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

Class description:
Implementation of the 'GetJobRunInfoResult' model. TODO: type description here. Attributes: bytes_transferred (long|int): Specifies bytes transferred in the run. end_time_usecs (long|int): Specifies the end time of the run. failure_entities (long|int): Specifies the number of failed objects in the run. job_id (string): Specifies the job id. job_run_id (string): Specifies the job run id. job_type (string): Specifies the job type, protection, replication, archival, apollo, indexing etc. sla_violated (bool): Specifies if the sla was violated the run. start_time_usecs (long|int): Specifies the start time of the run. status (long|int): Specifies status of the run success_entities (long|int): Spec

Method signatures and docstrings:
- def __init__(self, bytes_transferred=None, end_time_usecs=None, failure_entities=None, job_id=None, job_run_id=None, job_type=None, sla_violated=None, start_time_usecs=None, status=None, success_entities=None, total_entities=None): Constructor for the GetJobRunInfoResult class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `GetJobRunInfoResult` described below.

Class description:
Implementation of the 'GetJobRunInfoResult' model. TODO: type description here. Attributes: bytes_transferred (long|int): Specifies bytes transferred in the run. end_time_usecs (long|int): Specifies the end time of the run. failure_entities (long|int): Specifies the number of failed objects in the run. job_id (string): Specifies the job id. job_run_id (string): Specifies the job run id. job_type (string): Specifies the job type, protection, replication, archival, apollo, indexing etc. sla_violated (bool): Specifies if the sla was violated the run. start_time_usecs (long|int): Specifies the start time of the run. status (long|int): Specifies status of the run success_entities (long|int): Spec

Method signatures and docstrings:
- def __init__(self, bytes_transferred=None, end_time_usecs=None, failure_entities=None, job_id=None, job_run_id=None, job_type=None, sla_violated=None, start_time_usecs=None, status=None, success_entities=None, total_entities=None): Constructor for the GetJobRunInfoResult class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class GetJobRunInfoResult:
    """Implementation of the 'GetJobRunInfoResult' model. TODO: type description here. Attributes: bytes_transferred (long|int): Specifies bytes transferred in the run. end_time_usecs (long|int): Specifies the end time of the run. failure_entities (long|int): Specifies the number of failed objects in the run. job_id (string): Specifies the job id. job_run_id (string): Specifies the job run id. job_type (string): Specifies the job type, protection, replication, archival, apollo, indexing etc. sla_violated (bool): Specifies if the sla was violated the run. start_time_usecs (long|int): Specifies the start time of the run. status (long|int): Specifies status of the run success_entities (long|int): Spec"""

    def __init__(self, bytes_transferred=None, end_time_usecs=None, failure_entities=None, job_id=None, job_run_id=None, job_type=None, sla_violated=None, start_time_usecs=None, status=None, success_entities=None, total_entities=None):
        """Constructor for the GetJobRunInfoResult class"""
        <|body_0|>

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

<|end_skeleton|>

<|body_start_0|>
        self.bytes_transferred = bytes_transferred
        self.end_time_usecs = end_time_usecs
        self.failure_entities = failure_entities
        self.job_id = job_id
        self.job_run_id = job_run_id
        self.job_type = job_type
        self.sla_violated = sla_violated
        self.start_time_usecs = start_time_usecs
        self.status = status
        self.success_entities = success_entities
        self.total_entities = total_entities
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        bytes_transferred = dictionary.get('bytesTransferred')
        end_time_usecs = dictionary.get('endTimeUsecs')
        failure_entities = dictionary.get('failureEntities')
        job_id = dictionary.get('jobId')
        job_run_id = dictionary.get('jobRunId')
        job_type = dictionary.get('jobType')
        sla_violated = dictionary.get('slaViolated')
        start_time_usecs = dictionary.get('startTimeUsecs')
        status = dictionary.get('status')
        success_entities = dictionary.get('successEntities')
        total_entities = dictionary.get('totalEntities')
        return cls(bytes_transferred, end_time_usecs, failure_entities, job_id, job_run_id, job_type, sla_violated, start_time_usecs, status, success_entities, total_entities)
<|end_body_1|>

Solution:
class GetJobRunInfoResult:
    """Implementation of the 'GetJobRunInfoResult' model. TODO: type description here. Attributes: bytes_transferred (long|int): Specifies bytes transferred in the run. end_time_usecs (long|int): Specifies the end time of the run. failure_entities (long|int): Specifies the number of failed objects in the run. job_id (string): Specifies the job id. job_run_id (string): Specifies the job run id. job_type (string): Specifies the job type, protection, replication, archival, apollo, indexing etc. sla_violated (bool): Specifies if the sla was violated the run. start_time_usecs (long|int): Specifies the start time of the run. status (long|int): Specifies status of the run success_entities (long|int): Spec"""

    def __init__(self, bytes_transferred=None, end_time_usecs=None, failure_entities=None, job_id=None, job_run_id=None, job_type=None, sla_violated=None, start_time_usecs=None, status=None, success_entities=None, total_entities=None):
        """Constructor for the GetJobRunInfoResult class"""
        self.bytes_transferred = bytes_transferred
        self.end_time_usecs = end_time_usecs
        self.failure_entities = failure_entities
        self.job_id = job_id
        self.job_run_id = job_run_id
        self.job_type = job_type
        self.sla_violated = sla_violated
        self.start_time_usecs = start_time_usecs
        self.status = status
        self.success_entities = success_entities
        self.total_entities = total_entities

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        bytes_transferred = dictionary.get('bytesTransferred')
        end_time_usecs = dictionary.get('endTimeUsecs')
        failure_entities = dictionary.get('failureEntities')
        job_id = dictionary.get('jobId')
        job_run_id = dictionary.get('jobRunId')
        job_type = dictionary.get('jobType')
        sla_violated = dictionary.get('slaViolated')
        start_time_usecs = dictionary.get('startTimeUsecs')
        status = dictionary.get('status')
        success_entities = dictionary.get('successEntities')
        total_entities = dictionary.get('totalEntities')
        return cls(bytes_transferred, end_time_usecs, failure_entities, job_id, job_run_id, job_type, sla_violated, start_time_usecs, status, success_entities, total_entities)


deserialize

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

Class description:
Implement the Codec class.

Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: TreeNode

Implement the Python class `Codec` described below.

Class description:
Implement the Codec class.

Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: TreeNode

<|skeleton|>
class Codec:

    def serialize(self, root):
        """Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
        <|body_0|>

    def deserialize(self, data):
        """Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        ret = []
        queue = collections.deque([root])
        while queue:
            node = queue.popleft()
            if not node:
                ret.append('null')
            else:
                ret.append(str(node.val))
                queue.append(node.left)
                queue.append(node.right)
        while ret and ret[-1] == 'null':
            ret.pop()
        return ','.join(ret)
<|end_body_0|>

<|body_start_1|>
        if data == '':
            return None
        data = data.split(',')
        root = TreeNode(data[0])
        que = collections.deque([root])
        i = 1
        while que and i < len(data):
            node = que.popleft()
            if data[i] != 'null':
                node.left = TreeNode(data[i])
                que.append(node.left)
            i += 1
            if i < len(data) and data[i] != 'null':
                node.right = TreeNode(data[i])
                que.append(node.right)
            i += 1
        return root
<|end_body_1|>

Solution:
class Codec:
    def serialize(self, root):
        """Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
        ret = []
        queue = collections.deque([root])
        while queue:
            node = queue.popleft()
            if not node:
                ret.append('null')
            else:
                ret.append(str(node.val))
                queue.append(node.left)
                queue.append(node.right)
        while ret and ret[-1] == 'null':
            ret.pop()
        return ','.join(ret)

    def deserialize(self, data):
        """Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
        if data == '':
            return None
        data = data.split(',')
        root = TreeNode(data[0])
        que = collections.deque([root])
        i = 1
        while que and i < len(data):
            node = que.popleft()
            if data[i] != 'null':
                node.left = TreeNode(data[i])
                que.append(node.left)
            i += 1
            if i < len(data) and data[i] != 'null':
                node.right = TreeNode(data[i])
                que.append(node.right)
            i += 1
        return root


create

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

Class description:
Manage roles that users can have in organizations in the Power Reg system

Method signatures and docstrings:
- def __init__(self): constructor
- def create(self, auth_token, name): Create a new OrgRole @param name name of the OrgRole @return a reference to the newly created OrgRole

Implement the Python class `OrgRoleManager` described below.

Class description:
Manage roles that users can have in organizations in the Power Reg system

Method signatures and docstrings:
- def __init__(self): constructor
- def create(self, auth_token, name): Create a new OrgRole @param name name of the OrgRole @return a reference to the newly created OrgRole

<|skeleton|>
class OrgRoleManager:
    """Manage roles that users can have in organizations in the Power Reg system"""

    def __init__(self):
        """constructor"""
        <|body_0|>

    def create(self, auth_token, name):
        """Create a new OrgRole @param name name of the OrgRole @return a reference to the newly created OrgRole"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        ObjectManager.__init__(self)
        self.getters.update({'name': 'get_general', 'organizations': 'get_many_to_many', 'users': 'get_many_to_many', 'user_org_roles': 'get_many_to_one'})
        self.setters.update({'name': 'set_general', 'organizations': 'set_many', 'users': 'set_many'})
        self.my_django_model = facade.models.OrgRole
<|end_body_0|>

<|body_start_1|>
        o = self.my_django_model.objects.create(name=name)
        self.authorizer.check_create_permissions(auth_token, o)
        return o
<|end_body_1|>

Solution:
class OrgRoleManager:
    """Manage roles that users can have in organizations in the Power Reg system"""

    def __init__(self):
        """constructor"""
        ObjectManager.__init__(self)
        self.getters.update({'name': 'get_general', 'organizations': 'get_many_to_many', 'users': 'get_many_to_many', 'user_org_roles': 'get_many_to_one'})
        self.setters.update({'name': 'set_general', 'organizations': 'set_many', 'users': 'set_many'})
        self.my_django_model = facade.models.OrgRole

    def create(self, auth_token, name):
        """Create a new OrgRole @param name name of the OrgRole @return a reference to the newly created OrgRole"""
        o = self.my_django_model.objects.create(name=name)
        self.authorizer.check_create_permissions(auth_token, o)
        return o


start

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

Class description:
Implement the Systemd class.

Method signatures and docstrings:
- def enable(self): Sets monasca-agent to start on boot. Generally this requires running as super user
- def start(self, restart=True): Starts monasca-agent. If the agent is running and restart is True, restart
- def stop(self): Stops monasca-agent.
- def is_enabled(self): Returns True if monasca-agent is setup to start on boot, false otherwise.

Implement the Python class `Systemd` described below.

Class description:
Implement the Systemd class.

Method signatures and docstrings:
- def enable(self): Sets monasca-agent to start on boot. Generally this requires running as super user
- def start(self, restart=True): Starts monasca-agent. If the agent is running and restart is True, restart
- def stop(self): Stops monasca-agent.
- def is_enabled(self): Returns True if monasca-agent is setup to start on boot, false otherwise.

<|skeleton|>
class Systemd:

    def enable(self):
        """Sets monasca-agent to start on boot. Generally this requires running as super user"""
        <|body_0|>

    def start(self, restart=True):
        """Starts monasca-agent. If the agent is running and restart is True, restart"""
        <|body_1|>

    def stop(self):
        """Stops monasca-agent."""
        <|body_2|>

    def is_enabled(self):
        """Returns True if monasca-agent is setup to start on boot, false otherwise."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        LinuxInit.enable(self)
        init_path = '/etc/systemd/system/{0}.service'.format(self.name)
        with open(os.path.join(self.template_dir, 'monasca-agent.service.template'), 'r') as template:
            with open(init_path, 'w') as service_script:
                service_script.write(template.read().format(prefix=self.prefix_dir, monasca_user=self.username, config_dir=self.config_dir))
        os.chown(init_path, 0, 0)
        os.chmod(init_path, 420)
        subprocess.check_call(['systemctl', 'daemon-reload'])
        subprocess.check_call(['systemctl', 'enable', '{0}.service'.format(self.name)])
        log.info('Enabled {0} service via systemd'.format(self.name))
<|end_body_0|>

<|body_start_1|>
        LinuxInit.start(self)
        log.info('Starting {0} service via systemd'.format(self.name))
        if restart:
            subprocess.check_call(['systemctl', 'restart', '{0}.service'.format(self.name)])
        else:
            subprocess.check_call(['systemctl', 'start', '{0}.service'.format(self.name)])
        return True
<|end_body_1|>

<|body_start_2|>
        LinuxInit.stop(self)
        log.info('Stopping {0} service'.format(self.name))
        subprocess.check_call(['systemctl', 'stop', '{0}.service'.format(self.name)])
        return True
<|end_body_2|>

<|body_start_3|>
        try:
            subprocess.check_output(['systemctl', 'is-enabled', '{0}.service'.format(self.name)])
        except subprocess.CalledProcessError:
            return False
        return True
<|end_body_3|>

Solution:
class Systemd:
    def enable(self):
        """Sets monasca-agent to start on boot. Generally this requires running as super user"""
        LinuxInit.enable(self)
        init_path = '/etc/systemd/system/{0}.service'.format(self.name)
        with open(os.path.join(self.template_dir, 'monasca-agent.service.template'), 'r') as template:
            with open(init_path, 'w') as service_script:
                service_script.write(template.read().format(prefix=self.prefix_dir, monasca_user=self.username, config_dir=self.config_dir))
        os.chown(init_path, 0, 0)
        os.chmod(init_path, 420)
        subprocess.check_call(['systemctl', 'daemon-reload'])
        subprocess.check_call(['systemctl', 'enable', '{0}.service'.format(self.name)])
        log.info('Enabled {0} service via systemd'.format(self.name))

    def start(self, restart=True):
        """Starts monasca-agent. If the agent is running and restart is True, restart"""
        LinuxInit.start(self)
        log.info('Starting {0} service via systemd'.format(self.name))
        if restart:
            subprocess.check_call(['systemctl', 'restart', '{0}.service'.format(self.name)])
        else:
            subprocess.check_call(['systemctl', 'start', '{0}.service'.format(self.name)])
        return True

    def stop(self):
        """Stops monasca-agent."""
        LinuxInit.stop(self)
        log.info('Stopping {0} service'.format(self.name))
        subprocess.check_call(['systemctl', 'stop', '{0}.service'.format(self.name)])
        return True

    def is_enabled(self):
        """Returns True if monasca-agent is setup to start on boot, false otherwise."""
        try:
            subprocess.check_output(['systemctl', 'is-enabled', '{0}.service'.format(self.name)])
        except subprocess.CalledProcessError:
            return False
        return True


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

Class description:
定义主机信息识别服务

Method signatures and docstrings:
- def Location(self, request, context): 定义IP信息查询方法
- def Alive(self, request, context): 定义主机存活方法
- def Detail(self, request, context): 定义服务识别方法

Implement the Python class `HostServicer` described below.

Class description:
定义主机信息识别服务

Method signatures and docstrings:
- def Location(self, request, context): 定义IP信息查询方法
- def Alive(self, request, context): 定义主机存活方法
- def Detail(self, request, context): 定义服务识别方法

<|skeleton|>
class HostServicer:
    """定义主机信息识别服务"""

    def Location(self, request, context):
        """定义IP信息查询方法"""
        <|body_0|>

    def Alive(self, request, context):
        """定义主机存活方法"""
        <|body_1|>

    def Detail(self, request, context):
        """定义服务识别方法"""
        <|body_2|>

<|end_skeleton|>

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

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

<|body_start_2|>
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')
<|end_body_2|>

Solution:
class HostServicer:
    """定义主机信息识别服务"""

    def Location(self, request, context):
        """定义IP信息查询方法"""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def Alive(self, request, context):
        """定义主机存活方法"""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def Detail(self, request, context):
        """定义服务识别方法"""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')


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

Class description:
Implementation of the 'Externally Sourced Route Start Stop Details' model. TODO: type model description here. NOTE: This class inherits from 'ExternallySourcedRouteStopDetails'. Attributes: start_name (string): a name for the start location start_address (string): an optional street address of the start start_location (string): the location of the start route_add_instructions (string): an optional comment with details about the route

Method signatures and docstrings:
- def __init__(self, start_name=None, start_location=None, stop_name=None, stop_location=None, start_address=None, route_add_instructions=None, stop_address=None, stop_deadline=None): Constructor for the ExternallySourcedRouteStartStopDetails class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `ExternallySourcedRouteStartStopDetails` described below.

Class description:
Implementation of the 'Externally Sourced Route Start Stop Details' model. TODO: type model description here. NOTE: This class inherits from 'ExternallySourcedRouteStopDetails'. Attributes: start_name (string): a name for the start location start_address (string): an optional street address of the start start_location (string): the location of the start route_add_instructions (string): an optional comment with details about the route

Method signatures and docstrings:
- def __init__(self, start_name=None, start_location=None, stop_name=None, stop_location=None, start_address=None, route_add_instructions=None, stop_address=None, stop_deadline=None): Constructor for the ExternallySourcedRouteStartStopDetails class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class ExternallySourcedRouteStartStopDetails:
    """Implementation of the 'Externally Sourced Route Start Stop Details' model. TODO: type model description here. NOTE: This class inherits from 'ExternallySourcedRouteStopDetails'. Attributes: start_name (string): a name for the start location start_address (string): an optional street address of the start start_location (string): the location of the start route_add_instructions (string): an optional comment with details about the route"""

    def __init__(self, start_name=None, start_location=None, stop_name=None, stop_location=None, start_address=None, route_add_instructions=None, stop_address=None, stop_deadline=None):
        """Constructor for the ExternallySourcedRouteStartStopDetails class"""
        <|body_0|>

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

<|end_skeleton|>

<|body_start_0|>
        self.start_name = start_name
        self.start_address = start_address
        self.start_location = start_location
        self.route_add_instructions = route_add_instructions
        super(ExternallySourcedRouteStartStopDetails, self).__init__(stop_name, stop_location, stop_address, stop_deadline)
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        start_name = dictionary.get('startName')
        start_location = dictionary.get('startLocation')
        stop_name = dictionary.get('stopName')
        stop_location = dictionary.get('stopLocation')
        start_address = dictionary.get('startAddress')
        route_add_instructions = dictionary.get('routeAddInstructions')
        stop_address = dictionary.get('stopAddress')
        stop_deadline = dictionary.get('stopDeadline')
        return cls(start_name, start_location, stop_name, stop_location, start_address, route_add_instructions, stop_address, stop_deadline)
<|end_body_1|>

Solution:
class ExternallySourcedRouteStartStopDetails:
    """Implementation of the 'Externally Sourced Route Start Stop Details' model. TODO: type model description here. NOTE: This class inherits from 'ExternallySourcedRouteStopDetails'. Attributes: start_name (string): a name for the start location start_address (string): an optional street address of the start start_location (string): the location of the start route_add_instructions (string): an optional comment with details about the route"""

    def __init__(self, start_name=None, start_location=None, stop_name=None, stop_location=None, start_address=None, route_add_instructions=None, stop_address=None, stop_deadline=None):
        """Constructor for the ExternallySourcedRouteStartStopDetails class"""
        self.start_name = start_name
        self.start_address = start_address
        self.start_location = start_location
        self.route_add_instructions = route_add_instructions
        super(ExternallySourcedRouteStartStopDetails, self).__init__(stop_name, stop_location, stop_address, stop_deadline)

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        start_name = dictionary.get('startName')
        start_location = dictionary.get('startLocation')
        stop_name = dictionary.get('stopName')
        stop_location = dictionary.get('stopLocation')
        start_address = dictionary.get('startAddress')
        route_add_instructions = dictionary.get('routeAddInstructions')
        stop_address = dictionary.get('stopAddress')
        stop_deadline = dictionary.get('stopDeadline')
        return cls(start_name, start_location, stop_name, stop_location, start_address, route_add_instructions, stop_address, stop_deadline)


testDiscreteGaussian

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

Class description:
Implement the utilsTest class.

Method signatures and docstrings:
- def testDiscreteGaussian(self): Tests versus values computed using `https://keisan.casio.com/exec/system/1180573473`
- def testDiscreteGaussianBadSize(self): Tests versus values computed using `https://keisan.casio.com/exec/system/1180573473`

Implement the Python class `utilsTest` described below.

Class description:
Implement the utilsTest class.

Method signatures and docstrings:
- def testDiscreteGaussian(self): Tests versus values computed using `https://keisan.casio.com/exec/system/1180573473`
- def testDiscreteGaussianBadSize(self): Tests versus values computed using `https://keisan.casio.com/exec/system/1180573473`

<|skeleton|>
class utilsTest:

    def testDiscreteGaussian(self):
        """Tests versus values computed using `https://keisan.casio.com/exec/system/1180573473`"""
        <|body_0|>

    def testDiscreteGaussianBadSize(self):
        """Tests versus values computed using `https://keisan.casio.com/exec/system/1180573473`"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        sigma = 1.0
        size = 5
        real_values = 0.3678 * np.array([0.1357, 0.565, 1.266, 0.565, 0.1357])
        for real, computed in zip(real_values, utils.discrete_gaussian(size, sigma)):
            self.assertAlmostEqual(real, computed, 3)
<|end_body_0|>

<|body_start_1|>
        sigma = 1.0
        size = 6
        with self.assertRaisesRegex(ValueError, '`size` must be odd.'):
            utils.discrete_gaussian(size, sigma)
<|end_body_1|>

Solution:
class utilsTest:
    def testDiscreteGaussian(self):
        """Tests versus values computed using `https://keisan.casio.com/exec/system/1180573473`"""
        sigma = 1.0
        size = 5
        real_values = 0.3678 * np.array([0.1357, 0.565, 1.266, 0.565, 0.1357])
        for real, computed in zip(real_values, utils.discrete_gaussian(size, sigma)):
            self.assertAlmostEqual(real, computed, 3)

    def testDiscreteGaussianBadSize(self):
        """Tests versus values computed using `https://keisan.casio.com/exec/system/1180573473`"""
        sigma = 1.0
        size = 6
        with self.assertRaisesRegex(ValueError, '`size` must be odd.'):
            utils.discrete_gaussian(size, sigma)


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

Class description:
Generate a VolatileOrganicCompoundsSensor accessory as VOCs sensor. Sensor entity must return VOC in µg/m3.

Method signatures and docstrings:
- def create_services(self): Override the init function for VOC Sensor.
- def async_update_state(self, new_state): Update accessory after state change.

Implement the Python class `VolatileOrganicCompoundsSensor` described below.

Class description:
Generate a VolatileOrganicCompoundsSensor accessory as VOCs sensor. Sensor entity must return VOC in µg/m3.

Method signatures and docstrings:
- def create_services(self): Override the init function for VOC Sensor.
- def async_update_state(self, new_state): Update accessory after state change.

<|skeleton|>
class VolatileOrganicCompoundsSensor:
    """Generate a VolatileOrganicCompoundsSensor accessory as VOCs sensor. Sensor entity must return VOC in µg/m3."""

    def create_services(self):
        """Override the init function for VOC Sensor."""
        <|body_0|>

    def async_update_state(self, new_state):
        """Update accessory after state change."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        serv_air_quality: Service = self.add_preload_service(SERV_AIR_QUALITY_SENSOR, [CHAR_VOC_DENSITY])
        self.char_quality = serv_air_quality.configure_char(CHAR_AIR_QUALITY, value=0)
        self.char_density = serv_air_quality.configure_char(CHAR_VOC_DENSITY, value=0, properties={PROP_MIN_VALUE: 0, PROP_MAX_VALUE: 5000})
<|end_body_0|>

<|body_start_1|>
        density = convert_to_float(new_state.state)
        if density is None:
            return
        if self.char_density.value != density:
            self.char_density.set_value(density)
            _LOGGER.debug('%s: Set density to %d', self.entity_id, density)
        air_quality = density_to_air_quality_voc(density)
        if self.char_quality.value != air_quality:
            self.char_quality.set_value(air_quality)
            _LOGGER.debug('%s: Set air_quality to %d', self.entity_id, air_quality)
<|end_body_1|>

Solution:
class VolatileOrganicCompoundsSensor:
    """Generate a VolatileOrganicCompoundsSensor accessory as VOCs sensor. Sensor entity must return VOC in µg/m3."""

    def create_services(self):
        """Override the init function for VOC Sensor."""
        serv_air_quality: Service = self.add_preload_service(SERV_AIR_QUALITY_SENSOR, [CHAR_VOC_DENSITY])
        self.char_quality = serv_air_quality.configure_char(CHAR_AIR_QUALITY, value=0)
        self.char_density = serv_air_quality.configure_char(CHAR_VOC_DENSITY, value=0, properties={PROP_MIN_VALUE: 0, PROP_MAX_VALUE: 5000})

    def async_update_state(self, new_state):
        """Update accessory after state change."""
        density = convert_to_float(new_state.state)
        if density is None:
            return
        if self.char_density.value != density:
            self.char_density.set_value(density)
            _LOGGER.debug('%s: Set density to %d', self.entity_id, density)
        air_quality = density_to_air_quality_voc(density)
        if self.char_quality.value != air_quality:
            self.char_quality.set_value(air_quality)
            _LOGGER.debug('%s: Set air_quality to %d', self.entity_id, air_quality)


call

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

Class description:
The Vanilla PointNet feature encoder. Consists of five conv2 layers with (64,64,64,128,1024) output channels. Note: PointNetConv2Layer are used instead of tf.keras.layers.Conv2D. https://github.com/charlesq34/pointnet/blob/master/models/pointnet_cls_basic.py

Method signatures and docstrings:
- def __init__(self, momentum: float=0.5): Constructs a VanillaEncoder keras layer. Args: momentum: the momentum used for the batch normalization layer.
- def call(self, inputs: tf.Tensor, training: Optional[bool]=None) -> tf.Tensor: Computes the PointNet features. Args: inputs: a dense tensor of size `[B,N,D]`. training: flag to control batch normalization update statistics. Returns: Tensor with shape `[B, N, C=1024]`

Implement the Python class `VanillaEncoder` described below.

Class description:
The Vanilla PointNet feature encoder. Consists of five conv2 layers with (64,64,64,128,1024) output channels. Note: PointNetConv2Layer are used instead of tf.keras.layers.Conv2D. https://github.com/charlesq34/pointnet/blob/master/models/pointnet_cls_basic.py

Method signatures and docstrings:
- def __init__(self, momentum: float=0.5): Constructs a VanillaEncoder keras layer. Args: momentum: the momentum used for the batch normalization layer.
- def call(self, inputs: tf.Tensor, training: Optional[bool]=None) -> tf.Tensor: Computes the PointNet features. Args: inputs: a dense tensor of size `[B,N,D]`. training: flag to control batch normalization update statistics. Returns: Tensor with shape `[B, N, C=1024]`

<|skeleton|>
class VanillaEncoder:
    """The Vanilla PointNet feature encoder. Consists of five conv2 layers with (64,64,64,128,1024) output channels. Note: PointNetConv2Layer are used instead of tf.keras.layers.Conv2D. https://github.com/charlesq34/pointnet/blob/master/models/pointnet_cls_basic.py"""

    def __init__(self, momentum: float=0.5):
        """Constructs a VanillaEncoder keras layer. Args: momentum: the momentum used for the batch normalization layer."""
        <|body_0|>

    def call(self, inputs: tf.Tensor, training: Optional[bool]=None) -> tf.Tensor:
        """Computes the PointNet features. Args: inputs: a dense tensor of size `[B,N,D]`. training: flag to control batch normalization update statistics. Returns: Tensor with shape `[B, N, C=1024]`"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(VanillaEncoder, self).__init__()
        self.conv1 = PointNetConv2Layer(64, momentum)
        self.conv2 = PointNetConv2Layer(64, momentum)
        self.conv3 = PointNetConv2Layer(64, momentum)
        self.conv4 = PointNetConv2Layer(128, momentum)
        self.conv5 = PointNetConv2Layer(1024, momentum)
<|end_body_0|>

<|body_start_1|>
        x = tf.expand_dims(inputs, axis=2)
        x = self.conv1(x, training)
        x = self.conv2(x, training)
        x = self.conv3(x, training)
        x = self.conv4(x, training)
        x = self.conv5(x, training)
        x = tf.math.reduce_max(input_tensor=x, axis=1)
        return tf.squeeze(x)
<|end_body_1|>

Solution:
class VanillaEncoder:
    """The Vanilla PointNet feature encoder. Consists of five conv2 layers with (64,64,64,128,1024) output channels. Note: PointNetConv2Layer are used instead of tf.keras.layers.Conv2D. https://github.com/charlesq34/pointnet/blob/master/models/pointnet_cls_basic.py"""

    def __init__(self, momentum: float=0.5):
        """Constructs a VanillaEncoder keras layer. Args: momentum: the momentum used for the batch normalization layer."""
        super(VanillaEncoder, self).__init__()
        self.conv1 = PointNetConv2Layer(64, momentum)
        self.conv2 = PointNetConv2Layer(64, momentum)
        self.conv3 = PointNetConv2Layer(64, momentum)
        self.conv4 = PointNetConv2Layer(128, momentum)
        self.conv5 = PointNetConv2Layer(1024, momentum)

    def call(self, inputs: tf.Tensor, training: Optional[bool]=None) -> tf.Tensor:
        """Computes the PointNet features. Args: inputs: a dense tensor of size `[B,N,D]`. training: flag to control batch normalization update statistics. Returns: Tensor with shape `[B, N, C=1024]`"""
        x = tf.expand_dims(inputs, axis=2)
        x = self.conv1(x, training)
        x = self.conv2(x, training)
        x = self.conv3(x, training)
        x = self.conv4(x, training)
        x = self.conv5(x, training)
        x = tf.math.reduce_max(input_tensor=x, axis=1)
        return tf.squeeze(x)


unfollow

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

Class description:
Implement the Twitter class.

Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def postTweet(self, userId: int, tweetId: int) -> None: Compose a new tweet.
- def getNewsFeed(self, userId: int) -> List[int]: Retrieve the 10 most recent tweet ids in the user's news feed. Each item in the news feed must be posted by users who the user followed or by the user herself. Tweets must be ordered from most recent to least recent.
- def follow(self, followerId: int, followeeId: int) -> None: Follower follows a followee. If the operation is invalid, it should be a no-op.
- def unfollow(self, followerId: int, followeeId: int) -> None: Follower unfollows a followee. If the operation is invalid, it should be a no-op.

Implement the Python class `Twitter` described below.

Class description:
Implement the Twitter class.

Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def postTweet(self, userId: int, tweetId: int) -> None: Compose a new tweet.
- def getNewsFeed(self, userId: int) -> List[int]: Retrieve the 10 most recent tweet ids in the user's news feed. Each item in the news feed must be posted by users who the user followed or by the user herself. Tweets must be ordered from most recent to least recent.
- def follow(self, followerId: int, followeeId: int) -> None: Follower follows a followee. If the operation is invalid, it should be a no-op.
- def unfollow(self, followerId: int, followeeId: int) -> None: Follower unfollows a followee. If the operation is invalid, it should be a no-op.

<|skeleton|>
class Twitter:

    def __init__(self):
        """Initialize your data structure here."""
        <|body_0|>

    def postTweet(self, userId: int, tweetId: int) -> None:
        """Compose a new tweet."""
        <|body_1|>

    def getNewsFeed(self, userId: int) -> List[int]:
        """Retrieve the 10 most recent tweet ids in the user's news feed. Each item in the news feed must be posted by users who the user followed or by the user herself. Tweets must be ordered from most recent to least recent."""
        <|body_2|>

    def follow(self, followerId: int, followeeId: int) -> None:
        """Follower follows a followee. If the operation is invalid, it should be a no-op."""
        <|body_3|>

    def unfollow(self, followerId: int, followeeId: int) -> None:
        """Follower unfollows a followee. If the operation is invalid, it should be a no-op."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.timer = 0
        self.tweets = collections.defaultdict(list)
        self.followees = collections.defaultdict(set)
        self.max_tweets = 10
<|end_body_0|>

<|body_start_1|>
        self.timer += 1
        if len(self.tweets[userId]) == self.max_tweets:
            self.tweets[userId].pop(0)
        self.tweets[userId].append((self.timer, tweetId))
<|end_body_1|>

<|body_start_2|>
        ts = [i for i in self.tweets[userId]]
        for uid in self.followees[userId]:
            ts.extend(self.tweets[uid])
        ts.sort(key=lambda x: x[0], reverse=True)
        res = []
        for i in range(min(self.max_tweets, len(ts))):
            res.append(ts[i][1])
        return res
<|end_body_2|>

<|body_start_3|>
        if followerId == followeeId:
            return
        self.followees[followerId].add(followeeId)
<|end_body_3|>

<|body_start_4|>
        if followerId == followeeId:
            return
        self.followees[followerId].discard(followeeId)
<|end_body_4|>

Solution:
class Twitter:
    def __init__(self):
        """Initialize your data structure here."""
        self.timer = 0
        self.tweets = collections.defaultdict(list)
        self.followees = collections.defaultdict(set)
        self.max_tweets = 10

    def postTweet(self, userId: int, tweetId: int) -> None:
        """Compose a new tweet."""
        self.timer += 1
        if len(self.tweets[userId]) == self.max_tweets:
            self.tweets[userId].pop(0)
        self.tweets[userId].append((self.timer, tweetId))

    def getNewsFeed(self, userId: int) -> List[int]:
        """Retrieve the 10 most recent tweet ids in the user's news feed. Each item in the news feed must be posted by users who the user followed or by the user herself. Tweets must be ordered from most recent to least recent."""
        ts = [i for i in self.tweets[userId]]
        for uid in self.followees[userId]:
            ts.extend(self.tweets[uid])
        ts.sort(key=lambda x: x[0], reverse=True)
        res = []
        for i in range(min(self.max_tweets, len(ts))):
            res.append(ts[i][1])
        return res

    def follow(self, followerId: int, followeeId: int) -> None:
        """Follower follows a followee. If the operation is invalid, it should be a no-op."""
        if followerId == followeeId:
            return
        self.followees[followerId].add(followeeId)

    def unfollow(self, followerId: int, followeeId: int) -> None:
        """Follower unfollows a followee. If the operation is invalid, it should be a no-op."""
        if followerId == followeeId:
            return
        self.followees[followerId].discard(followeeId)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def solveNQueens(self, n): 回溯 :type n: int :rtype: List[List[str]]
- def solveNQueens2(self, n): https://leetcode.com/problems/n-queens/discuss/19937/AC-Python-76-ms-iterative-backtracking-solution :param n: :return:

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def solveNQueens(self, n): 回溯 :type n: int :rtype: List[List[str]]
- def solveNQueens2(self, n): https://leetcode.com/problems/n-queens/discuss/19937/AC-Python-76-ms-iterative-backtracking-solution :param n: :return:

<|skeleton|>
class Solution:

    def solveNQueens(self, n):
        """回溯 :type n: int :rtype: List[List[str]]"""
        <|body_0|>

    def solveNQueens2(self, n):
        """https://leetcode.com/problems/n-queens/discuss/19937/AC-Python-76-ms-iterative-backtracking-solution :param n: :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        row, col = ([0] * n, [0] * n)
        diag, anti_diag = ([0] * (2 * n), [0] * (2 * n))

        def dfs(x, y, s, n, path, res):
            if y == n:
                x += 1
                y = 0
            if x == n:
                if s == n:
                    res.append(copy.deepcopy(path))
                return
            if not row[x] and (not col[y]) and (not diag[x + y]) and (not anti_diag[n - 1 - x + y]):
                row[x] = col[y] = diag[x + y] = anti_diag[n - 1 - x + y] = 1
                path[x][y] = 'Q'
                dfs(x, y + 1, s + 1, n, path, res)
                path[x][y] = '.'
                row[x] = col[y] = diag[x + y] = anti_diag[n - 1 - x + y] = 0
            dfs(x, y + 1, s, n, path, res)
        res = []
        path = [['.'] * n for _ in range(n)]
        dfs(0, 0, 0, n, path, res)
        return [[''.join(y) for y in x] for x in res]
<|end_body_0|>

<|body_start_1|>
        ans = []
        queens = [-1] * n
        columns = [True] * n + [False]
        back = [True] * n * 2
        forward = [True] * n * 2
        row = col = 0
        while True:
            if columns[col] and back[col - row + n] and forward[col + row]:
                queens[row] = col
                columns[col] = back[col - row + n] = forward[col + row] = False
                row += 1
                col = 0
                if row == n:
                    ans.append(['.' * q + 'Q' + '.' * (n - q - 1) for q in queens])
            else:
                if row == n or col == n:
                    if row == 0:
                        return ans
                    row -= 1
                    col = queens[row]
                    columns[col] = back[col - row + n] = forward[col + row] = True
                col += 1
<|end_body_1|>

Solution:
class Solution:
    def solveNQueens(self, n):
        """回溯 :type n: int :rtype: List[List[str]]"""
        row, col = ([0] * n, [0] * n)
        diag, anti_diag = ([0] * (2 * n), [0] * (2 * n))

        def dfs(x, y, s, n, path, res):
            if y == n:
                x += 1
                y = 0
            if x == n:
                if s == n:
                    res.append(copy.deepcopy(path))
                return
            if not row[x] and (not col[y]) and (not diag[x + y]) and (not anti_diag[n - 1 - x + y]):
                row[x] = col[y] = diag[x + y] = anti_diag[n - 1 - x + y] = 1
                path[x][y] = 'Q'
                dfs(x, y + 1, s + 1, n, path, res)
                path[x][y] = '.'
                row[x] = col[y] = diag[x + y] = anti_diag[n - 1 - x + y] = 0
            dfs(x, y + 1, s, n, path, res)
        res = []
        path = [['.'] * n for _ in range(n)]
        dfs(0, 0, 0, n, path, res)
        return [[''.join(y) for y in x] for x in res]

    def solveNQueens2(self, n):
        """https://leetcode.com/problems/n-queens/discuss/19937/AC-Python-76-ms-iterative-backtracking-solution :param n: :return:"""
        ans = []
        queens = [-1] * n
        columns = [True] * n + [False]
        back = [True] * n * 2
        forward = [True] * n * 2
        row = col = 0
        while True:
            if columns[col] and back[col - row + n] and forward[col + row]:
                queens[row] = col
                columns[col] = back[col - row + n] = forward[col + row] = False
                row += 1
                col = 0
                if row == n:
                    ans.append(['.' * q + 'Q' + '.' * (n - q - 1) for q in queens])
            else:
                if row == n or col == n:
                    if row == 0:
                        return ans
                    row -= 1
                    col = queens[row]
                    columns[col] = back[col - row + n] = forward[col + row] = True
                col += 1


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

Class description:
Serialize the Exam Problem with link and info.

Method signatures and docstrings:
- def get_info_data(self, obj, *args, **kwargs): Get information data. :param obj: :param args: :param kwargs: :return:
- def get_links_url(self, obj, *args, **kwargs): Get link url :param obj: :param args: :param kwargs: :return:

Implement the Python class `ExamProblemsSerializers` described below.

Class description:
Serialize the Exam Problem with link and info.

Method signatures and docstrings:
- def get_info_data(self, obj, *args, **kwargs): Get information data. :param obj: :param args: :param kwargs: :return:
- def get_links_url(self, obj, *args, **kwargs): Get link url :param obj: :param args: :param kwargs: :return:

<|skeleton|>
class ExamProblemsSerializers:
    """Serialize the Exam Problem with link and info."""

    def get_info_data(self, obj, *args, **kwargs):
        """Get information data. :param obj: :param args: :param kwargs: :return:"""
        <|body_0|>

    def get_links_url(self, obj, *args, **kwargs):
        """Get link url :param obj: :param args: :param kwargs: :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        info = {}
        try:
            if obj.teacher:
                info['teacher'] = obj.teacher.pen_name
        except Sensei.DoesNotExist as e:
            info['teacher'] = str(e)
        try:
            info_problems = OrderedDict({})
            for index, value in enumerate(obj.problems.all()):
                info_problems[value.pk] = value.get_data()
            info['problems'] = info_problems
        except ProblemInstance.DoesNotExist as e:
            info['problems'] = str(e)
        return info
<|end_body_0|>

<|body_start_1|>
        link = OrderedDict({})
        try:
            if obj.teacher:
                link['teacher'] = urljoin(reverse('v1:classroom:sensei-list', request=self.context['request']), str(obj.teacher.pk))
        except Sensei.DoesNotExist as e:
            link['teacher'] = str(e)
        try:
            link_problems = OrderedDict({})
            for index, value in enumerate(obj.problems.all()):
                link_problems[value.pk] = urljoin(reverse('v1:problem:problem-instance-list', request=self.context['request']), str(value.pk))
            link['problems'] = link_problems
        except ProblemInstance.DoesNotExist as e:
            link['problems'] = str(e)
        return link
<|end_body_1|>

Solution:
class ExamProblemsSerializers:
    """Serialize the Exam Problem with link and info."""

    def get_info_data(self, obj, *args, **kwargs):
        """Get information data. :param obj: :param args: :param kwargs: :return:"""
        info = {}
        try:
            if obj.teacher:
                info['teacher'] = obj.teacher.pen_name
        except Sensei.DoesNotExist as e:
            info['teacher'] = str(e)
        try:
            info_problems = OrderedDict({})
            for index, value in enumerate(obj.problems.all()):
                info_problems[value.pk] = value.get_data()
            info['problems'] = info_problems
        except ProblemInstance.DoesNotExist as e:
            info['problems'] = str(e)
        return info

    def get_links_url(self, obj, *args, **kwargs):
        """Get link url :param obj: :param args: :param kwargs: :return:"""
        link = OrderedDict({})
        try:
            if obj.teacher:
                link['teacher'] = urljoin(reverse('v1:classroom:sensei-list', request=self.context['request']), str(obj.teacher.pk))
        except Sensei.DoesNotExist as e:
            link['teacher'] = str(e)
        try:
            link_problems = OrderedDict({})
            for index, value in enumerate(obj.problems.all()):
                link_problems[value.pk] = urljoin(reverse('v1:problem:problem-instance-list', request=self.context['request']), str(value.pk))
            link['problems'] = link_problems
        except ProblemInstance.DoesNotExist as e:
            link['problems'] = str(e)
        return link


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

Class description:
Implement the CarrierApi class.

Method signatures and docstrings:
- def get_default_carrier_service(self): Get default service API
- def test_connection(self): Test connection Carrier API - Webservices Call method test_methodname

Implement the Python class `CarrierApi` described below.

Class description:
Implement the CarrierApi class.

Method signatures and docstrings:
- def get_default_carrier_service(self): Get default service API
- def test_connection(self): Test connection Carrier API - Webservices Call method test_methodname

<|skeleton|>
class CarrierApi:

    def get_default_carrier_service(self):
        """Get default service API"""
        <|body_0|>

    def test_connection(self):
        """Test connection Carrier API - Webservices Call method test_methodname"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.ensure_one()
        for service in self.services:
            if service.default:
                return service
        raise exceptions.Warning(_('Service error'), _("API doesn't have default service"))
<|end_body_0|>

<|body_start_1|>
        for carrier_api in self:
            test = getattr(self, 'test_%s' % self.method)
            test()
<|end_body_1|>

Solution:
class CarrierApi:
    def get_default_carrier_service(self):
        """Get default service API"""
        self.ensure_one()
        for service in self.services:
            if service.default:
                return service
        raise exceptions.Warning(_('Service error'), _("API doesn't have default service"))

    def test_connection(self):
        """Test connection Carrier API - Webservices Call method test_methodname"""
        for carrier_api in self:
            test = getattr(self, 'test_%s' % self.method)
            test()


resids

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

Class description:
TODO: Summary Attributes: allowed_pairs (TYPE): Description body (TYPE): Description bodyid (TYPE): Description chains (TYPE): Description end_of_chain (TYPE): Description min_seg_len (TYPE): Description nsite (TYPE): Description sites (TYPE): Description start_of_chain (TYPE): Description

Method signatures and docstrings:
- def __init__(self, body, sites, *, bodyid=None, min_seg_len=1, allowed_pairs=None): TODO: Summary Args: body (TYPE): Description sites (TYPE): Description bodyid (None, optional): Description min_seg_len (int, optional): Description allowed_pairs (None, optional): Description Raises: ValueError: Description
- def resids(self, isite): TODO: Summary Args: isite (TYPE): Description Returns: TYPE: Description
- def spliceable_positions(self): selection of resids, and map 'global' index to selected index Returns: TYPE: Description
- def is_compatible(self, isite, ires, jsite, jres): TODO: Summary Args: isite (TYPE): Description ires (TYPE): Description jsite (TYPE): Description jres (TYPE): Description Returns: TYPE: Description
- def sitepair_allowed(self, isite, jsite): TODO: Summary Args: isite (TYPE): Description jsite (TYPE): Description Returns: TYPE: Description
- def __repr__(self): TODO: Summary Returns: TYPE: Description

Implement the Python class `Spliceable` described below.

Class description:
TODO: Summary Attributes: allowed_pairs (TYPE): Description body (TYPE): Description bodyid (TYPE): Description chains (TYPE): Description end_of_chain (TYPE): Description min_seg_len (TYPE): Description nsite (TYPE): Description sites (TYPE): Description start_of_chain (TYPE): Description

Method signatures and docstrings:
- def __init__(self, body, sites, *, bodyid=None, min_seg_len=1, allowed_pairs=None): TODO: Summary Args: body (TYPE): Description sites (TYPE): Description bodyid (None, optional): Description min_seg_len (int, optional): Description allowed_pairs (None, optional): Description Raises: ValueError: Description
- def resids(self, isite): TODO: Summary Args: isite (TYPE): Description Returns: TYPE: Description
- def spliceable_positions(self): selection of resids, and map 'global' index to selected index Returns: TYPE: Description
- def is_compatible(self, isite, ires, jsite, jres): TODO: Summary Args: isite (TYPE): Description ires (TYPE): Description jsite (TYPE): Description jres (TYPE): Description Returns: TYPE: Description
- def sitepair_allowed(self, isite, jsite): TODO: Summary Args: isite (TYPE): Description jsite (TYPE): Description Returns: TYPE: Description
- def __repr__(self): TODO: Summary Returns: TYPE: Description

<|skeleton|>
class Spliceable:
    """TODO: Summary Attributes: allowed_pairs (TYPE): Description body (TYPE): Description bodyid (TYPE): Description chains (TYPE): Description end_of_chain (TYPE): Description min_seg_len (TYPE): Description nsite (TYPE): Description sites (TYPE): Description start_of_chain (TYPE): Description"""

    def __init__(self, body, sites, *, bodyid=None, min_seg_len=1, allowed_pairs=None):
        """TODO: Summary Args: body (TYPE): Description sites (TYPE): Description bodyid (None, optional): Description min_seg_len (int, optional): Description allowed_pairs (None, optional): Description Raises: ValueError: Description"""
        <|body_0|>

    def resids(self, isite):
        """TODO: Summary Args: isite (TYPE): Description Returns: TYPE: Description"""
        <|body_1|>

    def spliceable_positions(self):
        """selection of resids, and map 'global' index to selected index Returns: TYPE: Description"""
        <|body_2|>

    def is_compatible(self, isite, ires, jsite, jres):
        """TODO: Summary Args: isite (TYPE): Description ires (TYPE): Description jsite (TYPE): Description jres (TYPE): Description Returns: TYPE: Description"""
        <|body_3|>

    def sitepair_allowed(self, isite, jsite):
        """TODO: Summary Args: isite (TYPE): Description jsite (TYPE): Description Returns: TYPE: Description"""
        <|body_4|>

    def __repr__(self):
        """TODO: Summary Returns: TYPE: Description"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        self.body = body
        chains = list(body.split_by_chain())
        self.start_of_chain = {i + 1: sum((len(c) for c in chains[:i])) for i in range(len(chains))}
        self.end_of_chain = {i + 1: sum((len(c) for c in chains[:i + 1])) for i in range(len(chains))}
        self.start_of_chain[None] = 0
        self.chains = {i + 1: c for i, c in enumerate(chains)}
        self.bodyid = bodyid
        if callable(sites):
            sites = sites(body)
        if isinstance(sites, SpliceSite):
            sites = [sites]
        self.sites = list(sites)
        for i, site in enumerate(self.sites):
            if isinstance(site, str):
                raise ValueError('site currently must be (sele, polarity)')
            if not isinstance(site, SpliceSite):
                if isinstance(site, dict):
                    self.sites[i] = SpliceSite(**site)
                else:
                    if not isinstance(site, Iterable):
                        self.sites[i] = (site,)
                    self.sites[i] = SpliceSite(*site)
        self.nsite = dict(N=0, C=0)
        for s in self.sites:
            self.nsite[s.polarity] += 1
        self.min_seg_len = min_seg_len
        self._resids_list = [site._resids(self) for site in self.sites]
        self._len_body = len(body)
        self._chains = np.array([body.chain(i + 1) for i in range(len(body))])
        self.allowed_pairs = allowed_pairs
<|end_body_0|>

<|body_start_1|>
        if isite < 0:
            return [None]
        return self._resids_list[isite]
<|end_body_1|>

<|body_start_2|>
        resid_subset = set()
        for i in range(len(self.sites)):
            resid_subset |= set(self._resids_list[i])
        resid_subset = np.array(list(resid_subset))
        N = self._len_body + 1
        val, idx = np.where(0 == np.arange(N)[np.newaxis, :] - resid_subset[:, np.newaxis])
        to_subset = np.array(N * [-1])
        to_subset[idx] = val
        assert np.all(to_subset[resid_subset] == np.arange(len(resid_subset)))
        return (resid_subset, to_subset)
<|end_body_2|>

<|body_start_3|>
        if ires < 0 or jres < 0:
            return True
        assert 0 < ires <= self._len_body and 0 < jres <= self._len_body
        ichain, jchain = (self._chains[ires - 1], self._chains[jres - 1])
        if ichain == jchain:
            ipol = self.sites[isite].polarity
            jpol = self.sites[jsite].polarity
            if ipol == jpol:
                return False
            if ipol == 'N':
                seglen = jres - ires + 1
            else:
                seglen = ires - jres + 1
            if seglen < self.min_seg_len:
                return False
        return True
<|end_body_3|>

<|body_start_4|>
        if isite == jsite:
            return False
        if isite < 0 or jsite < 0:
            return True
        if self.allowed_pairs is not None and (isite, jsite) not in self.allowed_pairs:
            return False
        return True
<|end_body_4|>

<|body_start_5|>
        sites = str([(s._resids(self), s.polarity) for s in self.sites])
        if len(sites) > 30:
            sites = sites[:30] + '...'
        return 'Spliceable: body=(' + str(self._len_body) + ',' + str(self.body).splitlines()[0].split('/')[-1] + '), sites=' + sites
<|end_body_5|>

Solution:
class Spliceable:
    """TODO: Summary Attributes: allowed_pairs (TYPE): Description body (TYPE): Description bodyid (TYPE): Description chains (TYPE): Description end_of_chain (TYPE): Description min_seg_len (TYPE): Description nsite (TYPE): Description sites (TYPE): Description start_of_chain (TYPE): Description"""

    def __init__(self, body, sites, *, bodyid=None, min_seg_len=1, allowed_pairs=None):
        """TODO: Summary Args: body (TYPE): Description sites (TYPE): Description bodyid (None, optional): Description min_seg_len (int, optional): Description allowed_pairs (None, optional): Description Raises: ValueError: Description"""
        self.body = body
        chains = list(body.split_by_chain())
        self.start_of_chain = {i + 1: sum((len(c) for c in chains[:i])) for i in range(len(chains))}
        self.end_of_chain = {i + 1: sum((len(c) for c in chains[:i + 1])) for i in range(len(chains))}
        self.start_of_chain[None] = 0
        self.chains = {i + 1: c for i, c in enumerate(chains)}
        self.bodyid = bodyid
        if callable(sites):
            sites = sites(body)
        if isinstance(sites, SpliceSite):
            sites = [sites]
        self.sites = list(sites)
        for i, site in enumerate(self.sites):
            if isinstance(site, str):
                raise ValueError('site currently must be (sele, polarity)')
            if not isinstance(site, SpliceSite):
                if isinstance(site, dict):
                    self.sites[i] = SpliceSite(**site)
                else:
                    if not isinstance(site, Iterable):
                        self.sites[i] = (site,)
                    self.sites[i] = SpliceSite(*site)
        self.nsite = dict(N=0, C=0)
        for s in self.sites:
            self.nsite[s.polarity] += 1
        self.min_seg_len = min_seg_len
        self._resids_list = [site._resids(self) for site in self.sites]
        self._len_body = len(body)
        self._chains = np.array([body.chain(i + 1) for i in range(len(body))])
        self.allowed_pairs = allowed_pairs

    def resids(self, isite):
        """TODO: Summary Args: isite (TYPE): Description Returns: TYPE: Description"""
        if isite < 0:
            return [None]
        return self._resids_list[isite]

    def spliceable_positions(self):
        """selection of resids, and map 'global' index to selected index Returns: TYPE: Description"""
        resid_subset = set()
        for i in range(len(self.sites)):
            resid_subset |= set(self._resids_list[i])
        resid_subset = np.array(list(resid_subset))
        N = self._len_body + 1
        val, idx = np.where(0 == np.arange(N)[np.newaxis, :] - resid_subset[:, np.newaxis])
        to_subset = np.array(N * [-1])
        to_subset[idx] = val
        assert np.all(to_subset[resid_subset] == np.arange(len(resid_subset)))
        return (resid_subset, to_subset)

    def is_compatible(self, isite, ires, jsite, jres):
        """TODO: Summary Args: isite (TYPE): Description ires (TYPE): Description jsite (TYPE): Description jres (TYPE): Description Returns: TYPE: Description"""
        if ires < 0 or jres < 0:
            return True
        assert 0 < ires <= self._len_body and 0 < jres <= self._len_body
        ichain, jchain = (self._chains[ires - 1], self._chains[jres - 1])
        if ichain == jchain:
            ipol = self.sites[isite].polarity
            jpol = self.sites[jsite].polarity
            if ipol == jpol:
                return False
            if ipol == 'N':
                seglen = jres - ires + 1
            else:
                seglen = ires - jres + 1
            if seglen < self.min_seg_len:
                return False
        return True

    def sitepair_allowed(self, isite, jsite):
        """TODO: Summary Args: isite (TYPE): Description jsite (TYPE): Description Returns: TYPE: Description"""
        if isite == jsite:
            return False
        if isite < 0 or jsite < 0:
            return True
        if self.allowed_pairs is not None and (isite, jsite) not in self.allowed_pairs:
            return False
        return True

    def __repr__(self):
        """TODO: Summary Returns: TYPE: Description"""
        sites = str([(s._resids(self), s.polarity) for s in self.sites])
        if len(sites) > 30:
            sites = sites[:30] + '...'
        return 'Spliceable: body=(' + str(self._len_body) + ',' + str(self.body).splitlines()[0].split('/')[-1] + '), sites=' + sites


testRelativeLibPaths

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

Class description:
Test the ELF parsing functions.

Method signatures and docstrings:
- def testIsLib(self): Tests the 'is_lib' attribute is inferred correctly for libs.
- def testNotIsLib(self): Tests the 'is_lib' attribute is inferred correctly for executables.
- def testUnsupportedFiles(self): Tests unsupported files are ignored.
- def testParsedSymbols(self): Tests the list of imported/exported symbols.
- def testLibDependencies(self): Tests the list direct dependencies.
- def testRelativeLibPaths(self): Test that the paths reported by ParseELF are relative to root.

Implement the Python class `ELFParsingTest` described below.

Class description:
Test the ELF parsing functions.

Method signatures and docstrings:
- def testIsLib(self): Tests the 'is_lib' attribute is inferred correctly for libs.
- def testNotIsLib(self): Tests the 'is_lib' attribute is inferred correctly for executables.
- def testUnsupportedFiles(self): Tests unsupported files are ignored.
- def testParsedSymbols(self): Tests the list of imported/exported symbols.
- def testLibDependencies(self): Tests the list direct dependencies.
- def testRelativeLibPaths(self): Test that the paths reported by ParseELF are relative to root.

<|skeleton|>
class ELFParsingTest:
    """Test the ELF parsing functions."""

    def testIsLib(self):
        """Tests the 'is_lib' attribute is inferred correctly for libs."""
        <|body_0|>

    def testNotIsLib(self):
        """Tests the 'is_lib' attribute is inferred correctly for executables."""
        <|body_1|>

    def testUnsupportedFiles(self):
        """Tests unsupported files are ignored."""
        <|body_2|>

    def testParsedSymbols(self):
        """Tests the list of imported/exported symbols."""
        <|body_3|>

    def testLibDependencies(self):
        """Tests the list direct dependencies."""
        <|body_4|>

    def testRelativeLibPaths(self):
        """Test that the paths reported by ParseELF are relative to root."""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'liba.so'), ['func_a'])
        elf = parseelf.ParseELF(self.tempdir, 'liba.so', self._ldpaths)
        self.assertTrue('is_lib' in elf)
        self.assertTrue(elf['is_lib'])
<|end_body_0|>

<|body_start_1|>
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'abc_main'), executable=True)
        elf = parseelf.ParseELF(self.tempdir, 'abc_main', self._ldpaths)
        self.assertTrue('is_lib' in elf)
        self.assertFalse(elf['is_lib'])
<|end_body_1|>

<|body_start_2|>
        osutils.WriteFile(os.path.join(self.tempdir, 'foo.so'), 'foo')
        self.assertEquals(None, parseelf.ParseELF(self.tempdir, 'foo.so', self._ldpaths))
        osutils.WriteFile(os.path.join(self.tempdir, 'foo.so'), '\x7fELF-foo')
        self.assertEquals(None, parseelf.ParseELF(self.tempdir, 'foo.so', self._ldpaths))
<|end_body_2|>

<|body_start_3|>
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libabc.so'), defined_symbols=['fa', 'fb', 'fc'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libxyz.so'), defined_symbols=['fx', 'fy', 'fz'], undefined_symbols=['fa', 'fb', 'fc'], used_libs=['abc'])
        elf = parseelf.ParseELF(self.tempdir, 'libxyz.so', self._ldpaths, parse_symbols=False)
        self.assertFalse('imp_sym' in elf)
        self.assertFalse('exp_sym' in elf)
        elf = parseelf.ParseELF(self.tempdir, 'libxyz.so', self._ldpaths)
        self.assertTrue('imp_sym' in elf)
        self.assertTrue('exp_sym' in elf)
        self.assertEquals(elf['imp_sym'], set(['fa', 'fb', 'fc']))
        self.assertEquals(set((k for k, (_, _, st_shndx) in elf['exp_sym'].iteritems() if st_shndx == 'SHT_DYNSYM')), set(['fx', 'fy', 'fz']))
        for sym in ['fx', 'fy', 'fz']:
            self.assertEquals('STB_GLOBAL', elf['exp_sym'][sym][0])
<|end_body_3|>

<|body_start_4|>
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libabc.so'), defined_symbols=['fa', 'fb', 'fc'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libu.so'), defined_symbols=['fu'], undefined_symbols=['fa'], used_libs=['abc'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libv.so'), defined_symbols=['fv'], undefined_symbols=['fb'], used_libs=['abc'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'prog'), undefined_symbols=['fu', 'fv'], used_libs=['u', 'v'], executable=True)
        elf_prog = parseelf.ParseELF(self.tempdir, 'prog', self._ldpaths)
        self.assertTrue('libu.so' in elf_prog['needed'])
        self.assertTrue('libv.so' in elf_prog['needed'])
        self.assertFalse('libabc.so' in elf_prog['needed'])
<|end_body_4|>

<|body_start_5|>
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'liba.so'), ['fa'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'prog'), undefined_symbols=['fa'], used_libs=['a'], executable=True)
        elf = parseelf.ParseELF(self.tempdir, 'prog', self._ldpaths)
        for lib in elf['libs'].values():
            for path in ('realpath', 'path'):
                if lib[path] is None:
                    continue
                self.assertFalse(lib[path].startswith('/'))
                self.assertFalse(lib[path].startswith(self.tempdir))
                self.assertTrue(lib[path] == elf['interp'] or os.path.exists(os.path.join(self.tempdir, lib[path])))
<|end_body_5|>

Solution:
class ELFParsingTest:
    """Test the ELF parsing functions."""

    def testIsLib(self):
        """Tests the 'is_lib' attribute is inferred correctly for libs."""
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'liba.so'), ['func_a'])
        elf = parseelf.ParseELF(self.tempdir, 'liba.so', self._ldpaths)
        self.assertTrue('is_lib' in elf)
        self.assertTrue(elf['is_lib'])

    def testNotIsLib(self):
        """Tests the 'is_lib' attribute is inferred correctly for executables."""
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'abc_main'), executable=True)
        elf = parseelf.ParseELF(self.tempdir, 'abc_main', self._ldpaths)
        self.assertTrue('is_lib' in elf)
        self.assertFalse(elf['is_lib'])

    def testUnsupportedFiles(self):
        """Tests unsupported files are ignored."""
        osutils.WriteFile(os.path.join(self.tempdir, 'foo.so'), 'foo')
        self.assertEquals(None, parseelf.ParseELF(self.tempdir, 'foo.so', self._ldpaths))
        osutils.WriteFile(os.path.join(self.tempdir, 'foo.so'), '\x7fELF-foo')
        self.assertEquals(None, parseelf.ParseELF(self.tempdir, 'foo.so', self._ldpaths))

    def testParsedSymbols(self):
        """Tests the list of imported/exported symbols."""
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libabc.so'), defined_symbols=['fa', 'fb', 'fc'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libxyz.so'), defined_symbols=['fx', 'fy', 'fz'], undefined_symbols=['fa', 'fb', 'fc'], used_libs=['abc'])
        elf = parseelf.ParseELF(self.tempdir, 'libxyz.so', self._ldpaths, parse_symbols=False)
        self.assertFalse('imp_sym' in elf)
        self.assertFalse('exp_sym' in elf)
        elf = parseelf.ParseELF(self.tempdir, 'libxyz.so', self._ldpaths)
        self.assertTrue('imp_sym' in elf)
        self.assertTrue('exp_sym' in elf)
        self.assertEquals(elf['imp_sym'], set(['fa', 'fb', 'fc']))
        self.assertEquals(set((k for k, (_, _, st_shndx) in elf['exp_sym'].iteritems() if st_shndx == 'SHT_DYNSYM')), set(['fx', 'fy', 'fz']))
        for sym in ['fx', 'fy', 'fz']:
            self.assertEquals('STB_GLOBAL', elf['exp_sym'][sym][0])

    def testLibDependencies(self):
        """Tests the list direct dependencies."""
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libabc.so'), defined_symbols=['fa', 'fb', 'fc'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libu.so'), defined_symbols=['fu'], undefined_symbols=['fa'], used_libs=['abc'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'libv.so'), defined_symbols=['fv'], undefined_symbols=['fb'], used_libs=['abc'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'prog'), undefined_symbols=['fu', 'fv'], used_libs=['u', 'v'], executable=True)
        elf_prog = parseelf.ParseELF(self.tempdir, 'prog', self._ldpaths)
        self.assertTrue('libu.so' in elf_prog['needed'])
        self.assertTrue('libv.so' in elf_prog['needed'])
        self.assertFalse('libabc.so' in elf_prog['needed'])

    def testRelativeLibPaths(self):
        """Test that the paths reported by ParseELF are relative to root."""
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'liba.so'), ['fa'])
        unittest_lib.BuildELF(os.path.join(self.tempdir, 'prog'), undefined_symbols=['fa'], used_libs=['a'], executable=True)
        elf = parseelf.ParseELF(self.tempdir, 'prog', self._ldpaths)
        for lib in elf['libs'].values():
            for path in ('realpath', 'path'):
                if lib[path] is None:
                    continue
                self.assertFalse(lib[path].startswith('/'))
                self.assertFalse(lib[path].startswith(self.tempdir))
                self.assertTrue(lib[path] == elf['interp'] or os.path.exists(os.path.join(self.tempdir, lib[path])))


step

Prompt:
Implement the Python class `DwuFp16Optimizer` described below.

Class description:
Distributed weight update mixed precision optimizer with dynamic loss scaling and backoff. https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#scalefactor

Method signatures and docstrings:
- def __init__(self, fp16_model, loss_scale=1024, dls_downscale=2, dls_upscale=2, dls_upscale_interval=128): Constructor for the DwuFp16Optimizer. :param fp16_model: model (previously casted to half) :param loss_scale: initial loss scale :param dls_downscale: loss downscale factor, loss scale is divided by this factor when NaN/INF occurs in the gradients :param dls_upscale: loss upscale factor, loss scale is multiplied by this factor if previous dls_upscale_interval batches finished successfully :param dls_upscale_interval: interval for loss scale upscaling
- def step(self, loss, optimizer, scheduler, update=True): Performs one step of the optimizer. Applies loss scaling, computes gradients in fp16, converts gradients to fp32, inverts scaling and applies optional gradient norm clipping. If gradients are finite, it applies update to fp32 master weights and copies updated parameters to fp16 model for the next iteration. If gradients are not finite, it skips the batch and adjusts scaling factor for the next iteration. :param loss: value of loss function :param optimizer: optimizer :param update: if True executes weight update

Implement the Python class `DwuFp16Optimizer` described below.

Class description:
Distributed weight update mixed precision optimizer with dynamic loss scaling and backoff. https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#scalefactor

Method signatures and docstrings:
- def __init__(self, fp16_model, loss_scale=1024, dls_downscale=2, dls_upscale=2, dls_upscale_interval=128): Constructor for the DwuFp16Optimizer. :param fp16_model: model (previously casted to half) :param loss_scale: initial loss scale :param dls_downscale: loss downscale factor, loss scale is divided by this factor when NaN/INF occurs in the gradients :param dls_upscale: loss upscale factor, loss scale is multiplied by this factor if previous dls_upscale_interval batches finished successfully :param dls_upscale_interval: interval for loss scale upscaling
- def step(self, loss, optimizer, scheduler, update=True): Performs one step of the optimizer. Applies loss scaling, computes gradients in fp16, converts gradients to fp32, inverts scaling and applies optional gradient norm clipping. If gradients are finite, it applies update to fp32 master weights and copies updated parameters to fp16 model for the next iteration. If gradients are not finite, it skips the batch and adjusts scaling factor for the next iteration. :param loss: value of loss function :param optimizer: optimizer :param update: if True executes weight update

<|skeleton|>
class DwuFp16Optimizer:
    """Distributed weight update mixed precision optimizer with dynamic loss scaling and backoff. https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#scalefactor"""

    def __init__(self, fp16_model, loss_scale=1024, dls_downscale=2, dls_upscale=2, dls_upscale_interval=128):
        """Constructor for the DwuFp16Optimizer. :param fp16_model: model (previously casted to half) :param loss_scale: initial loss scale :param dls_downscale: loss downscale factor, loss scale is divided by this factor when NaN/INF occurs in the gradients :param dls_upscale: loss upscale factor, loss scale is multiplied by this factor if previous dls_upscale_interval batches finished successfully :param dls_upscale_interval: interval for loss scale upscaling"""
        <|body_0|>

    def step(self, loss, optimizer, scheduler, update=True):
        """Performs one step of the optimizer. Applies loss scaling, computes gradients in fp16, converts gradients to fp32, inverts scaling and applies optional gradient norm clipping. If gradients are finite, it applies update to fp32 master weights and copies updated parameters to fp16 model for the next iteration. If gradients are not finite, it skips the batch and adjusts scaling factor for the next iteration. :param loss: value of loss function :param optimizer: optimizer :param update: if True executes weight update"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        logging.info('Initializing dwu fp16 optimizer')
        self.since_last_invalid = 0
        self.loss_scale = loss_scale
        self.dls_downscale = dls_downscale
        self.dls_upscale = dls_upscale
        self.dls_upscale_interval = dls_upscale_interval
        self.world_size = utils.get_world_size()
        self.fp16_model = fp16_model
<|end_body_0|>

<|body_start_1|>
        scaling_factor = self.loss_scale * self.world_size
        optimizer.set_global_scale(scaling_factor)
        loss *= self.loss_scale
        loss.backward()
        optimizer.complete_reductions()
        if not update:
            torch.cuda.synchronize()
            return
        norm = optimizer.L2_grad_norm / scaling_factor
        should_update = math.isfinite(norm)
        if should_update:
            if scheduler is not None:
                scheduler.step()
            optimizer.step(skip_overflow_check=True)
        if should_update:
            self.since_last_invalid += 1
        else:
            self.loss_scale /= self.dls_downscale
            self.since_last_invalid = 0
            logging.info(f'Gradient norm: {norm}')
            logging.info(f'Skipped batch, new scale: {self.loss_scale}')
        if self.since_last_invalid >= self.dls_upscale_interval:
            self.loss_scale *= self.dls_upscale
            self.loss_scale = min(self.loss_scale, 8192.0)
            logging.info(f'Upscaling, new scale: {self.loss_scale}')
            self.since_last_invalid = 0
        for p in self.fp16_model.parameters():
            p.grad = None
<|end_body_1|>

Solution:
class DwuFp16Optimizer:
    """Distributed weight update mixed precision optimizer with dynamic loss scaling and backoff. https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#scalefactor"""

    def __init__(self, fp16_model, loss_scale=1024, dls_downscale=2, dls_upscale=2, dls_upscale_interval=128):
        """Constructor for the DwuFp16Optimizer. :param fp16_model: model (previously casted to half) :param loss_scale: initial loss scale :param dls_downscale: loss downscale factor, loss scale is divided by this factor when NaN/INF occurs in the gradients :param dls_upscale: loss upscale factor, loss scale is multiplied by this factor if previous dls_upscale_interval batches finished successfully :param dls_upscale_interval: interval for loss scale upscaling"""
        logging.info('Initializing dwu fp16 optimizer')
        self.since_last_invalid = 0
        self.loss_scale = loss_scale
        self.dls_downscale = dls_downscale
        self.dls_upscale = dls_upscale
        self.dls_upscale_interval = dls_upscale_interval
        self.world_size = utils.get_world_size()
        self.fp16_model = fp16_model

    def step(self, loss, optimizer, scheduler, update=True):
        """Performs one step of the optimizer. Applies loss scaling, computes gradients in fp16, converts gradients to fp32, inverts scaling and applies optional gradient norm clipping. If gradients are finite, it applies update to fp32 master weights and copies updated parameters to fp16 model for the next iteration. If gradients are not finite, it skips the batch and adjusts scaling factor for the next iteration. :param loss: value of loss function :param optimizer: optimizer :param update: if True executes weight update"""
        scaling_factor = self.loss_scale * self.world_size
        optimizer.set_global_scale(scaling_factor)
        loss *= self.loss_scale
        loss.backward()
        optimizer.complete_reductions()
        if not update:
            torch.cuda.synchronize()
            return
        norm = optimizer.L2_grad_norm / scaling_factor
        should_update = math.isfinite(norm)
        if should_update:
            if scheduler is not None:
                scheduler.step()
            optimizer.step(skip_overflow_check=True)
        if should_update:
            self.since_last_invalid += 1
        else:
            self.loss_scale /= self.dls_downscale
            self.since_last_invalid = 0
            logging.info(f'Gradient norm: {norm}')
            logging.info(f'Skipped batch, new scale: {self.loss_scale}')
        if self.since_last_invalid >= self.dls_upscale_interval:
            self.loss_scale *= self.dls_upscale
            self.loss_scale = min(self.loss_scale, 8192.0)
            logging.info(f'Upscaling, new scale: {self.loss_scale}')
            self.since_last_invalid = 0
        for p in self.fp16_model.parameters():
            p.grad = None


fit

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

Class description:
Implement the Trainer class.

Method signatures and docstrings:
- def __init__(self, start_train, end_train, custom_loss_function=None, **kwargs): Model trainer object. Args: custom_loss_function(function or None): If not None, we use this loss function to train the model.
- def build_train_set(self, data: DataLoader): Build the training set, the labels Args: data (DataLoader): data object containing all relevant tables Returns: train (pd.DataFrame): Train set including target labels x_train : train set in numpy array y_train: labels
- def fit(self, data: DataLoader, regressor=None, parameters=None): Build the training set, the labels and train th model Args: data (DataLoader): data object containing all relevant tables Returns: model (ModelFactory): Trained model train (pd.DataFrame): Train set including target labels

Implement the Python class `Trainer` described below.

Class description:
Implement the Trainer class.

Method signatures and docstrings:
- def __init__(self, start_train, end_train, custom_loss_function=None, **kwargs): Model trainer object. Args: custom_loss_function(function or None): If not None, we use this loss function to train the model.
- def build_train_set(self, data: DataLoader): Build the training set, the labels Args: data (DataLoader): data object containing all relevant tables Returns: train (pd.DataFrame): Train set including target labels x_train : train set in numpy array y_train: labels
- def fit(self, data: DataLoader, regressor=None, parameters=None): Build the training set, the labels and train th model Args: data (DataLoader): data object containing all relevant tables Returns: model (ModelFactory): Trained model train (pd.DataFrame): Train set including target labels

<|skeleton|>
class Trainer:

    def __init__(self, start_train, end_train, custom_loss_function=None, **kwargs):
        """Model trainer object. Args: custom_loss_function(function or None): If not None, we use this loss function to train the model."""
        <|body_0|>

    def build_train_set(self, data: DataLoader):
        """Build the training set, the labels Args: data (DataLoader): data object containing all relevant tables Returns: train (pd.DataFrame): Train set including target labels x_train : train set in numpy array y_train: labels"""
        <|body_1|>

    def fit(self, data: DataLoader, regressor=None, parameters=None):
        """Build the training set, the labels and train th model Args: data (DataLoader): data object containing all relevant tables Returns: model (ModelFactory): Trained model train (pd.DataFrame): Train set including target labels"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(**kwargs)
        self.custom_loss_function = custom_loss_function
        self.start_train = start_train
        self.end_train = end_train
<|end_body_0|>

<|body_start_1|>
        dwp = create_list_period(self.start_train, self.end_train, self.is_weekly_forecast)
        dtp = get_all_datehorizons(dwp, self.forecasting_horizons, self.is_weekly_forecast)
        Logger.info('Scope data used to train model (dates_to_predict)', '[%d, %d]' % (min(dtp), max(dtp)), self.__class__.__name__)
        if self.is_sell_in_model:
            feature_creator = FeatureEngineeringSellIn(is_weekly_forecast=self.is_weekly_forecast, granularity=self.granularity, feature_controller=self.feature_controller)
        else:
            feature_creator = FeatureEngineeringSellOut(is_weekly_forecast=self.is_weekly_forecast, granularity=self.granularity, feature_controller=self.feature_controller)
        train = feature_creator.build_train(data, dwp, dtp)
        x_train = train[Config.find_relevant_feature_columns(train)]
        y_train = train[n.FIELD_LABEL]
        return (feature_creator, train, x_train, y_train)
<|end_body_1|>

<|body_start_2|>
        feature_creator, train, x_train, y_train = self.build_train_set(data)
        model = ModelFactory(regressor=regressor, parameters=parameters, feature_creator=feature_creator, use_light_regressor=self.use_light_regressor, custom_loss_function=self.custom_loss_function)
        model.train_model(x_train, y_train)
        return (model, train)
<|end_body_2|>

Solution:
class Trainer:
    def __init__(self, start_train, end_train, custom_loss_function=None, **kwargs):
        """Model trainer object. Args: custom_loss_function(function or None): If not None, we use this loss function to train the model."""
        super().__init__(**kwargs)
        self.custom_loss_function = custom_loss_function
        self.start_train = start_train
        self.end_train = end_train

    def build_train_set(self, data: DataLoader):
        """Build the training set, the labels Args: data (DataLoader): data object containing all relevant tables Returns: train (pd.DataFrame): Train set including target labels x_train : train set in numpy array y_train: labels"""
        dwp = create_list_period(self.start_train, self.end_train, self.is_weekly_forecast)
        dtp = get_all_datehorizons(dwp, self.forecasting_horizons, self.is_weekly_forecast)
        Logger.info('Scope data used to train model (dates_to_predict)', '[%d, %d]' % (min(dtp), max(dtp)), self.__class__.__name__)
        if self.is_sell_in_model:
            feature_creator = FeatureEngineeringSellIn(is_weekly_forecast=self.is_weekly_forecast, granularity=self.granularity, feature_controller=self.feature_controller)
        else:
            feature_creator = FeatureEngineeringSellOut(is_weekly_forecast=self.is_weekly_forecast, granularity=self.granularity, feature_controller=self.feature_controller)
        train = feature_creator.build_train(data, dwp, dtp)
        x_train = train[Config.find_relevant_feature_columns(train)]
        y_train = train[n.FIELD_LABEL]
        return (feature_creator, train, x_train, y_train)

    def fit(self, data: DataLoader, regressor=None, parameters=None):
        """Build the training set, the labels and train th model Args: data (DataLoader): data object containing all relevant tables Returns: model (ModelFactory): Trained model train (pd.DataFrame): Train set including target labels"""
        feature_creator, train, x_train, y_train = self.build_train_set(data)
        model = ModelFactory(regressor=regressor, parameters=parameters, feature_creator=feature_creator, use_light_regressor=self.use_light_regressor, custom_loss_function=self.custom_loss_function)
        model.train_model(x_train, y_train)
        return (model, train)