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

Class description:
Mixin class for accessing netCDF unlimited dimensions. .. versionadded:: 1.7.0

Method signatures and docstrings:
- def nc_unlimited_dimensions(self): Return the selection of domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_clear_unlimited_dimensions`, `nc_set_unlimited_dimensions` :Returns: `set` The selection of domain axis constructs to be written as netCDF unlimited dimensions. **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f.nc_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_clear_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_unlimited_dimensions() set()
- def nc_set_unlimited_dimensions(self, axes): Select domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_unlimited_dimensions`, `nc_clear_unlimited_dimensions` :Parameters: axes: sequence of `str`, optional Select the domain axis constructs from the sequence provided. Domain axis constructs are identified by their construct identifiers. *Parameter example:* ``axes=['domainaxis0', 'domainaxis1']`` *Parameter example:* ``axes=()`` :Returns: `None` **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f
- def nc_clear_unlimited_dimensions(self): Remove the selection of domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_unlimited_dimensions`, `nc_set_unlimited_dimensions` :Returns: `set` The selection of domain axis constructs that has been removed. **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f.nc_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_clear_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_unlimited_dimensions() set()

Implement the Python class `NetCDFUnlimitedDimensions` described below.

Class description:
Mixin class for accessing netCDF unlimited dimensions. .. versionadded:: 1.7.0

Method signatures and docstrings:
- def nc_unlimited_dimensions(self): Return the selection of domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_clear_unlimited_dimensions`, `nc_set_unlimited_dimensions` :Returns: `set` The selection of domain axis constructs to be written as netCDF unlimited dimensions. **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f.nc_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_clear_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_unlimited_dimensions() set()
- def nc_set_unlimited_dimensions(self, axes): Select domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_unlimited_dimensions`, `nc_clear_unlimited_dimensions` :Parameters: axes: sequence of `str`, optional Select the domain axis constructs from the sequence provided. Domain axis constructs are identified by their construct identifiers. *Parameter example:* ``axes=['domainaxis0', 'domainaxis1']`` *Parameter example:* ``axes=()`` :Returns: `None` **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f
- def nc_clear_unlimited_dimensions(self): Remove the selection of domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_unlimited_dimensions`, `nc_set_unlimited_dimensions` :Returns: `set` The selection of domain axis constructs that has been removed. **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f.nc_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_clear_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_unlimited_dimensions() set()

<|skeleton|>
class NetCDFUnlimitedDimensions:
    """Mixin class for accessing netCDF unlimited dimensions. .. versionadded:: 1.7.0"""

    def nc_unlimited_dimensions(self):
        """Return the selection of domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_clear_unlimited_dimensions`, `nc_set_unlimited_dimensions` :Returns: `set` The selection of domain axis constructs to be written as netCDF unlimited dimensions. **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f.nc_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_clear_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_unlimited_dimensions() set()"""
        <|body_0|>

    def nc_set_unlimited_dimensions(self, axes):
        """Select domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_unlimited_dimensions`, `nc_clear_unlimited_dimensions` :Parameters: axes: sequence of `str`, optional Select the domain axis constructs from the sequence provided. Domain axis constructs are identified by their construct identifiers. *Parameter example:* ``axes=['domainaxis0', 'domainaxis1']`` *Parameter example:* ``axes=()`` :Returns: `None` **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f"""
        <|body_1|>

    def nc_clear_unlimited_dimensions(self):
        """Remove the selection of domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_unlimited_dimensions`, `nc_set_unlimited_dimensions` :Returns: `set` The selection of domain axis constructs that has been removed. **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f.nc_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_clear_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_unlimited_dimensions() set()"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        out = self._get_component('netcdf').get('unlimited_dimensions')
        if out is None:
            return set()
        return set(out)
<|end_body_0|>

<|body_start_1|>
        out = self._get_component('netcdf').get('unlimited_dimensions')
        if out is None:
            out = set()
        else:
            out = set(out)
        out.update(axes)
        self._get_component('netcdf')['unlimited_dimensions'] = tuple(out)
<|end_body_1|>

<|body_start_2|>
        out = self._get_component('netcdf').get('unlimited_dimensions')
        if out is None:
            out = set()
        else:
            out = set(out)
        self._get_component('netcdf')['unlimited_dimensions'] = ()
        return out
<|end_body_2|>

Solution:
class NetCDFUnlimitedDimensions:
    """Mixin class for accessing netCDF unlimited dimensions. .. versionadded:: 1.7.0"""

    def nc_unlimited_dimensions(self):
        """Return the selection of domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_clear_unlimited_dimensions`, `nc_set_unlimited_dimensions` :Returns: `set` The selection of domain axis constructs to be written as netCDF unlimited dimensions. **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f.nc_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_clear_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_unlimited_dimensions() set()"""
        out = self._get_component('netcdf').get('unlimited_dimensions')
        if out is None:
            return set()
        return set(out)

    def nc_set_unlimited_dimensions(self, axes):
        """Select domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_unlimited_dimensions`, `nc_clear_unlimited_dimensions` :Parameters: axes: sequence of `str`, optional Select the domain axis constructs from the sequence provided. Domain axis constructs are identified by their construct identifiers. *Parameter example:* ``axes=['domainaxis0', 'domainaxis1']`` *Parameter example:* ``axes=()`` :Returns: `None` **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f"""
        out = self._get_component('netcdf').get('unlimited_dimensions')
        if out is None:
            out = set()
        else:
            out = set(out)
        out.update(axes)
        self._get_component('netcdf')['unlimited_dimensions'] = tuple(out)

    def nc_clear_unlimited_dimensions(self):
        """Remove the selection of domain axis constructs to be written as netCDF unlimited dimensions. By default output netCDF dimensions are not unlimited. .. versionadded:: 1.7.0 .. seealso:: `cfdm.write`, `nc_unlimited_dimensions`, `nc_set_unlimited_dimensions` :Returns: `set` The selection of domain axis constructs that has been removed. **Examples:** >>> f.nc_set_unlimited_dimensions(['domainaxis0']) >>> f.nc_unlimited_dimensions() {'domainaxis0'} >>> f.nc_set_unlimited_dimensions(['domainaxis1']) >>> f.nc_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_clear_unlimited_dimensions() {'domainaxis0', 'domainaxis1'} >>> f.nc_unlimited_dimensions() set()"""
        out = self._get_component('netcdf').get('unlimited_dimensions')
        if out is None:
            out = set()
        else:
            out = set(out)
        self._get_component('netcdf')['unlimited_dimensions'] = ()
        return out


split

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

Class description:
Each triangle is made up of three lines. When the triangle is split, the lines have to split, too, and they have to keep references to their respective segments, or otherwise gaps will form between adjacent triangles.

Method signatures and docstrings:
- def __init__(self, s, t): Create Line instance. s and t are expected as complex.
- def split(self): Split line into two segments. The split happens roughly in the middle, but with some noise applied determined by FACTOR. The segments are lazily created and then reused.

Implement the Python class `Line` described below.

Class description:
Each triangle is made up of three lines. When the triangle is split, the lines have to split, too, and they have to keep references to their respective segments, or otherwise gaps will form between adjacent triangles.

Method signatures and docstrings:
- def __init__(self, s, t): Create Line instance. s and t are expected as complex.
- def split(self): Split line into two segments. The split happens roughly in the middle, but with some noise applied determined by FACTOR. The segments are lazily created and then reused.

<|skeleton|>
class Line:
    """Each triangle is made up of three lines. When the triangle is split, the lines have to split, too, and they have to keep references to their respective segments, or otherwise gaps will form between adjacent triangles."""

    def __init__(self, s, t):
        """Create Line instance. s and t are expected as complex."""
        <|body_0|>

    def split(self):
        """Split line into two segments. The split happens roughly in the middle, but with some noise applied determined by FACTOR. The segments are lazily created and then reused."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.s = s
        self.t = t
        self.left = self.right = None
<|end_body_0|>

<|body_start_1|>
        if self.left is None and self.right is None:
            length = abs(self.s - self.t)
            midpoint = (self.s + self.t) / 2
            p = complex(random.gauss(midpoint.real, length * FACTOR), random.gauss(midpoint.imag, length * FACTOR))
            self.left = Line(self.s, p)
            self.right = Line(p, self.t)
        return (self.left, self.right)
<|end_body_1|>

Solution:
class Line:
    """Each triangle is made up of three lines. When the triangle is split, the lines have to split, too, and they have to keep references to their respective segments, or otherwise gaps will form between adjacent triangles."""

    def __init__(self, s, t):
        """Create Line instance. s and t are expected as complex."""
        self.s = s
        self.t = t
        self.left = self.right = None

    def split(self):
        """Split line into two segments. The split happens roughly in the middle, but with some noise applied determined by FACTOR. The segments are lazily created and then reused."""
        if self.left is None and self.right is None:
            length = abs(self.s - self.t)
            midpoint = (self.s + self.t) / 2
            p = complex(random.gauss(midpoint.real, length * FACTOR), random.gauss(midpoint.imag, length * FACTOR))
            self.left = Line(self.s, p)
            self.right = Line(p, self.t)
        return (self.left, self.right)


get

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

Class description:
Show all Ballots for a Committee This view returns a list of all Ballots within a Committee. The queryset returned is defined by the requesting user's status: is_authenticated and or is a member of the Committee

Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Access URL parameters We need to define self.committee in order to return the correct set of Ballot objects :param request: Request object :type request: HttpRequestObject :param args: None :param kwargs: (django dict) :type kwargs: dict
- def get_context_data(self, **kwargs): Add to the context We need to add the Committee object to the context :param kwargs: (django dict) :type kwargs: dict
- def get_queryset(self): Specify the queryset Return a specific queryset based on the requesting user's status :return: If user.is_authenticated and a member of the Committee: All public Ballots, both open and closed. If not user.is_authenticated: All public ballots, both open and closed :rtype: QuerySet

Implement the Python class `BallotListView` described below.

Class description:
Show all Ballots for a Committee This view returns a list of all Ballots within a Committee. The queryset returned is defined by the requesting user's status: is_authenticated and or is a member of the Committee

Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Access URL parameters We need to define self.committee in order to return the correct set of Ballot objects :param request: Request object :type request: HttpRequestObject :param args: None :param kwargs: (django dict) :type kwargs: dict
- def get_context_data(self, **kwargs): Add to the context We need to add the Committee object to the context :param kwargs: (django dict) :type kwargs: dict
- def get_queryset(self): Specify the queryset Return a specific queryset based on the requesting user's status :return: If user.is_authenticated and a member of the Committee: All public Ballots, both open and closed. If not user.is_authenticated: All public ballots, both open and closed :rtype: QuerySet

<|skeleton|>
class BallotListView:
    """Show all Ballots for a Committee This view returns a list of all Ballots within a Committee. The queryset returned is defined by the requesting user's status: is_authenticated and or is a member of the Committee"""

    def get(self, request, *args, **kwargs):
        """Access URL parameters We need to define self.committee in order to return the correct set of Ballot objects :param request: Request object :type request: HttpRequestObject :param args: None :param kwargs: (django dict) :type kwargs: dict"""
        <|body_0|>

    def get_context_data(self, **kwargs):
        """Add to the context We need to add the Committee object to the context :param kwargs: (django dict) :type kwargs: dict"""
        <|body_1|>

    def get_queryset(self):
        """Specify the queryset Return a specific queryset based on the requesting user's status :return: If user.is_authenticated and a member of the Committee: All public Ballots, both open and closed. If not user.is_authenticated: All public ballots, both open and closed :rtype: QuerySet"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        committee_slug = self.kwargs.get('committee_slug')
        project_slug = self.kwargs.get('project_slug')
        self.project = Project.objects.get(slug=project_slug)
        self.committee = Committee.objects.filter(project=self.project).get(slug=committee_slug)
        self.is_member = False
        if request.user in self.committee.users.all():
            self.is_member = True
        return super(BallotListView, self).get(request, *args, **kwargs)
<|end_body_0|>

<|body_start_1|>
        context = super(BallotListView, self).get_context_data(**kwargs)
        context['committee'] = self.committee
        context['is_member'] = self.is_member
        return context
<|end_body_1|>

<|body_start_2|>
        if self.request.user.is_authenticated() and self.is_member:
            qs = Ballot.objects.filter(committee=self.committee)
        else:
            qs = Ballot.objects.filter(committee=self.committee).filter(private=False)
        return qs
<|end_body_2|>

Solution:
class BallotListView:
    """Show all Ballots for a Committee This view returns a list of all Ballots within a Committee. The queryset returned is defined by the requesting user's status: is_authenticated and or is a member of the Committee"""

    def get(self, request, *args, **kwargs):
        """Access URL parameters We need to define self.committee in order to return the correct set of Ballot objects :param request: Request object :type request: HttpRequestObject :param args: None :param kwargs: (django dict) :type kwargs: dict"""
        committee_slug = self.kwargs.get('committee_slug')
        project_slug = self.kwargs.get('project_slug')
        self.project = Project.objects.get(slug=project_slug)
        self.committee = Committee.objects.filter(project=self.project).get(slug=committee_slug)
        self.is_member = False
        if request.user in self.committee.users.all():
            self.is_member = True
        return super(BallotListView, self).get(request, *args, **kwargs)

    def get_context_data(self, **kwargs):
        """Add to the context We need to add the Committee object to the context :param kwargs: (django dict) :type kwargs: dict"""
        context = super(BallotListView, self).get_context_data(**kwargs)
        context['committee'] = self.committee
        context['is_member'] = self.is_member
        return context

    def get_queryset(self):
        """Specify the queryset Return a specific queryset based on the requesting user's status :return: If user.is_authenticated and a member of the Committee: All public Ballots, both open and closed. If not user.is_authenticated: All public ballots, both open and closed :rtype: QuerySet"""
        if self.request.user.is_authenticated() and self.is_member:
            qs = Ballot.objects.filter(committee=self.committee)
        else:
            qs = Ballot.objects.filter(committee=self.committee).filter(private=False)
        return qs


for df in dfs:
    df.persist()
    df.count()

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

Class description:
Define common methods for pyspark profiler.

Method signatures and docstrings:
- def _wrap_fit_transform(self) -> Callable: Wrapper function to call `count()` on wrangler's `fit_transform` to enforce computation on lazily evaluated pyspark dataframes. Returns ------- wrapped: callable Wrapped `fit_transform` method as a function.
- def _cache_input(dfs: Iterable[DataFrame]): Persist lazily evaluated pyspark dataframes before profiling to capture only relevant `fit_transform`. Apply `count` to enforce computation to create cached representation. Parameters ---------- dfs: iterable Spark dataframes to be persisted. Returns ------- persisted: iterable List of computed dask collections.
- def _clear_cached_input(dfs: Iterable[DataFrame]): Unpersist previously persisted pyspark dataframes after profiling. Parameters ---------- dfs: iterable Persisted pyspark dataframes.

Implement the Python class `PySparkBaseProfiler` described below.

Class description:
Define common methods for pyspark profiler.

Method signatures and docstrings:
- def _wrap_fit_transform(self) -> Callable: Wrapper function to call `count()` on wrangler's `fit_transform` to enforce computation on lazily evaluated pyspark dataframes. Returns ------- wrapped: callable Wrapped `fit_transform` method as a function.
- def _cache_input(dfs: Iterable[DataFrame]): Persist lazily evaluated pyspark dataframes before profiling to capture only relevant `fit_transform`. Apply `count` to enforce computation to create cached representation. Parameters ---------- dfs: iterable Spark dataframes to be persisted. Returns ------- persisted: iterable List of computed dask collections.
- def _clear_cached_input(dfs: Iterable[DataFrame]): Unpersist previously persisted pyspark dataframes after profiling. Parameters ---------- dfs: iterable Persisted pyspark dataframes.

<|skeleton|>
class PySparkBaseProfiler:
    """Define common methods for pyspark profiler."""

    def _wrap_fit_transform(self) -> Callable:
        """Wrapper function to call `count()` on wrangler's `fit_transform` to enforce computation on lazily evaluated pyspark dataframes. Returns ------- wrapped: callable Wrapped `fit_transform` method as a function."""
        <|body_0|>

    def _cache_input(dfs: Iterable[DataFrame]):
        """Persist lazily evaluated pyspark dataframes before profiling to capture only relevant `fit_transform`. Apply `count` to enforce computation to create cached representation. Parameters ---------- dfs: iterable Spark dataframes to be persisted. Returns ------- persisted: iterable List of computed dask collections."""
        <|body_1|>

    def _clear_cached_input(dfs: Iterable[DataFrame]):
        """Unpersist previously persisted pyspark dataframes after profiling. Parameters ---------- dfs: iterable Persisted pyspark dataframes."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        def wrapped(*args, **kwargs):
            return self.wrangler.fit_transform(*args, **kwargs).count()
        return wrapped
<|end_body_0|>

<|body_start_1|>
        for df in dfs:
            df.persist()
            df.count()
<|end_body_1|>

<|body_start_2|>
        for df in dfs:
            df.unpersist()
            if df.is_cached:
                warnings.warn('Spark dataframe could not be unpersisted.', ResourceWarning)
<|end_body_2|>

Solution:
class PySparkBaseProfiler:
    """Define common methods for pyspark profiler."""

    def _wrap_fit_transform(self) -> Callable:
        """Wrapper function to call `count()` on wrangler's `fit_transform` to enforce computation on lazily evaluated pyspark dataframes. Returns ------- wrapped: callable Wrapped `fit_transform` method as a function."""
        def wrapped(*args, **kwargs):
            return self.wrangler.fit_transform(*args, **kwargs).count()
        return wrapped

    def _cache_input(dfs: Iterable[DataFrame]):
        """Persist lazily evaluated pyspark dataframes before profiling to capture only relevant `fit_transform`. Apply `count` to enforce computation to create cached representation. Parameters ---------- dfs: iterable Spark dataframes to be persisted. Returns ------- persisted: iterable List of computed dask collections."""
        for df in dfs:
            df.persist()
            df.count()

    def _clear_cached_input(dfs: Iterable[DataFrame]):
        """Unpersist previously persisted pyspark dataframes after profiling. Parameters ---------- dfs: iterable Persisted pyspark dataframes."""
        for df in dfs:
            df.unpersist()
            if df.is_cached:
                warnings.warn('Spark dataframe could not be unpersisted.', ResourceWarning)


create

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

Class description:
Manage VideoSessions in the Power Reg system

Method signatures and docstrings:
- def __init__(self): constructor
- def create(self, auth_token, assignment): Create a new VideoSession object. @param auth_token The authentication token of the acting user @type auth_token facade.models.AuthToken @param assignment FK for an assignment @type assignment int @return A dictionary with two indices. One is 'id' and is the primary key of the VideoSession object. The other is 'urls', and is a list of URLs that the user is authorized to view under this VideoSession.
- def register_video_view(self, auth_token, video_id): Create an assignment to watch the video, if needed; then use it to create a VideoSession object, if needed. If a VideoSession for this (user, video) combination was created in the last 12 hours, does nothing. @param auth_token The authentication token of the acting user @type auth_token facade.models.AuthToken @param assignment FK for a video @type assignment int
- def watcher_report(self, auth_token, videos, start_date=None, end_date=None): Returns a list of views of the given videos (optinally filtered by date) along with some information about the viewer.
- def viewing_activity_report(self, auth_token, users, start_date=None, end_date=None): Returns a list of video views by the given users (optinally filtered by date) along with some information about the video.

Implement the Python class `VideoSessionManager` described below.

Class description:
Manage VideoSessions in the Power Reg system

Method signatures and docstrings:
- def __init__(self): constructor
- def create(self, auth_token, assignment): Create a new VideoSession object. @param auth_token The authentication token of the acting user @type auth_token facade.models.AuthToken @param assignment FK for an assignment @type assignment int @return A dictionary with two indices. One is 'id' and is the primary key of the VideoSession object. The other is 'urls', and is a list of URLs that the user is authorized to view under this VideoSession.
- def register_video_view(self, auth_token, video_id): Create an assignment to watch the video, if needed; then use it to create a VideoSession object, if needed. If a VideoSession for this (user, video) combination was created in the last 12 hours, does nothing. @param auth_token The authentication token of the acting user @type auth_token facade.models.AuthToken @param assignment FK for a video @type assignment int
- def watcher_report(self, auth_token, videos, start_date=None, end_date=None): Returns a list of views of the given videos (optinally filtered by date) along with some information about the viewer.
- def viewing_activity_report(self, auth_token, users, start_date=None, end_date=None): Returns a list of video views by the given users (optinally filtered by date) along with some information about the video.

<|skeleton|>
class VideoSessionManager:
    """Manage VideoSessions in the Power Reg system"""

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

    def create(self, auth_token, assignment):
        """Create a new VideoSession object. @param auth_token The authentication token of the acting user @type auth_token facade.models.AuthToken @param assignment FK for an assignment @type assignment int @return A dictionary with two indices. One is 'id' and is the primary key of the VideoSession object. The other is 'urls', and is a list of URLs that the user is authorized to view under this VideoSession."""
        <|body_1|>

    def register_video_view(self, auth_token, video_id):
        """Create an assignment to watch the video, if needed; then use it to create a VideoSession object, if needed. If a VideoSession for this (user, video) combination was created in the last 12 hours, does nothing. @param auth_token The authentication token of the acting user @type auth_token facade.models.AuthToken @param assignment FK for a video @type assignment int"""
        <|body_2|>

    def watcher_report(self, auth_token, videos, start_date=None, end_date=None):
        """Returns a list of views of the given videos (optinally filtered by date) along with some information about the viewer."""
        <|body_3|>

    def viewing_activity_report(self, auth_token, users, start_date=None, end_date=None):
        """Returns a list of video views by the given users (optinally filtered by date) along with some information about the video."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        super(VideoSessionManager, self).__init__()
        self.getters.update({'assignment': 'get_foreign_key', 'date_completed': 'get_time', 'date_started': 'get_time', 'user': 'get_foreign_key', 'video': 'get_foreign_key'})
        self.setters.update({'date_completed': 'set_time', 'date_started': 'set_time'})
        self.my_django_model = facade.models.VideoSession
<|end_body_0|>

<|body_start_1|>
        assignment_object = self._find_by_id(assignment, facade.models.Assignment)
        new_video_session = self.my_django_model(assignment=assignment_object)
        new_video_session.save()
        self.authorizer.check_create_permissions(auth_token, new_video_session)
        ret = {}
        ret['id'] = new_video_session.id
        ret['urls'] = list()
        for encoded_video in new_video_session.video.encoded_videos.all():
            ret['urls'].append(encoded_video.url)
        return ret
<|end_body_1|>

<|body_start_2|>
        video = facade.models.Video.objects.get(id=video_id)
        assignments = facade.models.Assignment.objects.filter(task__id=video_id, user__id=auth_token.user.id).order_by('-id')
        if len(assignments):
            assignment = assignments[0]
        else:
            assignment = facade.managers.AssignmentManager().create(auth_token, video_id)
        start_cutoff = datetime.utcnow() - timedelta(hours=12)
        attempts = facade.models.AssignmentAttempt.objects.filter(assignment__id=assignment.id, date_started__gt=start_cutoff).order_by('-date_started')
        if len(attempts):
            attempt = attempts[0]
        else:
            attempt = self.my_django_model.objects.create(assignment=assignment)
            self.authorizer.check_create_permissions(auth_token, attempt)
        return {'id': attempt.id}
<|end_body_2|>

<|body_start_3|>
        filters = {'member': {'assignment__task__id': videos}}
        if start_date or end_date:
            filters = [filters]
            if start_date:
                if pr_time.is_iso8601(start_date):
                    start_date = pr_time.iso8601_to_datetime(start_date)
                filters.append({'greater_than_or_equal': {'date_started': start_date}})
            if end_date:
                if pr_time.is_iso8601(end_date):
                    end_date = pr_time.iso8601_to_datetime(end_date)
                filters.append({'less_than_or_equal': {'date_started': end_date}})
            filters = {'and': filters}
        views = self.get_filtered(auth_token, filters, ['video', 'date_started', 'user'])
        views = Utils.merge_queries(views, facade.managers.VideoManager(), auth_token, ['name'], 'video')
        views = Utils.merge_queries(views, facade.managers.UserManager(), auth_token, ['first_name', 'last_name', 'email', 'default_username_and_domain'], 'user')
        return views
<|end_body_3|>

<|body_start_4|>
        filters = {'member': {'assignment__user__id': users}}
        if start_date or end_date:
            filters = [filters]
            if start_date:
                if pr_time.is_iso8601(start_date):
                    start_date = pr_time.iso8601_to_datetime(start_date)
                filters.append({'greater_than_or_equal': {'date_started': start_date}})
            if end_date:
                if pr_time.is_iso8601(end_date):
                    end_date = pr_time.iso8601_to_datetime(end_date)
                filters.append({'less_than_or_equal': {'date_started': end_date}})
            filters = {'and': filters}
        views = self.get_filtered(auth_token, filters, ['video', 'date_started', 'user'])
        views = Utils.merge_queries(views, facade.managers.VideoManager(), auth_token, ['name', 'author', 'description'], 'video')
        views = Utils.merge_queries(views, facade.managers.UserManager(), auth_token, ['first_name', 'last_name', 'email', 'username'], 'user')
        return views
<|end_body_4|>

Solution:
class VideoSessionManager:
    """Manage VideoSessions in the Power Reg system"""

    def __init__(self):
        """constructor"""
        super(VideoSessionManager, self).__init__()
        self.getters.update({'assignment': 'get_foreign_key', 'date_completed': 'get_time', 'date_started': 'get_time', 'user': 'get_foreign_key', 'video': 'get_foreign_key'})
        self.setters.update({'date_completed': 'set_time', 'date_started': 'set_time'})
        self.my_django_model = facade.models.VideoSession

    def create(self, auth_token, assignment):
        """Create a new VideoSession object. @param auth_token The authentication token of the acting user @type auth_token facade.models.AuthToken @param assignment FK for an assignment @type assignment int @return A dictionary with two indices. One is 'id' and is the primary key of the VideoSession object. The other is 'urls', and is a list of URLs that the user is authorized to view under this VideoSession."""
        assignment_object = self._find_by_id(assignment, facade.models.Assignment)
        new_video_session = self.my_django_model(assignment=assignment_object)
        new_video_session.save()
        self.authorizer.check_create_permissions(auth_token, new_video_session)
        ret = {}
        ret['id'] = new_video_session.id
        ret['urls'] = list()
        for encoded_video in new_video_session.video.encoded_videos.all():
            ret['urls'].append(encoded_video.url)
        return ret

    def register_video_view(self, auth_token, video_id):
        """Create an assignment to watch the video, if needed; then use it to create a VideoSession object, if needed. If a VideoSession for this (user, video) combination was created in the last 12 hours, does nothing. @param auth_token The authentication token of the acting user @type auth_token facade.models.AuthToken @param assignment FK for a video @type assignment int"""
        video = facade.models.Video.objects.get(id=video_id)
        assignments = facade.models.Assignment.objects.filter(task__id=video_id, user__id=auth_token.user.id).order_by('-id')
        if len(assignments):
            assignment = assignments[0]
        else:
            assignment = facade.managers.AssignmentManager().create(auth_token, video_id)
        start_cutoff = datetime.utcnow() - timedelta(hours=12)
        attempts = facade.models.AssignmentAttempt.objects.filter(assignment__id=assignment.id, date_started__gt=start_cutoff).order_by('-date_started')
        if len(attempts):
            attempt = attempts[0]
        else:
            attempt = self.my_django_model.objects.create(assignment=assignment)
            self.authorizer.check_create_permissions(auth_token, attempt)
        return {'id': attempt.id}

    def watcher_report(self, auth_token, videos, start_date=None, end_date=None):
        """Returns a list of views of the given videos (optinally filtered by date) along with some information about the viewer."""
        filters = {'member': {'assignment__task__id': videos}}
        if start_date or end_date:
            filters = [filters]
            if start_date:
                if pr_time.is_iso8601(start_date):
                    start_date = pr_time.iso8601_to_datetime(start_date)
                filters.append({'greater_than_or_equal': {'date_started': start_date}})
            if end_date:
                if pr_time.is_iso8601(end_date):
                    end_date = pr_time.iso8601_to_datetime(end_date)
                filters.append({'less_than_or_equal': {'date_started': end_date}})
            filters = {'and': filters}
        views = self.get_filtered(auth_token, filters, ['video', 'date_started', 'user'])
        views = Utils.merge_queries(views, facade.managers.VideoManager(), auth_token, ['name'], 'video')
        views = Utils.merge_queries(views, facade.managers.UserManager(), auth_token, ['first_name', 'last_name', 'email', 'default_username_and_domain'], 'user')
        return views

    def viewing_activity_report(self, auth_token, users, start_date=None, end_date=None):
        """Returns a list of video views by the given users (optinally filtered by date) along with some information about the video."""
        filters = {'member': {'assignment__user__id': users}}
        if start_date or end_date:
            filters = [filters]
            if start_date:
                if pr_time.is_iso8601(start_date):
                    start_date = pr_time.iso8601_to_datetime(start_date)
                filters.append({'greater_than_or_equal': {'date_started': start_date}})
            if end_date:
                if pr_time.is_iso8601(end_date):
                    end_date = pr_time.iso8601_to_datetime(end_date)
                filters.append({'less_than_or_equal': {'date_started': end_date}})
            filters = {'and': filters}
        views = self.get_filtered(auth_token, filters, ['video', 'date_started', 'user'])
        views = Utils.merge_queries(views, facade.managers.VideoManager(), auth_token, ['name', 'author', 'description'], 'video')
        views = Utils.merge_queries(views, facade.managers.UserManager(), auth_token, ['first_name', 'last_name', 'email', 'username'], 'user')
        return views


update

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

Class description:
Persistent query example

Method signatures and docstrings:
- def create(self, validated_data): Create and return a new `PersistentQueryExample` instance, given the validated data.
- def update(self, persistent_query_example, validated_data): Update and return an existing `PersistentQueryExample` instance, given the validated data.

Implement the Python class `PersistentQueryExampleSerializer` described below.

Class description:
Persistent query example

Method signatures and docstrings:
- def create(self, validated_data): Create and return a new `PersistentQueryExample` instance, given the validated data.
- def update(self, persistent_query_example, validated_data): Update and return an existing `PersistentQueryExample` instance, given the validated data.

<|skeleton|>
class PersistentQueryExampleSerializer:
    """Persistent query example"""

    def create(self, validated_data):
        """Create and return a new `PersistentQueryExample` instance, given the validated data."""
        <|body_0|>

    def update(self, persistent_query_example, validated_data):
        """Update and return an existing `PersistentQueryExample` instance, given the validated data."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        persistent_query_example = PersistentQueryExample(user_id=str(self.context['request'].user.id), content=validated_data['content'] if 'content' in validated_data else None, name=validated_data['name'] if 'name' in validated_data else None)
        persistent_query_example_api.upsert(persistent_query_example, self.context['request'].user)
        if 'templates' in validated_data:
            persistent_query_example.templates.set(validated_data['templates'])
        return persistent_query_example
<|end_body_0|>

<|body_start_1|>
        persistent_query_example.content = validated_data.get('content', persistent_query_example.content)
        persistent_query_example.name = validated_data.get('name', persistent_query_example.name)
        persistent_query_example_api.upsert(persistent_query_example, self.context['request'].user)
        if 'templates' in validated_data:
            persistent_query_example.templates.set(validated_data['templates'])
        return persistent_query_example
<|end_body_1|>

Solution:
class PersistentQueryExampleSerializer:
    """Persistent query example"""

    def create(self, validated_data):
        """Create and return a new `PersistentQueryExample` instance, given the validated data."""
        persistent_query_example = PersistentQueryExample(user_id=str(self.context['request'].user.id), content=validated_data['content'] if 'content' in validated_data else None, name=validated_data['name'] if 'name' in validated_data else None)
        persistent_query_example_api.upsert(persistent_query_example, self.context['request'].user)
        if 'templates' in validated_data:
            persistent_query_example.templates.set(validated_data['templates'])
        return persistent_query_example

    def update(self, persistent_query_example, validated_data):
        """Update and return an existing `PersistentQueryExample` instance, given the validated data."""
        persistent_query_example.content = validated_data.get('content', persistent_query_example.content)
        persistent_query_example.name = validated_data.get('name', persistent_query_example.name)
        persistent_query_example_api.upsert(persistent_query_example, self.context['request'].user)
        if 'templates' in validated_data:
            persistent_query_example.templates.set(validated_data['templates'])
        return persistent_query_example


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

Class description:
Implement the TranslatorTest class.

Method signatures and docstrings:
- def test_empty_molecule(self): Test that we can safely return a blank identifier for an empty molecule.
- def test_failure_message(self, mock_logging): Test that we log the molecule adjlist upon failure.

Implement the Python class `TranslatorTest` described below.

Class description:
Implement the TranslatorTest class.

Method signatures and docstrings:
- def test_empty_molecule(self): Test that we can safely return a blank identifier for an empty molecule.
- def test_failure_message(self, mock_logging): Test that we log the molecule adjlist upon failure.

<|skeleton|>
class TranslatorTest:

    def test_empty_molecule(self):
        """Test that we can safely return a blank identifier for an empty molecule."""
        <|body_0|>

    def test_failure_message(self, mock_logging):
        """Test that we log the molecule adjlist upon failure."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        mol = Molecule()
        self.assertEqual(mol.to_smiles(), '')
        self.assertEqual(mol.to_inchi(), '')
<|end_body_0|>

<|body_start_1|>
        mol = Molecule(smiles='[CH2-][N+]#N')
        with self.assertRaisesRegex(ValueError, 'Unable to generate identifier type'):
            to_inchi(mol, backend='rdkit')
        mock_logging.error.assert_called_with('Unable to generate identifier for this molecule:\n{0}'.format(mol.to_adjacency_list()))
<|end_body_1|>

Solution:
class TranslatorTest:
    def test_empty_molecule(self):
        """Test that we can safely return a blank identifier for an empty molecule."""
        mol = Molecule()
        self.assertEqual(mol.to_smiles(), '')
        self.assertEqual(mol.to_inchi(), '')

    def test_failure_message(self, mock_logging):
        """Test that we log the molecule adjlist upon failure."""
        mol = Molecule(smiles='[CH2-][N+]#N')
        with self.assertRaisesRegex(ValueError, 'Unable to generate identifier type'):
            to_inchi(mol, backend='rdkit')
        mock_logging.error.assert_called_with('Unable to generate identifier for this molecule:\n{0}'.format(mol.to_adjacency_list()))


retranslateUi

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

Class description:
Implement the MemoriaVisoespaciaWidget class.

Method signatures and docstrings:
- def setupUi(self, Form): Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)
- def retranslateUi(self, Form): Método empleado paraasignar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)

Implement the Python class `MemoriaVisoespaciaWidget` described below.

Class description:
Implement the MemoriaVisoespaciaWidget class.

Method signatures and docstrings:
- def setupUi(self, Form): Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)
- def retranslateUi(self, Form): Método empleado paraasignar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)

<|skeleton|>
class MemoriaVisoespaciaWidget:

    def setupUi(self, Form):
        """Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)"""
        <|body_0|>

    def retranslateUi(self, Form):
        """Método empleado paraasignar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().setupUI(Form)
        self.label_4 = QtWidgets.QLabel(self.verticalLayoutWidget)
        self.label_4.setToolTip('')
        self.label_4.setAlignment(QtCore.Qt.AlignCenter)
        self.label_4.setObjectName('label_4')
        self.verticalLayout_2.addWidget(self.label_4)
        self.label_8 = QtWidgets.QLabel(self.verticalLayoutWidget)
        self.label_8.setObjectName('label_8')
        self.formLayout.setWidget(0, QtWidgets.QFormLayout.LabelRole, self.label_8)
        self.sbDenomImg = QtWidgets.QSpinBox(self.verticalLayoutWidget)
        self.sbDenomImg.setObjectName('sbDenomImg')
        self.sbDenomImg.setRange(0, 36)
        self.sbDenomImg.setSizePolicy(QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Fixed)
        self.formLayout.setWidget(0, QtWidgets.QFormLayout.FieldRole, self.sbDenomImg)
        self.label_9 = QtWidgets.QLabel(self.verticalLayoutWidget)
        self.label_9.setObjectName('label_9')
        self.formLayout.setWidget(1, QtWidgets.QFormLayout.LabelRole, self.label_9)
        self.sbDenomImgT = QtWidgets.QSpinBox(self.verticalLayoutWidget)
        self.sbDenomImgT.setObjectName('sbDenomImgT')
        self.sbDenomImgT.setRange(0, 12)
        self.sbDenomImgT.setSizePolicy(QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Fixed)
        self.formLayout.setWidget(1, QtWidgets.QFormLayout.FieldRole, self.sbDenomImgT)
        self.retranslateUi(Form)
        QtCore.QMetaObject.connectSlotsByName(Form)
<|end_body_0|>

<|body_start_1|>
        _translate = QtCore.QCoreApplication.translate
        Form.setWindowTitle(_translate('Form', 'Prueba Memoria Visoespacial'))
        self.label_4.setText(_translate('Form', 'Ingrese los datos de la prueba de Memoria Visoespacia'))
        self.label_8.setText(_translate('Form', 'Total recall: '))
        self.label_9.setText(_translate('Form', 'Delayed recall:'))
        self.pbStart.setText(_translate('Form', 'Registrar Prueba'))
        self.backButton.setText(_translate('Form', 'Regresar al Menu'))
<|end_body_1|>

Solution:
class MemoriaVisoespaciaWidget:
    def setupUi(self, Form):
        """Método empleado para especificar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)"""
        super().setupUI(Form)
        self.label_4 = QtWidgets.QLabel(self.verticalLayoutWidget)
        self.label_4.setToolTip('')
        self.label_4.setAlignment(QtCore.Qt.AlignCenter)
        self.label_4.setObjectName('label_4')
        self.verticalLayout_2.addWidget(self.label_4)
        self.label_8 = QtWidgets.QLabel(self.verticalLayoutWidget)
        self.label_8.setObjectName('label_8')
        self.formLayout.setWidget(0, QtWidgets.QFormLayout.LabelRole, self.label_8)
        self.sbDenomImg = QtWidgets.QSpinBox(self.verticalLayoutWidget)
        self.sbDenomImg.setObjectName('sbDenomImg')
        self.sbDenomImg.setRange(0, 36)
        self.sbDenomImg.setSizePolicy(QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Fixed)
        self.formLayout.setWidget(0, QtWidgets.QFormLayout.FieldRole, self.sbDenomImg)
        self.label_9 = QtWidgets.QLabel(self.verticalLayoutWidget)
        self.label_9.setObjectName('label_9')
        self.formLayout.setWidget(1, QtWidgets.QFormLayout.LabelRole, self.label_9)
        self.sbDenomImgT = QtWidgets.QSpinBox(self.verticalLayoutWidget)
        self.sbDenomImgT.setObjectName('sbDenomImgT')
        self.sbDenomImgT.setRange(0, 12)
        self.sbDenomImgT.setSizePolicy(QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Fixed)
        self.formLayout.setWidget(1, QtWidgets.QFormLayout.FieldRole, self.sbDenomImgT)
        self.retranslateUi(Form)
        QtCore.QMetaObject.connectSlotsByName(Form)

    def retranslateUi(self, Form):
        """Método empleado paraasignar el contenido de la Interfáz gráfica, es generado por pyuic5. Args: Form: Ventana en la que se deplegará la interfáz gráfica (es un tipo de dato QtWidget.QWidget)"""
        _translate = QtCore.QCoreApplication.translate
        Form.setWindowTitle(_translate('Form', 'Prueba Memoria Visoespacial'))
        self.label_4.setText(_translate('Form', 'Ingrese los datos de la prueba de Memoria Visoespacia'))
        self.label_8.setText(_translate('Form', 'Total recall: '))
        self.label_9.setText(_translate('Form', 'Delayed recall:'))
        self.pbStart.setText(_translate('Form', 'Registrar Prueba'))
        self.backButton.setText(_translate('Form', 'Regresar al Menu'))


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

Class description:
Implementation of the 'Payroll Main Paystatement Fields' model. TODO: type model description here. Attributes: pay_date (long|int): Pay date for a pay period start_date (long|int): Start date for a pay period end_date (long|int): End date for a pay period pay_period_hours (int): The sum total of the number of hours worked each week for a pay period. gross_pay_amount (float): Gross pay amount for a pay period gross_pay_ytd (float): The year-to-date (YTD) gross pay amount for an employer.

**Note**: This field is only included in the pay histories from the last period of the year where `lastPayPeriodIndicator` = true. net_pay_amount (float): Net pay amount for a pay period net_pay_ytd

Method signatures and docstrings:
- def __init__(self, pay_date=None, start_date=None, end_date=None, pay_period_hours=None, gross_pay_amount=None, gross_pay_ytd=None, net_pay_amount=None, net_pay_ytd=None, payroll_provider=None, pay_frequency=None, pay_type=None, additional_properties={}): Constructor for the PayrollMainPaystatementFields 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 `PayrollMainPaystatementFields` described below.

Class description:
Implementation of the 'Payroll Main Paystatement Fields' model. TODO: type model description here. Attributes: pay_date (long|int): Pay date for a pay period start_date (long|int): Start date for a pay period end_date (long|int): End date for a pay period pay_period_hours (int): The sum total of the number of hours worked each week for a pay period. gross_pay_amount (float): Gross pay amount for a pay period gross_pay_ytd (float): The year-to-date (YTD) gross pay amount for an employer.

**Note**: This field is only included in the pay histories from the last period of the year where `lastPayPeriodIndicator` = true. net_pay_amount (float): Net pay amount for a pay period net_pay_ytd

Method signatures and docstrings:
- def __init__(self, pay_date=None, start_date=None, end_date=None, pay_period_hours=None, gross_pay_amount=None, gross_pay_ytd=None, net_pay_amount=None, net_pay_ytd=None, payroll_provider=None, pay_frequency=None, pay_type=None, additional_properties={}): Constructor for the PayrollMainPaystatementFields 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 PayrollMainPaystatementFields:
    """Implementation of the 'Payroll Main Paystatement Fields' model. TODO: type model description here. Attributes: pay_date (long|int): Pay date for a pay period start_date (long|int): Start date for a pay period end_date (long|int): End date for a pay period pay_period_hours (int): The sum total of the number of hours worked each week for a pay period. gross_pay_amount (float): Gross pay amount for a pay period gross_pay_ytd (float): The year-to-date (YTD) gross pay amount for an employer.

**Note**: This field is only included in the pay histories from the last period of the year where `lastPayPeriodIndicator` = true. net_pay_amount (float): Net pay amount for a pay period net_pay_ytd"""

    def __init__(self, pay_date=None, start_date=None, end_date=None, pay_period_hours=None, gross_pay_amount=None, gross_pay_ytd=None, net_pay_amount=None, net_pay_ytd=None, payroll_provider=None, pay_frequency=None, pay_type=None, additional_properties={}):
        """Constructor for the PayrollMainPaystatementFields 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.pay_date = pay_date
        self.start_date = start_date
        self.end_date = end_date
        self.pay_period_hours = pay_period_hours
        self.gross_pay_amount = gross_pay_amount
        self.gross_pay_ytd = gross_pay_ytd
        self.net_pay_amount = net_pay_amount
        self.net_pay_ytd = net_pay_ytd
        self.payroll_provider = payroll_provider
        self.pay_frequency = pay_frequency
        self.pay_type = pay_type
        self.additional_properties = additional_properties
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        pay_date = dictionary.get('payDate')
        start_date = dictionary.get('startDate')
        end_date = dictionary.get('endDate')
        pay_period_hours = dictionary.get('payPeriodHours')
        gross_pay_amount = dictionary.get('grossPayAmount')
        gross_pay_ytd = dictionary.get('grossPayYTD')
        net_pay_amount = dictionary.get('netPayAmount')
        net_pay_ytd = dictionary.get('netPayYTD')
        payroll_provider = dictionary.get('payrollProvider')
        pay_frequency = dictionary.get('payFrequency')
        pay_type = dictionary.get('payType')
        for key in cls._names.values():
            if key in dictionary:
                del dictionary[key]
        return cls(pay_date, start_date, end_date, pay_period_hours, gross_pay_amount, gross_pay_ytd, net_pay_amount, net_pay_ytd, payroll_provider, pay_frequency, pay_type, dictionary)
<|end_body_1|>

Solution:
class PayrollMainPaystatementFields:
    """Implementation of the 'Payroll Main Paystatement Fields' model. TODO: type model description here. Attributes: pay_date (long|int): Pay date for a pay period start_date (long|int): Start date for a pay period end_date (long|int): End date for a pay period pay_period_hours (int): The sum total of the number of hours worked each week for a pay period. gross_pay_amount (float): Gross pay amount for a pay period gross_pay_ytd (float): The year-to-date (YTD) gross pay amount for an employer.

**Note**: This field is only included in the pay histories from the last period of the year where `lastPayPeriodIndicator` = true. net_pay_amount (float): Net pay amount for a pay period net_pay_ytd"""

    def __init__(self, pay_date=None, start_date=None, end_date=None, pay_period_hours=None, gross_pay_amount=None, gross_pay_ytd=None, net_pay_amount=None, net_pay_ytd=None, payroll_provider=None, pay_frequency=None, pay_type=None, additional_properties={}):
        """Constructor for the PayrollMainPaystatementFields class"""
        self.pay_date = pay_date
        self.start_date = start_date
        self.end_date = end_date
        self.pay_period_hours = pay_period_hours
        self.gross_pay_amount = gross_pay_amount
        self.gross_pay_ytd = gross_pay_ytd
        self.net_pay_amount = net_pay_amount
        self.net_pay_ytd = net_pay_ytd
        self.payroll_provider = payroll_provider
        self.pay_frequency = pay_frequency
        self.pay_type = pay_type
        self.additional_properties = additional_properties

    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
        pay_date = dictionary.get('payDate')
        start_date = dictionary.get('startDate')
        end_date = dictionary.get('endDate')
        pay_period_hours = dictionary.get('payPeriodHours')
        gross_pay_amount = dictionary.get('grossPayAmount')
        gross_pay_ytd = dictionary.get('grossPayYTD')
        net_pay_amount = dictionary.get('netPayAmount')
        net_pay_ytd = dictionary.get('netPayYTD')
        payroll_provider = dictionary.get('payrollProvider')
        pay_frequency = dictionary.get('payFrequency')
        pay_type = dictionary.get('payType')
        for key in cls._names.values():
            if key in dictionary:
                del dictionary[key]
        return cls(pay_date, start_date, end_date, pay_period_hours, gross_pay_amount, gross_pay_ytd, net_pay_amount, net_pay_ytd, payroll_provider, pay_frequency, pay_type, dictionary)


training

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

Class description:
Defines a configuration class from which an ApexDDPG Trainer can be built. Example: >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig >>> config = ApexDDPGConfig().training(lr=0.01).resources(num_gpus=1) >>> print(config.to_dict()) >>> # Build a Trainer object from the config and run one training iteration. >>> trainer = config.build(env="Pendulum-v1") >>> trainer.train() Example: >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig >>> from ray import tune >>> config = ApexDDPGConfig() >>> # Print out some default values. >>> print(config.lr) # doctest: +SKIP 0.0004 >>> # Update the config object. >>> config.training(lr=tune.grid_search([0.001, 0.000

Method signatures and docstrings:
- def __init__(self, algo_class=None): Initializes an ApexDDPGConfig instance.
- def training(self, *, optimizer: Optional[dict]=None, max_requests_in_flight_per_sampler_worker: Optional[int]=None, max_requests_in_flight_per_replay_worker: Optional[int]=None, timeout_s_sampler_manager: Optional[float]=None, timeout_s_replay_manager: Optional[float]=None, **kwargs) -> 'ApexDDPGConfig': Sets the training related configuration. Args: optimizer: Apex-DDPG optimizer settings (dict). Set the number of reply buffer shards in here via the `num_replay_buffer_shards` key (default=4). max_requests_in_flight_per_sampler_worker: Max number of inflight requests to each sampling worker. See the AsyncRequestsManager class for more details. Tuning these values is important when running experimens with large sample batches, where there is the risk that the object store may fill up, causing spilling of objects to disk. This can cause any asynchronous requests to become very slow, making your experiment run slow as well. You can inspect the object store during your experiment via a call to r

Implement the Python class `ApexDDPGConfig` described below.

Class description:
Defines a configuration class from which an ApexDDPG Trainer can be built. Example: >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig >>> config = ApexDDPGConfig().training(lr=0.01).resources(num_gpus=1) >>> print(config.to_dict()) >>> # Build a Trainer object from the config and run one training iteration. >>> trainer = config.build(env="Pendulum-v1") >>> trainer.train() Example: >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig >>> from ray import tune >>> config = ApexDDPGConfig() >>> # Print out some default values. >>> print(config.lr) # doctest: +SKIP 0.0004 >>> # Update the config object. >>> config.training(lr=tune.grid_search([0.001, 0.000

Method signatures and docstrings:
- def __init__(self, algo_class=None): Initializes an ApexDDPGConfig instance.
- def training(self, *, optimizer: Optional[dict]=None, max_requests_in_flight_per_sampler_worker: Optional[int]=None, max_requests_in_flight_per_replay_worker: Optional[int]=None, timeout_s_sampler_manager: Optional[float]=None, timeout_s_replay_manager: Optional[float]=None, **kwargs) -> 'ApexDDPGConfig': Sets the training related configuration. Args: optimizer: Apex-DDPG optimizer settings (dict). Set the number of reply buffer shards in here via the `num_replay_buffer_shards` key (default=4). max_requests_in_flight_per_sampler_worker: Max number of inflight requests to each sampling worker. See the AsyncRequestsManager class for more details. Tuning these values is important when running experimens with large sample batches, where there is the risk that the object store may fill up, causing spilling of objects to disk. This can cause any asynchronous requests to become very slow, making your experiment run slow as well. You can inspect the object store during your experiment via a call to r

<|skeleton|>
class ApexDDPGConfig:
    """Defines a configuration class from which an ApexDDPG Trainer can be built. Example: >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig >>> config = ApexDDPGConfig().training(lr=0.01).resources(num_gpus=1) >>> print(config.to_dict()) >>> # Build a Trainer object from the config and run one training iteration. >>> trainer = config.build(env="Pendulum-v1") >>> trainer.train() Example: >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig >>> from ray import tune >>> config = ApexDDPGConfig() >>> # Print out some default values. >>> print(config.lr) # doctest: +SKIP 0.0004 >>> # Update the config object. >>> config.training(lr=tune.grid_search([0.001, 0.000"""

    def __init__(self, algo_class=None):
        """Initializes an ApexDDPGConfig instance."""
        <|body_0|>

    def training(self, *, optimizer: Optional[dict]=None, max_requests_in_flight_per_sampler_worker: Optional[int]=None, max_requests_in_flight_per_replay_worker: Optional[int]=None, timeout_s_sampler_manager: Optional[float]=None, timeout_s_replay_manager: Optional[float]=None, **kwargs) -> 'ApexDDPGConfig':
        """Sets the training related configuration. Args: optimizer: Apex-DDPG optimizer settings (dict). Set the number of reply buffer shards in here via the `num_replay_buffer_shards` key (default=4). max_requests_in_flight_per_sampler_worker: Max number of inflight requests to each sampling worker. See the AsyncRequestsManager class for more details. Tuning these values is important when running experimens with large sample batches, where there is the risk that the object store may fill up, causing spilling of objects to disk. This can cause any asynchronous requests to become very slow, making your experiment run slow as well. You can inspect the object store during your experiment via a call to r"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(algo_class=algo_class or ApexDDPG)
        self.optimizer = {'max_weight_sync_delay': 400, 'num_replay_buffer_shards': 4, 'debug': False}
        self.max_requests_in_flight_per_sampler_worker = 2
        self.max_requests_in_flight_per_replay_worker = float('inf')
        self.timeout_s_sampler_manager = 0.0
        self.timeout_s_replay_manager = 0.0
        self.n_step = 3
        self.exploration_config = {'type': 'PerWorkerOrnsteinUhlenbeckNoise'}
        self.num_gpus = 0
        self.num_workers = 32
        self.min_sample_timesteps_per_iteration = 25000
        self.min_time_s_per_iteration = 30
        self.train_batch_size = 512
        self.rollout_fragment_length = 50
        self.replay_buffer_config = {'type': 'MultiAgentPrioritizedReplayBuffer', 'capacity': 2000000, 'no_local_replay_buffer': True, 'prioritized_replay_alpha': 0.6, 'prioritized_replay_beta': 0.4, 'prioritized_replay_eps': 1e-06, 'replay_buffer_shards_colocated_with_driver': True, 'worker_side_prioritization': True, 'prioritized_replay': DEPRECATED_VALUE}
        self.num_steps_sampled_before_learning_starts = 50000
        self.target_network_update_freq = 500000
        self.training_intensity = 1
<|end_body_0|>

<|body_start_1|>
        super().training(**kwargs)
        if optimizer is not None:
            self.optimizer = optimizer
        if max_requests_in_flight_per_sampler_worker is not None:
            self.max_requests_in_flight_per_sampler_worker = max_requests_in_flight_per_sampler_worker
        if max_requests_in_flight_per_replay_worker is not None:
            self.max_requests_in_flight_per_replay_worker = max_requests_in_flight_per_replay_worker
        if timeout_s_sampler_manager is not None:
            self.timeout_s_sampler_manager = timeout_s_sampler_manager
        if timeout_s_replay_manager is not None:
            self.timeout_s_replay_manager = timeout_s_replay_manager
        return self
<|end_body_1|>

Solution:
class ApexDDPGConfig:
    """Defines a configuration class from which an ApexDDPG Trainer can be built. Example: >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig >>> config = ApexDDPGConfig().training(lr=0.01).resources(num_gpus=1) >>> print(config.to_dict()) >>> # Build a Trainer object from the config and run one training iteration. >>> trainer = config.build(env="Pendulum-v1") >>> trainer.train() Example: >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig >>> from ray import tune >>> config = ApexDDPGConfig() >>> # Print out some default values. >>> print(config.lr) # doctest: +SKIP 0.0004 >>> # Update the config object. >>> config.training(lr=tune.grid_search([0.001, 0.000"""

    def __init__(self, algo_class=None):
        """Initializes an ApexDDPGConfig instance."""
        super().__init__(algo_class=algo_class or ApexDDPG)
        self.optimizer = {'max_weight_sync_delay': 400, 'num_replay_buffer_shards': 4, 'debug': False}
        self.max_requests_in_flight_per_sampler_worker = 2
        self.max_requests_in_flight_per_replay_worker = float('inf')
        self.timeout_s_sampler_manager = 0.0
        self.timeout_s_replay_manager = 0.0
        self.n_step = 3
        self.exploration_config = {'type': 'PerWorkerOrnsteinUhlenbeckNoise'}
        self.num_gpus = 0
        self.num_workers = 32
        self.min_sample_timesteps_per_iteration = 25000
        self.min_time_s_per_iteration = 30
        self.train_batch_size = 512
        self.rollout_fragment_length = 50
        self.replay_buffer_config = {'type': 'MultiAgentPrioritizedReplayBuffer', 'capacity': 2000000, 'no_local_replay_buffer': True, 'prioritized_replay_alpha': 0.6, 'prioritized_replay_beta': 0.4, 'prioritized_replay_eps': 1e-06, 'replay_buffer_shards_colocated_with_driver': True, 'worker_side_prioritization': True, 'prioritized_replay': DEPRECATED_VALUE}
        self.num_steps_sampled_before_learning_starts = 50000
        self.target_network_update_freq = 500000
        self.training_intensity = 1

    def training(self, *, optimizer: Optional[dict]=None, max_requests_in_flight_per_sampler_worker: Optional[int]=None, max_requests_in_flight_per_replay_worker: Optional[int]=None, timeout_s_sampler_manager: Optional[float]=None, timeout_s_replay_manager: Optional[float]=None, **kwargs) -> 'ApexDDPGConfig':
        """Sets the training related configuration. Args: optimizer: Apex-DDPG optimizer settings (dict). Set the number of reply buffer shards in here via the `num_replay_buffer_shards` key (default=4). max_requests_in_flight_per_sampler_worker: Max number of inflight requests to each sampling worker. See the AsyncRequestsManager class for more details. Tuning these values is important when running experimens with large sample batches, where there is the risk that the object store may fill up, causing spilling of objects to disk. This can cause any asynchronous requests to become very slow, making your experiment run slow as well. You can inspect the object store during your experiment via a call to r"""
        super().training(**kwargs)
        if optimizer is not None:
            self.optimizer = optimizer
        if max_requests_in_flight_per_sampler_worker is not None:
            self.max_requests_in_flight_per_sampler_worker = max_requests_in_flight_per_sampler_worker
        if max_requests_in_flight_per_replay_worker is not None:
            self.max_requests_in_flight_per_replay_worker = max_requests_in_flight_per_replay_worker
        if timeout_s_sampler_manager is not None:
            self.timeout_s_sampler_manager = timeout_s_sampler_manager
        if timeout_s_replay_manager is not None:
            self.timeout_s_replay_manager = timeout_s_replay_manager
        return self


serialize

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

Class description:
Implement the DeviceConfigurationDeviceStatus class.

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

Implement the Python class `DeviceConfigurationDeviceStatus` described below.

Class description:
Implement the DeviceConfigurationDeviceStatus class.

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

<|skeleton|>
class DeviceConfigurationDeviceStatus:

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

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

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

<|end_skeleton|>

<|body_start_0|>
        if not parse_node:
            raise TypeError('parse_node cannot be null.')
        return DeviceConfigurationDeviceStatus()
<|end_body_0|>

<|body_start_1|>
        from .compliance_status import ComplianceStatus
        from .entity import Entity
        from .compliance_status import ComplianceStatus
        from .entity import Entity
        fields: Dict[str, Callable[[Any], None]] = {'complianceGracePeriodExpirationDateTime': lambda n: setattr(self, 'compliance_grace_period_expiration_date_time', n.get_datetime_value()), 'deviceDisplayName': lambda n: setattr(self, 'device_display_name', n.get_str_value()), 'deviceModel': lambda n: setattr(self, 'device_model', n.get_str_value()), 'lastReportedDateTime': lambda n: setattr(self, 'last_reported_date_time', n.get_datetime_value()), 'status': lambda n: setattr(self, 'status', n.get_enum_value(ComplianceStatus)), 'userName': lambda n: setattr(self, 'user_name', n.get_str_value()), 'userPrincipalName': lambda n: setattr(self, 'user_principal_name', n.get_str_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields
<|end_body_1|>

<|body_start_2|>
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_datetime_value('complianceGracePeriodExpirationDateTime', self.compliance_grace_period_expiration_date_time)
        writer.write_str_value('deviceDisplayName', self.device_display_name)
        writer.write_str_value('deviceModel', self.device_model)
        writer.write_datetime_value('lastReportedDateTime', self.last_reported_date_time)
        writer.write_enum_value('status', self.status)
        writer.write_str_value('userName', self.user_name)
        writer.write_str_value('userPrincipalName', self.user_principal_name)
<|end_body_2|>

Solution:
class DeviceConfigurationDeviceStatus:
    def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DeviceConfigurationDeviceStatus:
        """Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: DeviceConfigurationDeviceStatus"""
        if not parse_node:
            raise TypeError('parse_node cannot be null.')
        return DeviceConfigurationDeviceStatus()

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        from .compliance_status import ComplianceStatus
        from .entity import Entity
        from .compliance_status import ComplianceStatus
        from .entity import Entity
        fields: Dict[str, Callable[[Any], None]] = {'complianceGracePeriodExpirationDateTime': lambda n: setattr(self, 'compliance_grace_period_expiration_date_time', n.get_datetime_value()), 'deviceDisplayName': lambda n: setattr(self, 'device_display_name', n.get_str_value()), 'deviceModel': lambda n: setattr(self, 'device_model', n.get_str_value()), 'lastReportedDateTime': lambda n: setattr(self, 'last_reported_date_time', n.get_datetime_value()), 'status': lambda n: setattr(self, 'status', n.get_enum_value(ComplianceStatus)), 'userName': lambda n: setattr(self, 'user_name', n.get_str_value()), 'userPrincipalName': lambda n: setattr(self, 'user_principal_name', n.get_str_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_datetime_value('complianceGracePeriodExpirationDateTime', self.compliance_grace_period_expiration_date_time)
        writer.write_str_value('deviceDisplayName', self.device_display_name)
        writer.write_str_value('deviceModel', self.device_model)
        writer.write_datetime_value('lastReportedDateTime', self.last_reported_date_time)
        writer.write_enum_value('status', self.status)
        writer.write_str_value('userName', self.user_name)
        writer.write_str_value('userPrincipalName', self.user_principal_name)


post

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

Class description:
Implement the DaysOff class.

Method signatures and docstrings:
- def get(self, request): parameters: - name: next_month_only description: show only next month days off required: false type: bool
- def post(self, request): parameters: - name: email description: client's email to whom we should send an email required: true type: string

Implement the Python class `DaysOff` described below.

Class description:
Implement the DaysOff class.

Method signatures and docstrings:
- def get(self, request): parameters: - name: next_month_only description: show only next month days off required: false type: bool
- def post(self, request): parameters: - name: email description: client's email to whom we should send an email required: true type: string

<|skeleton|>
class DaysOff:

    def get(self, request):
        """parameters: - name: next_month_only description: show only next month days off required: false type: bool"""
        <|body_0|>

    def post(self, request):
        """parameters: - name: email description: client's email to whom we should send an email required: true type: string"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        data = request.query_params
        next_month_only = data.get('next_month_only', True)
        next_month_days_off = get_ua_days_off(next_month_only)
        return json_response_success(data=next_month_days_off)
<|end_body_0|>

<|body_start_1|>
        email = request.data.get('email', None)
        if not email:
            return json_response_error("Should provide customer's email")
        next_month_days_off = get_ua_days_off()
        if not next_month_days_off:
            return json_response_success('No holidays in next 30 days')
        if next_month_days_off:
            html = str(next_month_days_off)
            return json_response_success('Email to %s is succesfully sent' % email)
<|end_body_1|>

Solution:
class DaysOff:
    def get(self, request):
        """parameters: - name: next_month_only description: show only next month days off required: false type: bool"""
        data = request.query_params
        next_month_only = data.get('next_month_only', True)
        next_month_days_off = get_ua_days_off(next_month_only)
        return json_response_success(data=next_month_days_off)

    def post(self, request):
        """parameters: - name: email description: client's email to whom we should send an email required: true type: string"""
        email = request.data.get('email', None)
        if not email:
            return json_response_error("Should provide customer's email")
        next_month_days_off = get_ua_days_off()
        if not next_month_days_off:
            return json_response_success('No holidays in next 30 days')
        if next_month_days_off:
            html = str(next_month_days_off)
            return json_response_success('Email to %s is succesfully sent' % email)


pdf

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

Class description:
Inverse Gamma Distribution ---- This class contains methods relating to the inverse gamma distribution for time series.

Method signatures and docstrings:
- def __init__(self, alpha, beta, transform=None, **kwargs): Parameters ---------- alpha : float Alpha parameter for the Inverse Gamma distribution beta : float Beta parameter for the Inverse Gamma distribution transform : str Whether to apply a transformation - e.g. 'exp' or 'logit'
- def logpdf(self, x): Log PDF for Inverse Gamma prior Parameters ---------- x : float Latent variable for which the prior is being formed over Returns ---------- - log(p(x))
- def pdf(self, x): PDF for Inverse Gamma prior Parameters ---------- x : float Latent variable for which the prior is being formed over Returns ---------- - p(x)

Implement the Python class `InverseGamma` described below.

Class description:
Inverse Gamma Distribution ---- This class contains methods relating to the inverse gamma distribution for time series.

Method signatures and docstrings:
- def __init__(self, alpha, beta, transform=None, **kwargs): Parameters ---------- alpha : float Alpha parameter for the Inverse Gamma distribution beta : float Beta parameter for the Inverse Gamma distribution transform : str Whether to apply a transformation - e.g. 'exp' or 'logit'
- def logpdf(self, x): Log PDF for Inverse Gamma prior Parameters ---------- x : float Latent variable for which the prior is being formed over Returns ---------- - log(p(x))
- def pdf(self, x): PDF for Inverse Gamma prior Parameters ---------- x : float Latent variable for which the prior is being formed over Returns ---------- - p(x)

<|skeleton|>
class InverseGamma:
    """Inverse Gamma Distribution ---- This class contains methods relating to the inverse gamma distribution for time series."""

    def __init__(self, alpha, beta, transform=None, **kwargs):
        """Parameters ---------- alpha : float Alpha parameter for the Inverse Gamma distribution beta : float Beta parameter for the Inverse Gamma distribution transform : str Whether to apply a transformation - e.g. 'exp' or 'logit'"""
        <|body_0|>

    def logpdf(self, x):
        """Log PDF for Inverse Gamma prior Parameters ---------- x : float Latent variable for which the prior is being formed over Returns ---------- - log(p(x))"""
        <|body_1|>

    def pdf(self, x):
        """PDF for Inverse Gamma prior Parameters ---------- x : float Latent variable for which the prior is being formed over Returns ---------- - p(x)"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(InverseGamma, self).__init__(transform)
        self.covariance_prior = False
        self.alpha = alpha
        self.beta = beta
<|end_body_0|>

<|body_start_1|>
        if self.transform is not None:
            x = self.transform(x)
        return (-self.alpha - 1) * np.log(x) - self.beta / float(x)
<|end_body_1|>

<|body_start_2|>
        if self.transform is not None:
            x = self.transform(x)
        return x ** (-self.alpha - 1) * np.exp(-(self.beta / float(x)))
<|end_body_2|>

Solution:
class InverseGamma:
    """Inverse Gamma Distribution ---- This class contains methods relating to the inverse gamma distribution for time series."""

    def __init__(self, alpha, beta, transform=None, **kwargs):
        """Parameters ---------- alpha : float Alpha parameter for the Inverse Gamma distribution beta : float Beta parameter for the Inverse Gamma distribution transform : str Whether to apply a transformation - e.g. 'exp' or 'logit'"""
        super(InverseGamma, self).__init__(transform)
        self.covariance_prior = False
        self.alpha = alpha
        self.beta = beta

    def logpdf(self, x):
        """Log PDF for Inverse Gamma prior Parameters ---------- x : float Latent variable for which the prior is being formed over Returns ---------- - log(p(x))"""
        if self.transform is not None:
            x = self.transform(x)
        return (-self.alpha - 1) * np.log(x) - self.beta / float(x)

    def pdf(self, x):
        """PDF for Inverse Gamma prior Parameters ---------- x : float Latent variable for which the prior is being formed over Returns ---------- - p(x)"""
        if self.transform is not None:
            x = self.transform(x)
        return x ** (-self.alpha - 1) * np.exp(-(self.beta / float(x)))


Finish

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

Class description:
IceTray compatible class of the Deep Learning Classifier

Method signatures and docstrings:
- def __init__(self, context): Initialize the Class
- def Configure(self): Read the network architecture and input, output information from config files
- def BatchProcessBuffer(self, frames): Batch Process a list of frames. This includes pre-processing, prediction and storage of the results
- def Physics(self, frame): Buffer physics frames until batch size is reached, then start processing
- def DAQ(self, frame): Handel Q-Frames. Append to buffer if they should be kept
- def Finish(self): Process the remaining (incomplete) batch of frames

Implement the Python class `DeepLearningClassifier` described below.

Class description:
IceTray compatible class of the Deep Learning Classifier

Method signatures and docstrings:
- def __init__(self, context): Initialize the Class
- def Configure(self): Read the network architecture and input, output information from config files
- def BatchProcessBuffer(self, frames): Batch Process a list of frames. This includes pre-processing, prediction and storage of the results
- def Physics(self, frame): Buffer physics frames until batch size is reached, then start processing
- def DAQ(self, frame): Handel Q-Frames. Append to buffer if they should be kept
- def Finish(self): Process the remaining (incomplete) batch of frames

<|skeleton|>
class DeepLearningClassifier:
    """IceTray compatible class of the Deep Learning Classifier"""

    def __init__(self, context):
        """Initialize the Class"""
        <|body_0|>

    def Configure(self):
        """Read the network architecture and input, output information from config files"""
        <|body_1|>

    def BatchProcessBuffer(self, frames):
        """Batch Process a list of frames. This includes pre-processing, prediction and storage of the results"""
        <|body_2|>

    def Physics(self, frame):
        """Buffer physics frames until batch size is reached, then start processing"""
        <|body_3|>

    def DAQ(self, frame):
        """Handel Q-Frames. Append to buffer if they should be kept"""
        <|body_4|>

    def Finish(self):
        """Process the remaining (incomplete) batch of frames"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        icetray.I3ConditionalModule.__init__(self, context)
        self.AddParameter('pulsemap', 'Define the name of the pulsemap', 'InIceDSTPulses')
        self.AddParameter('save_as', 'Define the Output key', 'Deep_Learning_Classification')
        self.AddParameter('batch_size', 'Size of the batches', 40)
        self.AddParameter('cpu_cores', 'number of cores to be used', 1)
        self.AddParameter('gpu_cores', 'number of gpu to be used', 1)
        self.AddParameter('remove_daq', 'whether or not to remove Q-Frames', False)
        self.AddParameter('model', 'which model to use', 'classification')
<|end_body_0|>

<|body_start_1|>
        print('Initializing')
        self.__pulsemap = self.GetParameter('pulsemap')
        self.__save_as = self.GetParameter('save_as')
        self.__batch_size = self.GetParameter('batch_size')
        self.__cpu_cores = self.GetParameter('cpu_cores')
        self.__gpu_cores = self.GetParameter('gpu_cores')
        self.__remove_daq = self.GetParameter('remove_daq')
        self.__frame_buffer = []
        self.__buffer_length = 0
        self.__num_pframes = 0
        config = tf.ConfigProto(intra_op_parallelism_threads=self.__cpu_cores, inter_op_parallelism_threads=self.__cpu_cores, device_count={'GPU': self.__gpu_cores, 'CPU': self.__cpu_cores}, log_device_placement=False)
        sess = tf.Session(config=config)
        set_session(sess)
        return
<|end_body_1|>

<|body_start_2|>
        if self.__num_pframes == 0:
            return
        for frame in frames:
            if frame.Stop != icetray.I3Frame.Physics:
                continue
        predictions = self.__model.predict(you_processed_frames, *args, **kwargs)
        i = 0
        for frame in frames:
            if frame.Stop != icetray.I3Frame.Physics:
                continue
            frame.Put(self.__save_as, output)
            i += 1
        return
<|end_body_2|>

<|body_start_3|>
        self.__frame_buffer.append(frame)
        self.__buffer_length += 1
        self.__num_pframes += 1
        if self.__buffer_length == self.__batch_size:
            self.BatchProcessBuffer(self.__frame_buffer)
            for frame in self.__frame_buffer:
                self.PushFrame(frame)
            self.__frame_buffer[:] = []
            self.__buffer_length = 0
            self.__num_pframes = 0
        return
<|end_body_3|>

<|body_start_4|>
        if not self.__remove_daq:
            self.__frame_buffer.append(frame)
        return
<|end_body_4|>

<|body_start_5|>
        self.BatchProcessBuffer(self.__frame_buffer)
        for frame in self.__frame_buffer:
            self.PushFrame(frame)
        self.__frame_buffer[:] = []
        return
<|end_body_5|>

Solution:
class DeepLearningClassifier:
    """IceTray compatible class of the Deep Learning Classifier"""

    def __init__(self, context):
        """Initialize the Class"""
        icetray.I3ConditionalModule.__init__(self, context)
        self.AddParameter('pulsemap', 'Define the name of the pulsemap', 'InIceDSTPulses')
        self.AddParameter('save_as', 'Define the Output key', 'Deep_Learning_Classification')
        self.AddParameter('batch_size', 'Size of the batches', 40)
        self.AddParameter('cpu_cores', 'number of cores to be used', 1)
        self.AddParameter('gpu_cores', 'number of gpu to be used', 1)
        self.AddParameter('remove_daq', 'whether or not to remove Q-Frames', False)
        self.AddParameter('model', 'which model to use', 'classification')

    def Configure(self):
        """Read the network architecture and input, output information from config files"""
        print('Initializing')
        self.__pulsemap = self.GetParameter('pulsemap')
        self.__save_as = self.GetParameter('save_as')
        self.__batch_size = self.GetParameter('batch_size')
        self.__cpu_cores = self.GetParameter('cpu_cores')
        self.__gpu_cores = self.GetParameter('gpu_cores')
        self.__remove_daq = self.GetParameter('remove_daq')
        self.__frame_buffer = []
        self.__buffer_length = 0
        self.__num_pframes = 0
        config = tf.ConfigProto(intra_op_parallelism_threads=self.__cpu_cores, inter_op_parallelism_threads=self.__cpu_cores, device_count={'GPU': self.__gpu_cores, 'CPU': self.__cpu_cores}, log_device_placement=False)
        sess = tf.Session(config=config)
        set_session(sess)
        return

    def BatchProcessBuffer(self, frames):
        """Batch Process a list of frames. This includes pre-processing, prediction and storage of the results"""
        if self.__num_pframes == 0:
            return
        for frame in frames:
            if frame.Stop != icetray.I3Frame.Physics:
                continue
        predictions = self.__model.predict(you_processed_frames, *args, **kwargs)
        i = 0
        for frame in frames:
            if frame.Stop != icetray.I3Frame.Physics:
                continue
            frame.Put(self.__save_as, output)
            i += 1
        return

    def Physics(self, frame):
        """Buffer physics frames until batch size is reached, then start processing"""
        self.__frame_buffer.append(frame)
        self.__buffer_length += 1
        self.__num_pframes += 1
        if self.__buffer_length == self.__batch_size:
            self.BatchProcessBuffer(self.__frame_buffer)
            for frame in self.__frame_buffer:
                self.PushFrame(frame)
            self.__frame_buffer[:] = []
            self.__buffer_length = 0
            self.__num_pframes = 0
        return

    def DAQ(self, frame):
        """Handel Q-Frames. Append to buffer if they should be kept"""
        if not self.__remove_daq:
            self.__frame_buffer.append(frame)
        return

    def Finish(self):
        """Process the remaining (incomplete) batch of frames"""
        self.BatchProcessBuffer(self.__frame_buffer)
        for frame in self.__frame_buffer:
            self.PushFrame(frame)
        self.__frame_buffer[:] = []
        return


averages

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

Class description:
For each couple (receiver, sender) we gather a number of measurement points that must be averaged this is done through an instance of this Averager class all measurement points will contain one, two or three values depending on the number of antennas each measurement point is recorded, and at the end averages returns the right value(s)

Method signatures and docstrings:
- def __init__(self, columns): number of expected columns
- def record_point(self, values): values should have as many measurements as columns
- def averages(self, default): once all measurement points have been recorded, they can be averaged

Implement the Python class `Averager` described below.

Class description:
For each couple (receiver, sender) we gather a number of measurement points that must be averaged this is done through an instance of this Averager class all measurement points will contain one, two or three values depending on the number of antennas each measurement point is recorded, and at the end averages returns the right value(s)

Method signatures and docstrings:
- def __init__(self, columns): number of expected columns
- def record_point(self, values): values should have as many measurements as columns
- def averages(self, default): once all measurement points have been recorded, they can be averaged

<|skeleton|>
class Averager:
    """For each couple (receiver, sender) we gather a number of measurement points that must be averaged this is done through an instance of this Averager class all measurement points will contain one, two or three values depending on the number of antennas each measurement point is recorded, and at the end averages returns the right value(s)"""

    def __init__(self, columns):
        """number of expected columns"""
        <|body_0|>

    def record_point(self, values):
        """values should have as many measurements as columns"""
        <|body_1|>

    def averages(self, default):
        """once all measurement points have been recorded, they can be averaged"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.number = 0
        self.columns = columns
        self.total = [0 for i in range(self.columns)]
<|end_body_0|>

<|body_start_1|>
        self.number += 1
        for i in range(self.columns):
            self.total[i] += values[i]
<|end_body_1|>

<|body_start_2|>
        if self.number == 0:
            return [default for i in range(self.columns)]
        else:
            return [self.total[i] / self.number for i in range(self.columns)]
<|end_body_2|>

Solution:
class Averager:
    """For each couple (receiver, sender) we gather a number of measurement points that must be averaged this is done through an instance of this Averager class all measurement points will contain one, two or three values depending on the number of antennas each measurement point is recorded, and at the end averages returns the right value(s)"""

    def __init__(self, columns):
        """number of expected columns"""
        self.number = 0
        self.columns = columns
        self.total = [0 for i in range(self.columns)]

    def record_point(self, values):
        """values should have as many measurements as columns"""
        self.number += 1
        for i in range(self.columns):
            self.total[i] += values[i]

    def averages(self, default):
        """once all measurement points have been recorded, they can be averaged"""
        if self.number == 0:
            return [default for i in range(self.columns)]
        else:
            return [self.total[i] / self.number for i in range(self.columns)]