return shortcut + x
        else:
            return x
<|end_body_1|>

<|body_start_2|>
        input_filters = int(x.get_shape()[self.channel_axis])
        x = self.block(x, input_filters, output_filters, expansion, stride, training)
        for _ in range(1, repeat):
            input_filters = output_filters
            x = self.block(x, input_filters, output_filters, expansion, 1, training)
        return x
<|end_body_2|>

<|body_start_3|>
        outs = []
        x = tf.layers.conv2d(x, 32, 3, strides=2, use_bias=False, padding='same', data_format=self.data_format)
        x = tf.layers.batch_normalization(x, axis=self.channel_axis, training=training)
        x = tf.nn.relu6(x)
        expansion_list = [1] + [6] * 6
        output_filter_list = [16, 24, 32, 64, 96, 160, 320]
        repeat_list = [1, 2, 3, 4, 3, 3, 1]
        stride_list = [1, 2, 2, 2, 1, 2, 1]
        for i in range(7):
            x = self.blocks(x, expansion_list[i], output_filter_list[i], repeat_list[i], stride_list[i], training)
            if i in [1, 2, 4, 6]:
                outs.append(x)
        return outs
<|end_body_3|>

Solution:
class MobileNetV2Backbone:
    """Backbone of mobilenet v2."""

    def __init__(self, load_path=None, data_format='channels_first'):
        """Construct MobileNetV2 class. :param load_path: path for saved model"""
        self.data_format = data_format
        self.channel_axis = 1 if data_format == 'channels_first' else 3
        self.load_path = load_path

    def block(self, x, input_filters, output_filters, expansion, stride, training):
        """Mobilenetv2 block."""
        shortcut = x
        if expansion != 1:
            x = tf.layers.conv2d(x, input_filters * expansion, kernel_size=1, use_bias=False, padding='same', data_format=self.data_format)
            x = tf.layers.batch_normalization(x, axis=self.channel_axis, training=training)
            x = tf.nn.relu6(x)
        x = tf.keras.layers.DepthwiseConv2D(3, strides=stride, padding='same', data_format=self.data_format, use_bias=False)(x)
        x = tf.layers.batch_normalization(x, axis=self.channel_axis, training=training)
        x = tf.nn.relu6(x)
        x = tf.layers.conv2d(x, output_filters, kernel_size=1, use_bias=False, padding='same', data_format=self.data_format)
        x = tf.layers.batch_normalization(x, axis=self.channel_axis, training=training)
        if stride == 1 and input_filters == output_filters:
            return shortcut + x
        else:
            return x

    def blocks(self, x, expansion, output_filters, repeat, stride, training):
        """Mobilenetv2 blocks."""
        input_filters = int(x.get_shape()[self.channel_axis])
        x = self.block(x, input_filters, output_filters, expansion, stride, training)
        for _ in range(1, repeat):
            input_filters = output_filters
            x = self.block(x, input_filters, output_filters, expansion, 1, training)
        return x

    def __call__(self, x, training):
        """Do an inference on MobileNetV2. :param x: input tensor :return: output tensor"""
        outs = []
        x = tf.layers.conv2d(x, 32, 3, strides=2, use_bias=False, padding='same', data_format=self.data_format)
        x = tf.layers.batch_normalization(x, axis=self.channel_axis, training=training)
        x = tf.nn.relu6(x)
        expansion_list = [1] + [6] * 6
        output_filter_list = [16, 24, 32, 64, 96, 160, 320]
        repeat_list = [1, 2, 3, 4, 3, 3, 1]
        stride_list = [1, 2, 2, 2, 1, 2, 1]
        for i in range(7):
            x = self.blocks(x, expansion_list[i], output_filter_list[i], repeat_list[i], stride_list[i], training)
            if i in [1, 2, 4, 6]:
                outs.append(x)
        return outs


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

Class description:
This class provides HANA specific generation functionality for the Smart Data Access (SDA) scenario. It only creates the artifact for the second SDA HDI container which loads and uses data out of the first container which has been created before this class os called. It also extend the config to cater for specific required config.

Method signatures and docstrings:
- def __init__(self, config): This is main entry point for generating the HANA related artifacts for the SDA scenario Parameters ---------- config : dict Central config object
- def generate_artifacts(self, model_only=True): Generate the artifacts by first building up the required folder structure for artifact storage and then generating the different required files. Be aware that this method only generates the generic files and offloads the generation of artifacts where traversal of base and consumption layer elements is required. Parameters ---------- model_only: boolean In the sda case we are only interested in transferring the model using SDA. This forces the HANA artifact generation to cater only for this scenario. Returns ------- output_path : str Return the output path of the root folder where the related artifacts are stored.
- def _extend_config(self): Extend the config to cater for HANA SDA generation specific config.

Implement the Python class `HanaSDAGenerator` described below.

Class description:
This class provides HANA specific generation functionality for the Smart Data Access (SDA) scenario. It only creates the artifact for the second SDA HDI container which loads and uses data out of the first container which has been created before this class os called. It also extend the config to cater for specific required config.

Method signatures and docstrings:
- def __init__(self, config): This is main entry point for generating the HANA related artifacts for the SDA scenario Parameters ---------- config : dict Central config object
- def generate_artifacts(self, model_only=True): Generate the artifacts by first building up the required folder structure for artifact storage and then generating the different required files. Be aware that this method only generates the generic files and offloads the generation of artifacts where traversal of base and consumption layer elements is required. Parameters ---------- model_only: boolean In the sda case we are only interested in transferring the model using SDA. This forces the HANA artifact generation to cater only for this scenario. Returns ------- output_path : str Return the output path of the root folder where the related artifacts are stored.
- def _extend_config(self): Extend the config to cater for HANA SDA generation specific config.

<|skeleton|>
class HanaSDAGenerator:
    """This class provides HANA specific generation functionality for the Smart Data Access (SDA) scenario. It only creates the artifact for the second SDA HDI container which loads and uses data out of the first container which has been created before this class os called. It also extend the config to cater for specific required config."""

    def __init__(self, config):
        """This is main entry point for generating the HANA related artifacts for the SDA scenario Parameters ---------- config : dict Central config object"""
        <|body_0|>

    def generate_artifacts(self, model_only=True):
        """Generate the artifacts by first building up the required folder structure for artifact storage and then generating the different required files. Be aware that this method only generates the generic files and offloads the generation of artifacts where traversal of base and consumption layer elements is required. Parameters ---------- model_only: boolean In the sda case we are only interested in transferring the model using SDA. This forces the HANA artifact generation to cater only for this scenario. Returns ------- output_path : str Return the output path of the root folder where the related artifacts are stored."""
        <|body_1|>

    def _extend_config(self):
        """Extend the config to cater for HANA SDA generation specific config."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.hana_helper = HanaGeneratorHelper(config)
        self.config = config
        self._extend_config()
<|end_body_0|>

<|body_start_1|>
        self.hana_helper._build_folder_structure(ConfigConstants.PROJECT_TEMPLATE_BASE_SDA_STRUCT, self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_HANA), self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_MODULE))
        yaml_writer = MTAYamlWriter(self.config)
        grant_writer = HDBGrantWriter(self.config)
        synonym_writer = HDBSynonymWriter(self.config)
        role_writer = HDBRoleWriter(self.config)
        consumption_processor = HanaSDAConsumptionProcessor(self.config)
        output_path = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_HANA)
        app_id = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_APPID)
        module_name = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_MODULE_NAME)
        version = self.config.get_entry(ConfigConstants.CONFIG_KEY_VERSION)
        schema = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_SCHEMA)
        grant_service = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_GRANT_SERVICE)
        yaml_writer.generate(output_path, app_id, module_name, version, schema, grant_service)
        remote_source = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_REMOTE_SOURCE)
        grant_writer.generate(self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_GRANTS_PATH), remote_access=True, remote_source=remote_source)
        synonym_writer.generate(self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_SYNONYMS_PATH))
        role_writer.generate(self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_ROLES_PATH), name=self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_MODULE_NAME))
        consumption_processor.generate(model_only)
        return output_path
<|end_body_1|>

<|body_start_2|>
        project_name = self.config.get_entry(ConfigConstants.CONFIG_KEY_PROJECT_NAME)
        sda_module_name = project_name + '_sda'
        sda_app_id = sda_module_name
        sda_schema = '"' + (sda_module_name + '_SCHEMA').upper() + '"'
        sda_output_path_hana = os.path.join(self.config.get_entry(ConfigConstants.CONFIG_KEY_OUTPUT_PATH), ConfigConstants.SDA_HANA_BASE_PATH)
        sda_output_path_module = os.path.join(sda_output_path_hana, sda_module_name)
        sda_output_path_module_src = os.path.join(sda_output_path_module, ConfigConstants.MODULE_SOURCE_PATH)
        sda_grants_path = os.path.join(sda_output_path_module_src, ConfigConstants.GRANTS_SOURCE_PATH)
        sda_synonyms_path = os.path.join(sda_output_path_module_src, ConfigConstants.SYNONYMS_SOURCE_PATH)
        sda_procedures_path = os.path.join(sda_output_path_module_src, ConfigConstants.PROCEDURES_SOURCE_PATH)
        sda_roles_path = os.path.join(sda_output_path_module_src, ConfigConstants.ROLES_SOURCE_PATH)
        sda_virtual_table_path = os.path.join(sda_output_path_module_src, ConfigConstants.VIRTUAL_TABLE_SOURCE_PATH)
        sda_cds_path = os.path.join(sda_output_path_module_src, ConfigConstants.CDS_SOURCE_PATH)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_MODULE_NAME, sda_module_name)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_APPID, sda_app_id)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_HANA, sda_output_path_hana)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_MODULE, sda_output_path_module)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_MODULE_SRC, sda_output_path_module_src)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_GRANTS_PATH, sda_grants_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_SYNONYMS_PATH, sda_synonyms_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_PROCEDURES_PATH, sda_procedures_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_ROLES_PATH, sda_roles_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_VIRTUALTABLE_PATH, sda_virtual_table_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_CDS_PATH, sda_cds_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_SCHEMA, sda_schema)
<|end_body_2|>

Solution:
class HanaSDAGenerator:
    """This class provides HANA specific generation functionality for the Smart Data Access (SDA) scenario. It only creates the artifact for the second SDA HDI container which loads and uses data out of the first container which has been created before this class os called. It also extend the config to cater for specific required config."""

    def __init__(self, config):
        """This is main entry point for generating the HANA related artifacts for the SDA scenario Parameters ---------- config : dict Central config object"""
        self.hana_helper = HanaGeneratorHelper(config)
        self.config = config
        self._extend_config()

    def generate_artifacts(self, model_only=True):
        """Generate the artifacts by first building up the required folder structure for artifact storage and then generating the different required files. Be aware that this method only generates the generic files and offloads the generation of artifacts where traversal of base and consumption layer elements is required. Parameters ---------- model_only: boolean In the sda case we are only interested in transferring the model using SDA. This forces the HANA artifact generation to cater only for this scenario. Returns ------- output_path : str Return the output path of the root folder where the related artifacts are stored."""
        self.hana_helper._build_folder_structure(ConfigConstants.PROJECT_TEMPLATE_BASE_SDA_STRUCT, self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_HANA), self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_MODULE))
        yaml_writer = MTAYamlWriter(self.config)
        grant_writer = HDBGrantWriter(self.config)
        synonym_writer = HDBSynonymWriter(self.config)
        role_writer = HDBRoleWriter(self.config)
        consumption_processor = HanaSDAConsumptionProcessor(self.config)
        output_path = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_HANA)
        app_id = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_APPID)
        module_name = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_MODULE_NAME)
        version = self.config.get_entry(ConfigConstants.CONFIG_KEY_VERSION)
        schema = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_SCHEMA)
        grant_service = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_GRANT_SERVICE)
        yaml_writer.generate(output_path, app_id, module_name, version, schema, grant_service)
        remote_source = self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_REMOTE_SOURCE)
        grant_writer.generate(self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_GRANTS_PATH), remote_access=True, remote_source=remote_source)
        synonym_writer.generate(self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_SYNONYMS_PATH))
        role_writer.generate(self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_ROLES_PATH), name=self.config.get_entry(ConfigConstants.CONFIG_KEY_SDA_MODULE_NAME))
        consumption_processor.generate(model_only)
        return output_path

    def _extend_config(self):
        """Extend the config to cater for HANA SDA generation specific config."""
        project_name = self.config.get_entry(ConfigConstants.CONFIG_KEY_PROJECT_NAME)
        sda_module_name = project_name + '_sda'
        sda_app_id = sda_module_name
        sda_schema = '"' + (sda_module_name + '_SCHEMA').upper() + '"'
        sda_output_path_hana = os.path.join(self.config.get_entry(ConfigConstants.CONFIG_KEY_OUTPUT_PATH), ConfigConstants.SDA_HANA_BASE_PATH)
        sda_output_path_module = os.path.join(sda_output_path_hana, sda_module_name)
        sda_output_path_module_src = os.path.join(sda_output_path_module, ConfigConstants.MODULE_SOURCE_PATH)
        sda_grants_path = os.path.join(sda_output_path_module_src, ConfigConstants.GRANTS_SOURCE_PATH)
        sda_synonyms_path = os.path.join(sda_output_path_module_src, ConfigConstants.SYNONYMS_SOURCE_PATH)
        sda_procedures_path = os.path.join(sda_output_path_module_src, ConfigConstants.PROCEDURES_SOURCE_PATH)
        sda_roles_path = os.path.join(sda_output_path_module_src, ConfigConstants.ROLES_SOURCE_PATH)
        sda_virtual_table_path = os.path.join(sda_output_path_module_src, ConfigConstants.VIRTUAL_TABLE_SOURCE_PATH)
        sda_cds_path = os.path.join(sda_output_path_module_src, ConfigConstants.CDS_SOURCE_PATH)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_MODULE_NAME, sda_module_name)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_APPID, sda_app_id)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_HANA, sda_output_path_hana)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_MODULE, sda_output_path_module)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_OUTPUT_PATH_MODULE_SRC, sda_output_path_module_src)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_GRANTS_PATH, sda_grants_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_SYNONYMS_PATH, sda_synonyms_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_PROCEDURES_PATH, sda_procedures_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_ROLES_PATH, sda_roles_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_VIRTUALTABLE_PATH, sda_virtual_table_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_CDS_PATH, sda_cds_path)
        self.config.add_entry(ConfigConstants.CONFIG_KEY_SDA_SCHEMA, sda_schema)


search

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

Class description:
This viewset is responsible for the ``plant`` endpoint.

Method signatures and docstrings:
- def list(self, request): GET method implementation of listing the latest ``Plant`` models Instead of listing all of the models, we are only going to display the last 10
- def search(self, request): GET method implementation of listing all the ``Plant`` models that meet the requirement

Implement the Python class `PlantViewSet` described below.

Class description:
This viewset is responsible for the ``plant`` endpoint.

Method signatures and docstrings:
- def list(self, request): GET method implementation of listing the latest ``Plant`` models Instead of listing all of the models, we are only going to display the last 10
- def search(self, request): GET method implementation of listing all the ``Plant`` models that meet the requirement

<|skeleton|>
class PlantViewSet:
    """This viewset is responsible for the ``plant`` endpoint."""

    def list(self, request):
        """GET method implementation of listing the latest ``Plant`` models Instead of listing all of the models, we are only going to display the last 10"""
        <|body_0|>

    def search(self, request):
        """GET method implementation of listing all the ``Plant`` models that meet the requirement"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        query = Plant.objects.order_by('-id')[:10]
        serializer = PlantSerializer(query, many=True)
        return Response(serializer.data)
<|end_body_0|>

<|body_start_1|>
        query_dict = dict(request.GET)
        for key, value in query_dict.iteritems():
            if len(value) > 1:
                query_dict.pop('key')
            elif key == 'accepted' or key == 'public':
                if value[0] == 'True':
                    query_dict[key] = True
                else:
                    query_dict[key] = False
            else:
                query_dict[key] = value[0]
        query = Plant.objects.filter(**query_dict)[:10]
        serializer = PlantSerializer(query, many=True)
        return Response(serializer.data)
<|end_body_1|>

Solution:
class PlantViewSet:
    """This viewset is responsible for the ``plant`` endpoint."""

    def list(self, request):
        """GET method implementation of listing the latest ``Plant`` models Instead of listing all of the models, we are only going to display the last 10"""
        query = Plant.objects.order_by('-id')[:10]
        serializer = PlantSerializer(query, many=True)
        return Response(serializer.data)

    def search(self, request):
        """GET method implementation of listing all the ``Plant`` models that meet the requirement"""
        query_dict = dict(request.GET)
        for key, value in query_dict.iteritems():
            if len(value) > 1:
                query_dict.pop('key')
            elif key == 'accepted' or key == 'public':
                if value[0] == 'True':
                    query_dict[key] = True
                else:
                    query_dict[key] = False
            else:
                query_dict[key] = value[0]
        query = Plant.objects.filter(**query_dict)[:10]
        serializer = PlantSerializer(query, many=True)
        return Response(serializer.data)


chart

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

Class description:
Hospital class holds: 1) Dictionary of patients present 2) List of audit times 3) List of beds occupied at each audit time 4) Current total beds occupied 5) Admissions to data Methods: __init__: Set up hospital instance audit: records number of beds occupied build_audit_report: builds audit report at end of run (calculate 5th, 50th and 95th percentile bed occupancy. chart: plot beds occupied over time (at end of run)

Method signatures and docstrings:
- def __init__(self): Constructor method for hospital class" Initialise object with attributes.
- def audit(self, time): Audit method. When called appends current simulation time to audit_time list, and appends current bed count to audit_beds.
- def build_audit_report(self): This method is called at end of run. It creates a pandas DataFrame, transfers audit times and bed counts to the DataFrame, and calculates/stores 5th, 50th and 95th percentiles.
- def chart(self): This method is called at end of run. It plots beds occupancy over the model run, with 5%, 50% and 95% percentiles.

Implement the Python class `Hospital` described below.

Class description:
Hospital class holds: 1) Dictionary of patients present 2) List of audit times 3) List of beds occupied at each audit time 4) Current total beds occupied 5) Admissions to data Methods: __init__: Set up hospital instance audit: records number of beds occupied build_audit_report: builds audit report at end of run (calculate 5th, 50th and 95th percentile bed occupancy. chart: plot beds occupied over time (at end of run)

Method signatures and docstrings:
- def __init__(self): Constructor method for hospital class" Initialise object with attributes.
- def audit(self, time): Audit method. When called appends current simulation time to audit_time list, and appends current bed count to audit_beds.
- def build_audit_report(self): This method is called at end of run. It creates a pandas DataFrame, transfers audit times and bed counts to the DataFrame, and calculates/stores 5th, 50th and 95th percentiles.
- def chart(self): This method is called at end of run. It plots beds occupancy over the model run, with 5%, 50% and 95% percentiles.

<|skeleton|>
class Hospital:
    """Hospital class holds: 1) Dictionary of patients present 2) List of audit times 3) List of beds occupied at each audit time 4) Current total beds occupied 5) Admissions to data Methods: __init__: Set up hospital instance audit: records number of beds occupied build_audit_report: builds audit report at end of run (calculate 5th, 50th and 95th percentile bed occupancy. chart: plot beds occupied over time (at end of run)"""

    def __init__(self):
        """Constructor method for hospital class" Initialise object with attributes."""
        <|body_0|>

    def audit(self, time):
        """Audit method. When called appends current simulation time to audit_time list, and appends current bed count to audit_beds."""
        <|body_1|>

    def build_audit_report(self):
        """This method is called at end of run. It creates a pandas DataFrame, transfers audit times and bed counts to the DataFrame, and calculates/stores 5th, 50th and 95th percentiles."""
        <|body_2|>

    def chart(self):
        """This method is called at end of run. It plots beds occupancy over the model run, with 5%, 50% and 95% percentiles."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.patients = {}
        self.audit_time = []
        self.audit_beds = []
        self.bed_count = 0
        self.admissions = 0
        return
<|end_body_0|>

<|body_start_1|>
        self.audit_time.append(time)
        self.audit_beds.append(self.bed_count)
        return
<|end_body_1|>

<|body_start_2|>
        self.audit_report = pd.DataFrame()
        self.audit_report['Time'] = self.audit_time
        self.audit_report['Occupied_beds'] = self.audit_beds
        self.audit_report['Median_beds'] = self.audit_report['Occupied_beds'].quantile(0.5)
        self.audit_report['Beds_5_percent'] = self.audit_report['Occupied_beds'].quantile(0.05)
        self.audit_report['Beds_95_percent'] = self.audit_report['Occupied_beds'].quantile(0.95)
        return
<|end_body_2|>

<|body_start_3|>
        plt.plot(self.audit_report['Time'], self.audit_report['Occupied_beds'], color='k', marker='o', linestyle='solid', markevery=1, label='Occupied beds')
        plt.plot(self.audit_report['Time'], self.audit_report['Beds_5_percent'], color='0.5', linestyle='dashdot', markevery=1, label='5th percentile')
        plt.plot(self.audit_report['Time'], self.audit_report['Median_beds'], color='0.5', linestyle='dashed', label='Median')
        plt.plot(self.audit_report['Time'], self.audit_report['Beds_95_percent'], color='0.5', linestyle='dashdot', label='95th percentile')
        plt.xlabel('Day')
        plt.ylabel('Occupied beds')
        plt.title('Occupied beds (individual days with 5th, 50th and 95th ' + 'percentiles)')
        plt.show()
        return
<|end_body_3|>

Solution:
class Hospital:
    """Hospital class holds: 1) Dictionary of patients present 2) List of audit times 3) List of beds occupied at each audit time 4) Current total beds occupied 5) Admissions to data Methods: __init__: Set up hospital instance audit: records number of beds occupied build_audit_report: builds audit report at end of run (calculate 5th, 50th and 95th percentile bed occupancy. chart: plot beds occupied over time (at end of run)"""

    def __init__(self):
        """Constructor method for hospital class" Initialise object with attributes."""
        self.patients = {}
        self.audit_time = []
        self.audit_beds = []
        self.bed_count = 0
        self.admissions = 0
        return

    def audit(self, time):
        """Audit method. When called appends current simulation time to audit_time list, and appends current bed count to audit_beds."""
        self.audit_time.append(time)
        self.audit_beds.append(self.bed_count)
        return

    def build_audit_report(self):
        """This method is called at end of run. It creates a pandas DataFrame, transfers audit times and bed counts to the DataFrame, and calculates/stores 5th, 50th and 95th percentiles."""
        self.audit_report = pd.DataFrame()
        self.audit_report['Time'] = self.audit_time
        self.audit_report['Occupied_beds'] = self.audit_beds
        self.audit_report['Median_beds'] = self.audit_report['Occupied_beds'].quantile(0.5)
        self.audit_report['Beds_5_percent'] = self.audit_report['Occupied_beds'].quantile(0.05)
        self.audit_report['Beds_95_percent'] = self.audit_report['Occupied_beds'].quantile(0.95)
        return

    def chart(self):
        """This method is called at end of run. It plots beds occupancy over the model run, with 5%, 50% and 95% percentiles."""
        plt.plot(self.audit_report['Time'], self.audit_report['Occupied_beds'], color='k', marker='o', linestyle='solid', markevery=1, label='Occupied beds')
        plt.plot(self.audit_report['Time'], self.audit_report['Beds_5_percent'], color='0.5', linestyle='dashdot', markevery=1, label='5th percentile')
        plt.plot(self.audit_report['Time'], self.audit_report['Median_beds'], color='0.5', linestyle='dashed', label='Median')
        plt.plot(self.audit_report['Time'], self.audit_report['Beds_95_percent'], color='0.5', linestyle='dashdot', label='95th percentile')
        plt.xlabel('Day')
        plt.ylabel('Occupied beds')
        plt.title('Occupied beds (individual days with 5th, 50th and 95th ' + 'percentiles)')
        plt.show()
        return


post

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

Class description:
Container for the LIST and CREATE endpoints for favorite nodes

Method signatures and docstrings:
- def get(self): Returns all of the user's favorite nodes that he has access to UNTESTED
- def post(self): Endpoint used for favoriting a node for the currently authenticated user

Implement the Python class `ListCreateFavoriteNodes` described below.

Class description:
Container for the LIST and CREATE endpoints for favorite nodes

Method signatures and docstrings:
- def get(self): Returns all of the user's favorite nodes that he has access to UNTESTED
- def post(self): Endpoint used for favoriting a node for the currently authenticated user

<|skeleton|>
class ListCreateFavoriteNodes:
    """Container for the LIST and CREATE endpoints for favorite nodes"""

    def get(self):
        """Returns all of the user's favorite nodes that he has access to UNTESTED"""
        <|body_0|>

    def post(self):
        """Endpoint used for favoriting a node for the currently authenticated user"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        favorite_nodes = UserFavoriteNode.get_favorite_nodes(get_jwt_identity())
        schema = GenericNodeSchema(many=True)
        response = json.loads(schema.dumps(favorite_nodes).data)
        return jsonify_response(response, 200)
<|end_body_0|>

<|body_start_1|>
        user_id = get_jwt_identity()
        data = json.loads(request.data)
        if 'nodeId' not in data or 'nodeType' not in data:
            return jsonify_response({'error': '`nodeId` or `nodeType` not provided.'}, 400)
        vertex_class = self.vertex_types[data['nodeType']]
        vertex = vertex_class.filter(id=data['nodeId'])
        if not vertex:
            return jsonify_response({'error': 'Node does not exist.'}, 404)
        vertex = vertex[0]
        existing_edge = UserFavoriteNode.filter(outv_id=user_id, inv_id=vertex.id, outv_label='user', inv_label=data['nodeType'])
        if existing_edge:
            return jsonify_response({'error': 'Favorite node already exists.'}, 400)
        vertex_roles = vertex.get_user_permissions(user_id)
        direct_vertex_role = vertex_roles['direct_role']
        indirect_vertex_roles = vertex_roles['indirect_roles']
        if direct_vertex_role or [i for i in indirect_vertex_roles if 'lead' in i or 'admin' in i]:
            edge = UserFavoriteNode.create(outv_id=user_id, inv_id=vertex.id, outv_label='user', inv_label=data['nodeType'])
            schema = GenericNodeSchema()
            response = json.loads(schema.dumps(vertex).data)
            return jsonify_response(response, 201)
        return jsonify_response({'error': 'User does not have access to node'}, 403)
<|end_body_1|>

Solution:
class ListCreateFavoriteNodes:
    """Container for the LIST and CREATE endpoints for favorite nodes"""

    def get(self):
        """Returns all of the user's favorite nodes that he has access to UNTESTED"""
        favorite_nodes = UserFavoriteNode.get_favorite_nodes(get_jwt_identity())
        schema = GenericNodeSchema(many=True)
        response = json.loads(schema.dumps(favorite_nodes).data)
        return jsonify_response(response, 200)

    def post(self):
        """Endpoint used for favoriting a node for the currently authenticated user"""
        user_id = get_jwt_identity()
        data = json.loads(request.data)
        if 'nodeId' not in data or 'nodeType' not in data:
            return jsonify_response({'error': '`nodeId` or `nodeType` not provided.'}, 400)
        vertex_class = self.vertex_types[data['nodeType']]
        vertex = vertex_class.filter(id=data['nodeId'])
        if not vertex:
            return jsonify_response({'error': 'Node does not exist.'}, 404)
        vertex = vertex[0]
        existing_edge = UserFavoriteNode.filter(outv_id=user_id, inv_id=vertex.id, outv_label='user', inv_label=data['nodeType'])
        if existing_edge:
            return jsonify_response({'error': 'Favorite node already exists.'}, 400)
        vertex_roles = vertex.get_user_permissions(user_id)
        direct_vertex_role = vertex_roles['direct_role']
        indirect_vertex_roles = vertex_roles['indirect_roles']
        if direct_vertex_role or [i for i in indirect_vertex_roles if 'lead' in i or 'admin' in i]:
            edge = UserFavoriteNode.create(outv_id=user_id, inv_id=vertex.id, outv_label='user', inv_label=data['nodeType'])
            schema = GenericNodeSchema()
            response = json.loads(schema.dumps(vertex).data)
            return jsonify_response(response, 201)
        return jsonify_response({'error': 'User does not have access to node'}, 403)


fit

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

Class description:
Implement the BaseTreeQuantileRegressor class.

Method signatures and docstrings:
- def predict(self, X: np.ndarray, uncertainty: Optional[float]=None, quantiles: Optional[np.ndarray]=None, check_input: bool=True) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: Predict regression value for X. Args: X (array-like or sparse matrix): The input samples, of shape [n_samples, n_features]. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`. uncertainty (float or None, optional): Value ranging from 0 to 1. If None then no prediction intervals will be returned. Defaults to None. quantiles (sequence of floats or None, optional): List of quantiles to output, as an alternative to the `uncertainty` argument, and will not be used if that argument is set. If None then `uncertainty` is used. Defaults to None. check_input (boolean, optional): Allow to bypass several input checking. Don't use this param
- def fit(self, X: np.ndarray, y: np.ndarray, sample_weight: Optional[np.ndarray]=None, check_input: bool=True): Build a decision tree classifier from the training set (X, y). Args: X (array-like or sparse matrix) The training input samples, of shape [n_samples, n_features]. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csc_matrix`. y (array-like): The target values (class labels) as integers or strings, of shape [n_samples] or [n_samples, n_outputs]. sample_weight (array-like or None, optional): Sample weights of shape = [n_samples]. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in

Implement the Python class `BaseTreeQuantileRegressor` described below.

Class description:
Implement the BaseTreeQuantileRegressor class.

Method signatures and docstrings:
- def predict(self, X: np.ndarray, uncertainty: Optional[float]=None, quantiles: Optional[np.ndarray]=None, check_input: bool=True) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: Predict regression value for X. Args: X (array-like or sparse matrix): The input samples, of shape [n_samples, n_features]. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`. uncertainty (float or None, optional): Value ranging from 0 to 1. If None then no prediction intervals will be returned. Defaults to None. quantiles (sequence of floats or None, optional): List of quantiles to output, as an alternative to the `uncertainty` argument, and will not be used if that argument is set. If None then `uncertainty` is used. Defaults to None. check_input (boolean, optional): Allow to bypass several input checking. Don't use this param
- def fit(self, X: np.ndarray, y: np.ndarray, sample_weight: Optional[np.ndarray]=None, check_input: bool=True): Build a decision tree classifier from the training set (X, y). Args: X (array-like or sparse matrix) The training input samples, of shape [n_samples, n_features]. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csc_matrix`. y (array-like): The target values (class labels) as integers or strings, of shape [n_samples] or [n_samples, n_outputs]. sample_weight (array-like or None, optional): Sample weights of shape = [n_samples]. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in

<|skeleton|>
class BaseTreeQuantileRegressor:

    def predict(self, X: np.ndarray, uncertainty: Optional[float]=None, quantiles: Optional[np.ndarray]=None, check_input: bool=True) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
        """Predict regression value for X. Args: X (array-like or sparse matrix): The input samples, of shape [n_samples, n_features]. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`. uncertainty (float or None, optional): Value ranging from 0 to 1. If None then no prediction intervals will be returned. Defaults to None. quantiles (sequence of floats or None, optional): List of quantiles to output, as an alternative to the `uncertainty` argument, and will not be used if that argument is set. If None then `uncertainty` is used. Defaults to None. check_input (boolean, optional): Allow to bypass several input checking. Don't use this param"""
        <|body_0|>

    def fit(self, X: np.ndarray, y: np.ndarray, sample_weight: Optional[np.ndarray]=None, check_input: bool=True):
        """Build a decision tree classifier from the training set (X, y). Args: X (array-like or sparse matrix) The training input samples, of shape [n_samples, n_features]. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csc_matrix`. y (array-like): The target values (class labels) as integers or strings, of shape [n_samples] or [n_samples, n_outputs]. sample_weight (array-like or None, optional): Sample weights of shape = [n_samples]. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        X = check_array(X, dtype=np.float32, accept_sparse='csc')
        preds = super().predict(X, check_input=check_input)
        if uncertainty is not None or quantiles is not None:
            if uncertainty is not None:
                quantiles = [uncertainty / 2, 1 - uncertainty / 2]
            else:
                quantiles = list(quantiles or [])
            X_leaves = self.apply(X)
            unique_leaves = np.unique(X_leaves)
            quantile_vals = np.empty((X.shape[0], len(quantiles)))
            for leaf in unique_leaves:
                for idx, quantile in enumerate(quantiles):
                    X_leaf = X_leaves == leaf
                    y_leaf = self.y_train_[self.y_train_leaves_ == leaf]
                    quantile_vals[X_leaf, idx] = weighted_percentile(y_leaf, quantile)
            return (preds, quantile_vals)
        else:
            return preds
<|end_body_0|>

<|body_start_1|>
        y = np.asarray(y)
        if np.ndim(y) == 2 and y.shape[1] == 1:
            y = np.ravel(y)
        X, y = check_X_y(X, y, accept_sparse='csc', dtype=np.float32, multi_output=False)
        super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
        self.y_train_ = y
        self.y_train_leaves_ = self.tree_.apply(X)
        return self
<|end_body_1|>

Solution:
class BaseTreeQuantileRegressor:
    def predict(self, X: np.ndarray, uncertainty: Optional[float]=None, quantiles: Optional[np.ndarray]=None, check_input: bool=True) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
        """Predict regression value for X. Args: X (array-like or sparse matrix): The input samples, of shape [n_samples, n_features]. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`. uncertainty (float or None, optional): Value ranging from 0 to 1. If None then no prediction intervals will be returned. Defaults to None. quantiles (sequence of floats or None, optional): List of quantiles to output, as an alternative to the `uncertainty` argument, and will not be used if that argument is set. If None then `uncertainty` is used. Defaults to None. check_input (boolean, optional): Allow to bypass several input checking. Don't use this param"""
        X = check_array(X, dtype=np.float32, accept_sparse='csc')
        preds = super().predict(X, check_input=check_input)
        if uncertainty is not None or quantiles is not None:
            if uncertainty is not None:
                quantiles = [uncertainty / 2, 1 - uncertainty / 2]
            else:
                quantiles = list(quantiles or [])
            X_leaves = self.apply(X)
            unique_leaves = np.unique(X_leaves)
            quantile_vals = np.empty((X.shape[0], len(quantiles)))
            for leaf in unique_leaves:
                for idx, quantile in enumerate(quantiles):
                    X_leaf = X_leaves == leaf
                    y_leaf = self.y_train_[self.y_train_leaves_ == leaf]
                    quantile_vals[X_leaf, idx] = weighted_percentile(y_leaf, quantile)
            return (preds, quantile_vals)
        else:
            return preds

    def fit(self, X: np.ndarray, y: np.ndarray, sample_weight: Optional[np.ndarray]=None, check_input: bool=True):
        """Build a decision tree classifier from the training set (X, y). Args: X (array-like or sparse matrix) The training input samples, of shape [n_samples, n_features]. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csc_matrix`. y (array-like): The target values (class labels) as integers or strings, of shape [n_samples] or [n_samples, n_outputs]. sample_weight (array-like or None, optional): Sample weights of shape = [n_samples]. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in"""
        y = np.asarray(y)
        if np.ndim(y) == 2 and y.shape[1] == 1:
            y = np.ravel(y)
        X, y = check_X_y(X, y, accept_sparse='csc', dtype=np.float32, multi_output=False)
        super().fit(X, y, sample_weight=sample_weight, check_input=check_input)
        self.y_train_ = y
        self.y_train_leaves_ = self.tree_.apply(X)
        return self