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

Class description:
Perform single point crossover between genomes at some defined rates. This performs a single crossover between two genomes at some defined frequency. The location of the crossover is chosen randomly if the crossover meets the probability to occur.

Method signatures and docstrings:
- def __init__(self, crossover_prob=0.1, uniform_prob=0.7): Initialize to do uniform crossover at the specified probability and frequency.
- def do_crossover(self, org_1, org_2): Potentially do a crossover between the two organisms.

Implement the Python class `UniformCrossover` described below.

Class description:
Perform single point crossover between genomes at some defined rates. This performs a single crossover between two genomes at some defined frequency. The location of the crossover is chosen randomly if the crossover meets the probability to occur.

Method signatures and docstrings:
- def __init__(self, crossover_prob=0.1, uniform_prob=0.7): Initialize to do uniform crossover at the specified probability and frequency.
- def do_crossover(self, org_1, org_2): Potentially do a crossover between the two organisms.

<|skeleton|>
class UniformCrossover:
    """Perform single point crossover between genomes at some defined rates. This performs a single crossover between two genomes at some defined frequency. The location of the crossover is chosen randomly if the crossover meets the probability to occur."""

    def __init__(self, crossover_prob=0.1, uniform_prob=0.7):
        """Initialize to do uniform crossover at the specified probability and frequency."""
        <|body_0|>

    def do_crossover(self, org_1, org_2):
        """Potentially do a crossover between the two organisms."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self._crossover_prob = crossover_prob
        self._uniform_prob = uniform_prob
        return
<|end_body_0|>

<|body_start_1|>
        new_org_1 = org_1.copy()
        new_org_2 = org_2.copy()
        crossover_chance = random.random()
        if crossover_chance <= self._crossover_prob:
            minlen = min(len(new_org_1.genome), len(new_org_2.genome))
            for i in range(minlen):
                uniform_chance = random.random()
                if uniform_chance <= self._uniform_prob:
                    temp = new_org_1.genome[i]
                    new_org_1.genome[i] = new_org_2.genome[i]
                    new_org_2.genome[i] = temp
        return (new_org_1, new_org_2)
<|end_body_1|>

Solution:
class UniformCrossover:
    """Perform single point crossover between genomes at some defined rates. This performs a single crossover between two genomes at some defined frequency. The location of the crossover is chosen randomly if the crossover meets the probability to occur."""

    def __init__(self, crossover_prob=0.1, uniform_prob=0.7):
        """Initialize to do uniform crossover at the specified probability and frequency."""
        self._crossover_prob = crossover_prob
        self._uniform_prob = uniform_prob
        return

    def do_crossover(self, org_1, org_2):
        """Potentially do a crossover between the two organisms."""
        new_org_1 = org_1.copy()
        new_org_2 = org_2.copy()
        crossover_chance = random.random()
        if crossover_chance <= self._crossover_prob:
            minlen = min(len(new_org_1.genome), len(new_org_2.genome))
            for i in range(minlen):
                uniform_chance = random.random()
                if uniform_chance <= self._uniform_prob:
                    temp = new_org_1.genome[i]
                    new_org_1.genome[i] = new_org_2.genome[i]
                    new_org_2.genome[i] = temp
        return (new_org_1, new_org_2)


forward

Prompt:
Implement the Python class `ESPNetX3` described below.

Class description:
This class defines the ESPNetX3 network

Method signatures and docstrings:
- def __init__(self, classes=19, p=2, q=3, encoderFile=None): :param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplier :param q: depth multiplier :param encoderFile: pretrained encoder weights. Recall that we first trained the ESPNetX3-C and then attached the RUM-based light weight decoder. See paper for more details.
- def forward(self, input): :param input: RGB image :return: transformed feature map

Implement the Python class `ESPNetX3` described below.

Class description:
This class defines the ESPNetX3 network

Method signatures and docstrings:
- def __init__(self, classes=19, p=2, q=3, encoderFile=None): :param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplier :param q: depth multiplier :param encoderFile: pretrained encoder weights. Recall that we first trained the ESPNetX3-C and then attached the RUM-based light weight decoder. See paper for more details.
- def forward(self, input): :param input: RGB image :return: transformed feature map

<|skeleton|>
class ESPNetX3:
    """This class defines the ESPNetX3 network"""

    def __init__(self, classes=19, p=2, q=3, encoderFile=None):
        """:param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplier :param q: depth multiplier :param encoderFile: pretrained encoder weights. Recall that we first trained the ESPNetX3-C and then attached the RUM-based light weight decoder. See paper for more details."""
        <|body_0|>

    def forward(self, input):
        """:param input: RGB image :return: transformed feature map"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.encoder = ESPNetX3_Encoder(classes, p, q)
        if encoderFile != None:
            self.encoder.load_state_dict(torch.load(encoderFile))
            print('Encoder loaded!')
        self.en_modules = []
        for i, m in enumerate(self.encoder.children()):
            self.en_modules.append(m)
        self.level3_C = C(128 + 3, classes, 1, 1)
        self.br = nn.BatchNorm2d(classes, eps=0.001)
        self.conv = CBR(19 + classes, classes, 3, 1)
        self.up_l3 = nn.Sequential(nn.ConvTranspose2d(classes, classes, 2, stride=2, padding=0, output_padding=0, bias=False))
        self.combine_l2_l3 = nn.Sequential(BR(2 * classes), DilatedParllelResidualBlockB(2 * classes, classes, add=False))
        self.up_l2 = nn.Sequential(nn.ConvTranspose2d(classes, classes, 2, stride=2, padding=0, output_padding=0, bias=False), BR(classes))
        self.classifier = nn.ConvTranspose2d(classes, classes, 2, stride=2, padding=0, output_padding=0, bias=False)
<|end_body_0|>

<|body_start_1|>
        output0 = self.en_modules[0](input)
        inp1 = self.en_modules[1](input)
        inp2 = self.en_modules[2](input)
        output0_cat = self.en_modules[3](torch.cat([output0, inp1], 1))
        output1_0 = self.en_modules[4](output0_cat)
        for i, layer in enumerate(self.en_modules[5]):
            if i == 0:
                output1 = layer(output1_0)
            else:
                output1 = layer(output1)
        output1_cat = self.en_modules[6](torch.cat([output1, output1_0, inp2], 1))
        output2_0 = self.en_modules[7](output1_cat)
        for i, layer in enumerate(self.en_modules[8]):
            if i == 0:
                output2 = layer(output2_0)
            else:
                output2 = layer(output2)
        output2_cat = self.en_modules[9](torch.cat([output2_0, output2], 1))
        output2_c = self.up_l3(self.br(self.en_modules[10](output2_cat)))
        output1_C = self.level3_C(output1_cat)
        comb_l2_l3 = self.up_l2(self.combine_l2_l3(torch.cat([output1_C, output2_c], 1)))
        concat_features = self.conv(torch.cat([comb_l2_l3, output0_cat], 1))
        classifier = self.classifier(concat_features)
        return classifier
<|end_body_1|>

Solution:
class ESPNetX3:
    """This class defines the ESPNetX3 network"""

    def __init__(self, classes=19, p=2, q=3, encoderFile=None):
        """:param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplier :param q: depth multiplier :param encoderFile: pretrained encoder weights. Recall that we first trained the ESPNetX3-C and then attached the RUM-based light weight decoder. See paper for more details."""
        super().__init__()
        self.encoder = ESPNetX3_Encoder(classes, p, q)
        if encoderFile != None:
            self.encoder.load_state_dict(torch.load(encoderFile))
            print('Encoder loaded!')
        self.en_modules = []
        for i, m in enumerate(self.encoder.children()):
            self.en_modules.append(m)
        self.level3_C = C(128 + 3, classes, 1, 1)
        self.br = nn.BatchNorm2d(classes, eps=0.001)
        self.conv = CBR(19 + classes, classes, 3, 1)
        self.up_l3 = nn.Sequential(nn.ConvTranspose2d(classes, classes, 2, stride=2, padding=0, output_padding=0, bias=False))
        self.combine_l2_l3 = nn.Sequential(BR(2 * classes), DilatedParllelResidualBlockB(2 * classes, classes, add=False))
        self.up_l2 = nn.Sequential(nn.ConvTranspose2d(classes, classes, 2, stride=2, padding=0, output_padding=0, bias=False), BR(classes))
        self.classifier = nn.ConvTranspose2d(classes, classes, 2, stride=2, padding=0, output_padding=0, bias=False)

    def forward(self, input):
        """:param input: RGB image :return: transformed feature map"""
        output0 = self.en_modules[0](input)
        inp1 = self.en_modules[1](input)
        inp2 = self.en_modules[2](input)
        output0_cat = self.en_modules[3](torch.cat([output0, inp1], 1))
        output1_0 = self.en_modules[4](output0_cat)
        for i, layer in enumerate(self.en_modules[5]):
            if i == 0:
                output1 = layer(output1_0)
            else:
                output1 = layer(output1)
        output1_cat = self.en_modules[6](torch.cat([output1, output1_0, inp2], 1))
        output2_0 = self.en_modules[7](output1_cat)
        for i, layer in enumerate(self.en_modules[8]):
            if i == 0:
                output2 = layer(output2_0)
            else:
                output2 = layer(output2)
        output2_cat = self.en_modules[9](torch.cat([output2_0, output2], 1))
        output2_c = self.up_l3(self.br(self.en_modules[10](output2_cat)))
        output1_C = self.level3_C(output1_cat)
        comb_l2_l3 = self.up_l2(self.combine_l2_l3(torch.cat([output1_C, output2_c], 1)))
        concat_features = self.conv(torch.cat([comb_l2_l3, output0_cat], 1))
        classifier = self.classifier(concat_features)
        return classifier


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

Class description:
Implement the MellanoxMixin class.

Method signatures and docstrings:
- def inject_mellanox_settings_for_deployment(cls, node_attrs, cluster, networks): Mellanox settings for deployment Serialize mellanox node attrs then it will be merged with common attributes, if mellanox plugin or iSER storage enabled. :param node_attrs: attributes for specific node :type node_attrs: dict :param cluster: A cluster instance :type cluster: Cluster model :param networks: networks related for specific node :type networks: list :returns: None
- def inject_mellanox_settings_for_provisioning(cls, cluster_attrs, serialized_node): Mellanox settings for provisioning Serialize mellanox node attrs then it will be merged with common node attributes :param cluster_attrs: cluster attributes :type cluster_attrs: dict :param serialized_node: node attributes data for provisioning :type serialized_node: dict :returns: None

Implement the Python class `MellanoxMixin` described below.

Class description:
Implement the MellanoxMixin class.

Method signatures and docstrings:
- def inject_mellanox_settings_for_deployment(cls, node_attrs, cluster, networks): Mellanox settings for deployment Serialize mellanox node attrs then it will be merged with common attributes, if mellanox plugin or iSER storage enabled. :param node_attrs: attributes for specific node :type node_attrs: dict :param cluster: A cluster instance :type cluster: Cluster model :param networks: networks related for specific node :type networks: list :returns: None
- def inject_mellanox_settings_for_provisioning(cls, cluster_attrs, serialized_node): Mellanox settings for provisioning Serialize mellanox node attrs then it will be merged with common node attributes :param cluster_attrs: cluster attributes :type cluster_attrs: dict :param serialized_node: node attributes data for provisioning :type serialized_node: dict :returns: None

<|skeleton|>
class MellanoxMixin:

    def inject_mellanox_settings_for_deployment(cls, node_attrs, cluster, networks):
        """Mellanox settings for deployment Serialize mellanox node attrs then it will be merged with common attributes, if mellanox plugin or iSER storage enabled. :param node_attrs: attributes for specific node :type node_attrs: dict :param cluster: A cluster instance :type cluster: Cluster model :param networks: networks related for specific node :type networks: list :returns: None"""
        <|body_0|>

    def inject_mellanox_settings_for_provisioning(cls, cluster_attrs, serialized_node):
        """Mellanox settings for provisioning Serialize mellanox node attrs then it will be merged with common node attributes :param cluster_attrs: cluster attributes :type cluster_attrs: dict :param serialized_node: node attributes data for provisioning :type serialized_node: dict :returns: None"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        cluster_attrs = objects.Cluster.get_editable_attributes(cluster)
        neutron_mellanox_data = cluster_attrs.get('neutron_mellanox', {})
        if neutron_mellanox_data:
            storage_data = cluster_attrs.get('storage', {})
            nm = objects.Cluster.get_network_manager(cluster)
            node_attrs['neutron_mellanox'] = {}
            if 'plugin' in neutron_mellanox_data and neutron_mellanox_data['plugin']['value'] == 'ethernet':
                node_attrs['neutron_mellanox'].update({'physical_port': nm.get_network_by_netname('private', networks)['dev'], 'ml2_eswitch': {'vnic_type': 'hostdev', 'apply_profile_patch': True}})
            if 'iser' in storage_data and storage_data['iser']['value']:
                iser_new_name = 'eth_iser0'
                node_attrs['neutron_mellanox'].update({'storage_parent': nm.get_network_by_netname('storage', networks)['dev'], 'iser_interface_name': iser_new_name})
                storage_vlan = nm.get_network_by_netname('storage', networks).get('vlan')
                if storage_vlan:
                    vlan_name = 'vlan{0}'.format(storage_vlan)
                    node_attrs['network_scheme']['roles']['storage'] = vlan_name
                    node_attrs['network_scheme']['interfaces'][vlan_name] = {'L2': {'vlan_splinters': 'off'}}
                    node_attrs['network_scheme']['endpoints'][vlan_name] = node_attrs['network_scheme']['endpoints'].pop('br-storage', {})
                    node_attrs['network_scheme']['endpoints'][vlan_name]['vlandev'] = iser_new_name
                else:
                    node_attrs['network_scheme']['roles']['storage'] = iser_new_name
                    node_attrs['network_scheme']['interfaces'][iser_new_name] = {'L2': {'vlan_splinters': 'off'}}
                    node_attrs['network_scheme']['endpoints'][iser_new_name] = node_attrs['network_scheme']['endpoints'].pop('br-storage', {})
<|end_body_0|>

<|body_start_1|>
        mellanox_data = cluster_attrs.get('neutron_mellanox')
        if mellanox_data:
            serialized_node['ks_meta'].update({'mlnx_vf_num': mellanox_data.get('vf_num'), 'mlnx_plugin_mode': mellanox_data.get('plugin'), 'mlnx_iser_enabled': cluster_attrs.get('storage', {}).get('iser')})
            pm_data = serialized_node['ks_meta']['pm_data']
            if (mellanox_data['plugin'] == 'ethernet' or cluster_attrs['storage']['iser'] is True) and 'intel_iommu=' not in pm_data['kernel_params']:
                pm_data['kernel_params'] += ' intel_iommu=on'
<|end_body_1|>

Solution:
class MellanoxMixin:
    def inject_mellanox_settings_for_deployment(cls, node_attrs, cluster, networks):
        """Mellanox settings for deployment Serialize mellanox node attrs then it will be merged with common attributes, if mellanox plugin or iSER storage enabled. :param node_attrs: attributes for specific node :type node_attrs: dict :param cluster: A cluster instance :type cluster: Cluster model :param networks: networks related for specific node :type networks: list :returns: None"""
        cluster_attrs = objects.Cluster.get_editable_attributes(cluster)
        neutron_mellanox_data = cluster_attrs.get('neutron_mellanox', {})
        if neutron_mellanox_data:
            storage_data = cluster_attrs.get('storage', {})
            nm = objects.Cluster.get_network_manager(cluster)
            node_attrs['neutron_mellanox'] = {}
            if 'plugin' in neutron_mellanox_data and neutron_mellanox_data['plugin']['value'] == 'ethernet':
                node_attrs['neutron_mellanox'].update({'physical_port': nm.get_network_by_netname('private', networks)['dev'], 'ml2_eswitch': {'vnic_type': 'hostdev', 'apply_profile_patch': True}})
            if 'iser' in storage_data and storage_data['iser']['value']:
                iser_new_name = 'eth_iser0'
                node_attrs['neutron_mellanox'].update({'storage_parent': nm.get_network_by_netname('storage', networks)['dev'], 'iser_interface_name': iser_new_name})
                storage_vlan = nm.get_network_by_netname('storage', networks).get('vlan')
                if storage_vlan:
                    vlan_name = 'vlan{0}'.format(storage_vlan)
                    node_attrs['network_scheme']['roles']['storage'] = vlan_name
                    node_attrs['network_scheme']['interfaces'][vlan_name] = {'L2': {'vlan_splinters': 'off'}}
                    node_attrs['network_scheme']['endpoints'][vlan_name] = node_attrs['network_scheme']['endpoints'].pop('br-storage', {})
                    node_attrs['network_scheme']['endpoints'][vlan_name]['vlandev'] = iser_new_name
                else:
                    node_attrs['network_scheme']['roles']['storage'] = iser_new_name
                    node_attrs['network_scheme']['interfaces'][iser_new_name] = {'L2': {'vlan_splinters': 'off'}}
                    node_attrs['network_scheme']['endpoints'][iser_new_name] = node_attrs['network_scheme']['endpoints'].pop('br-storage', {})

    def inject_mellanox_settings_for_provisioning(cls, cluster_attrs, serialized_node):
        """Mellanox settings for provisioning Serialize mellanox node attrs then it will be merged with common node attributes :param cluster_attrs: cluster attributes :type cluster_attrs: dict :param serialized_node: node attributes data for provisioning :type serialized_node: dict :returns: None"""
        mellanox_data = cluster_attrs.get('neutron_mellanox')
        if mellanox_data:
            serialized_node['ks_meta'].update({'mlnx_vf_num': mellanox_data.get('vf_num'), 'mlnx_plugin_mode': mellanox_data.get('plugin'), 'mlnx_iser_enabled': cluster_attrs.get('storage', {}).get('iser')})
            pm_data = serialized_node['ks_meta']['pm_data']
            if (mellanox_data['plugin'] == 'ethernet' or cluster_attrs['storage']['iser'] is True) and 'intel_iommu=' not in pm_data['kernel_params']:
                pm_data['kernel_params'] += ' intel_iommu=on'


processArguments

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

Class description:
Implementation for @see: IProcessor that provides branching of other processors containers.

Method signatures and docstrings:
- def __init__(self, function, *branches): Construct the branching processor. @see: Contextual.__init__ @param branches: arguments[IBranch] The branches to use in branching.
- def register(self, sources, resolvers, extensions, calls, report): @see: IProcessor.register
- def processArguments(self, arguments, annotations): @see: Contextual.processArguments

Implement the Python class `Brancher` described below.

Class description:
Implementation for @see: IProcessor that provides branching of other processors containers.

Method signatures and docstrings:
- def __init__(self, function, *branches): Construct the branching processor. @see: Contextual.__init__ @param branches: arguments[IBranch] The branches to use in branching.
- def register(self, sources, resolvers, extensions, calls, report): @see: IProcessor.register
- def processArguments(self, arguments, annotations): @see: Contextual.processArguments

<|skeleton|>
class Brancher:
    """Implementation for @see: IProcessor that provides branching of other processors containers."""

    def __init__(self, function, *branches):
        """Construct the branching processor. @see: Contextual.__init__ @param branches: arguments[IBranch] The branches to use in branching."""
        <|body_0|>

    def register(self, sources, resolvers, extensions, calls, report):
        """@see: IProcessor.register"""
        <|body_1|>

    def processArguments(self, arguments, annotations):
        """@see: Contextual.processArguments"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        assert branches, 'At least one branch is required'
        if __debug__:
            for branch in branches:
                assert isinstance(branch, IBranch), 'Invalid branch %s' % branch
        self.branches = branches
        super().__init__(function)
<|end_body_0|>

<|body_start_1|>
        assert isinstance(calls, list), 'Invalid calls %s' % calls
        assert isinstance(report, IReport), 'Invalid report %s' % report
        try:
            merge(resolvers, self.contexts)
        except:
            raise AssemblyError('Cannot merge contexts at:%s' % locationStack(self.function))
        report = report.open('Branching processor at:%s' % locationStack(self.function))
        processings = []
        for branch in self.branches:
            assert isinstance(branch, IBranch), 'Invalid branch %s' % branch
            try:
                processing = branch.process(sources, resolvers, extensions, report)
            except:
                raise AssemblyError('Cannot create processing at:%s' % locationStack(self.function))
            assert processing is None or isinstance(processing, Processing), 'Invalid processing %s' % processing
            processings.append(processing)

        def wrapper(*args, **keyargs):
            self.call(*itertools.chain(args, processings), **keyargs)
        updateWrapper(wrapper, self.call)
        calls.append(wrapper)
<|end_body_1|>

<|body_start_2|>
        arguments, annotations = super().processArguments(arguments, annotations)
        n = len(self.branches)
        if len(arguments) >= n:
            return (arguments[n:], annotations)
        raise ProcessorError("Required function of form 'def processor(self, [chain], processing, ..., contex:Context ...)' for:%s" % locationStack(self.function))
<|end_body_2|>

Solution:
class Brancher:
    """Implementation for @see: IProcessor that provides branching of other processors containers."""

    def __init__(self, function, *branches):
        """Construct the branching processor. @see: Contextual.__init__ @param branches: arguments[IBranch] The branches to use in branching."""
        assert branches, 'At least one branch is required'
        if __debug__:
            for branch in branches:
                assert isinstance(branch, IBranch), 'Invalid branch %s' % branch
        self.branches = branches
        super().__init__(function)

    def register(self, sources, resolvers, extensions, calls, report):
        """@see: IProcessor.register"""
        assert isinstance(calls, list), 'Invalid calls %s' % calls
        assert isinstance(report, IReport), 'Invalid report %s' % report
        try:
            merge(resolvers, self.contexts)
        except:
            raise AssemblyError('Cannot merge contexts at:%s' % locationStack(self.function))
        report = report.open('Branching processor at:%s' % locationStack(self.function))
        processings = []
        for branch in self.branches:
            assert isinstance(branch, IBranch), 'Invalid branch %s' % branch
            try:
                processing = branch.process(sources, resolvers, extensions, report)
            except:
                raise AssemblyError('Cannot create processing at:%s' % locationStack(self.function))
            assert processing is None or isinstance(processing, Processing), 'Invalid processing %s' % processing
            processings.append(processing)

        def wrapper(*args, **keyargs):
            self.call(*itertools.chain(args, processings), **keyargs)
        updateWrapper(wrapper, self.call)
        calls.append(wrapper)

    def processArguments(self, arguments, annotations):
        """@see: Contextual.processArguments"""
        arguments, annotations = super().processArguments(arguments, annotations)
        n = len(self.branches)
        if len(arguments) >= n:
            return (arguments[n:], annotations)
        raise ProcessorError("Required function of form 'def processor(self, [chain], processing, ..., contex:Context ...)' for:%s" % locationStack(self.function))


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

Class description:
A class that holds the information relavent to a complete measurement taken with a D&P Instruments Model 103F MicroFT or Model 202 TurboFT. Attributes: cbb - A DpFile instance holding the cold blackbody information. wbb - A DpFile instance holding the warm blackbody information. sam - A DpFile instance holding the sample information dwr - A DpFile instance holding the downwelling information, or None.

Method signatures and docstrings:
- def __init__(self, cbb_file, wbb_file, sam_file, dwr_file=None, plate=-1): DpMeasurement instance constructor. Arguments: cbb_file - The cold blackbody filename. wbb_file - The warm blackbody filename. sam_file - The sample filename. dwr_file - The downwelling filename. plate - The plate emissivity.
- def _calibrate_measurement(self): Calibrate the data for a measurement.
- def check_consistency(self, lower_wave, upper_wave, tolerance): Check the consistency of the measurement scans. Arguments: lower_wave - The lower wavelength limit to use for the check. upper_wave - The upper wavelength limit to use for the check. tolerance - The minimum acceptable consistency tolerance. Returns: True if the consistency is greater than the specified minimum allowed tolerance, False otherwise.

Implement the Python class `DpMeasurement` described below.

Class description:
A class that holds the information relavent to a complete measurement taken with a D&P Instruments Model 103F MicroFT or Model 202 TurboFT. Attributes: cbb - A DpFile instance holding the cold blackbody information. wbb - A DpFile instance holding the warm blackbody information. sam - A DpFile instance holding the sample information dwr - A DpFile instance holding the downwelling information, or None.

Method signatures and docstrings:
- def __init__(self, cbb_file, wbb_file, sam_file, dwr_file=None, plate=-1): DpMeasurement instance constructor. Arguments: cbb_file - The cold blackbody filename. wbb_file - The warm blackbody filename. sam_file - The sample filename. dwr_file - The downwelling filename. plate - The plate emissivity.
- def _calibrate_measurement(self): Calibrate the data for a measurement.
- def check_consistency(self, lower_wave, upper_wave, tolerance): Check the consistency of the measurement scans. Arguments: lower_wave - The lower wavelength limit to use for the check. upper_wave - The upper wavelength limit to use for the check. tolerance - The minimum acceptable consistency tolerance. Returns: True if the consistency is greater than the specified minimum allowed tolerance, False otherwise.

<|skeleton|>
class DpMeasurement:
    """A class that holds the information relavent to a complete measurement taken with a D&P Instruments Model 103F MicroFT or Model 202 TurboFT. Attributes: cbb - A DpFile instance holding the cold blackbody information. wbb - A DpFile instance holding the warm blackbody information. sam - A DpFile instance holding the sample information dwr - A DpFile instance holding the downwelling information, or None."""

    def __init__(self, cbb_file, wbb_file, sam_file, dwr_file=None, plate=-1):
        """DpMeasurement instance constructor. Arguments: cbb_file - The cold blackbody filename. wbb_file - The warm blackbody filename. sam_file - The sample filename. dwr_file - The downwelling filename. plate - The plate emissivity."""
        <|body_0|>

    def _calibrate_measurement(self):
        """Calibrate the data for a measurement."""
        <|body_1|>

    def check_consistency(self, lower_wave, upper_wave, tolerance):
        """Check the consistency of the measurement scans. Arguments: lower_wave - The lower wavelength limit to use for the check. upper_wave - The upper wavelength limit to use for the check. tolerance - The minimum acceptable consistency tolerance. Returns: True if the consistency is greater than the specified minimum allowed tolerance, False otherwise."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.cbb = DpFile(cbb_file)
        self.wbb = DpFile(wbb_file)
        self.sam = DpFile(sam_file)
        self.plate = plate
        self.model = self.sam.model
        if not dwr_file == None:
            self.dwr = DpFile(dwr_file)
        else:
            self.dwr = None
        self._calibrate_measurement()
<|end_body_0|>

<|body_start_1|>
        cold_blackbody = bb_radiance(self.cbb.header.cbb_temperature + 273.15, self.cbb.data.wavelength)
        warm_blackbody = bb_radiance(self.wbb.header.wbb_temperature + 273.15, self.wbb.data.wavelength)
        self.wbb.data.average_spectrum[0] = 1
        self.wbb.data.average_spectrum[2047] = 1
        calibration_slope = (warm_blackbody - cold_blackbody) / (self.wbb.data.average_spectrum - self.cbb.data.average_spectrum)
        calibration_offset = warm_blackbody - self.wbb.data.average_spectrum * calibration_slope
        self.wbb.calibrate_file(calibration_slope, calibration_offset)
        self.cbb.calibrate_file(calibration_slope, calibration_offset)
        self.sam.calibrate_file(calibration_slope, calibration_offset)
        if not self.dwr is None:
            self.dwr.calibrate_file(calibration_slope, calibration_offset)
            plate_temperature = self.dwr.header.spare_f[0]
            if self.plate == -1:
                plate_emissivity = self.dwr.header.spare_f[1]
            plate_blackbody = bb_radiance(plate_temperature + 273.15, self.dwr.data.wavelength)
            plate_emission = plate_emissivity * plate_blackbody
            self.dwr.data.average_spectrum = (self.dwr.data.average_spectrum - plate_emission) / (1 - plate_emissivity)
<|end_body_1|>

<|body_start_2|>
        errors = []
        errors.append(self.wbb.check_file(lower_wave, upper_wave))
        errors.append(self.cbb.check_file(lower_wave, upper_wave))
        errors.append(self.sam.check_file(lower_wave, upper_wave))
        if not self.dwr is None:
            errors.append(self.dwr.check_file(lower_wave, upper_wave))
        errors = np.array(errors)
        if errors.max() > tolerance:
            return False
        else:
            return True
<|end_body_2|>

Solution:
class DpMeasurement:
    """A class that holds the information relavent to a complete measurement taken with a D&P Instruments Model 103F MicroFT or Model 202 TurboFT. Attributes: cbb - A DpFile instance holding the cold blackbody information. wbb - A DpFile instance holding the warm blackbody information. sam - A DpFile instance holding the sample information dwr - A DpFile instance holding the downwelling information, or None."""

    def __init__(self, cbb_file, wbb_file, sam_file, dwr_file=None, plate=-1):
        """DpMeasurement instance constructor. Arguments: cbb_file - The cold blackbody filename. wbb_file - The warm blackbody filename. sam_file - The sample filename. dwr_file - The downwelling filename. plate - The plate emissivity."""
        self.cbb = DpFile(cbb_file)
        self.wbb = DpFile(wbb_file)
        self.sam = DpFile(sam_file)
        self.plate = plate
        self.model = self.sam.model
        if not dwr_file == None:
            self.dwr = DpFile(dwr_file)
        else:
            self.dwr = None
        self._calibrate_measurement()

    def _calibrate_measurement(self):
        """Calibrate the data for a measurement."""
        cold_blackbody = bb_radiance(self.cbb.header.cbb_temperature + 273.15, self.cbb.data.wavelength)
        warm_blackbody = bb_radiance(self.wbb.header.wbb_temperature + 273.15, self.wbb.data.wavelength)
        self.wbb.data.average_spectrum[0] = 1
        self.wbb.data.average_spectrum[2047] = 1
        calibration_slope = (warm_blackbody - cold_blackbody) / (self.wbb.data.average_spectrum - self.cbb.data.average_spectrum)
        calibration_offset = warm_blackbody - self.wbb.data.average_spectrum * calibration_slope
        self.wbb.calibrate_file(calibration_slope, calibration_offset)
        self.cbb.calibrate_file(calibration_slope, calibration_offset)
        self.sam.calibrate_file(calibration_slope, calibration_offset)
        if not self.dwr is None:
            self.dwr.calibrate_file(calibration_slope, calibration_offset)
            plate_temperature = self.dwr.header.spare_f[0]
            if self.plate == -1:
                plate_emissivity = self.dwr.header.spare_f[1]
            plate_blackbody = bb_radiance(plate_temperature + 273.15, self.dwr.data.wavelength)
            plate_emission = plate_emissivity * plate_blackbody
            self.dwr.data.average_spectrum = (self.dwr.data.average_spectrum - plate_emission) / (1 - plate_emissivity)

    def check_consistency(self, lower_wave, upper_wave, tolerance):
        """Check the consistency of the measurement scans. Arguments: lower_wave - The lower wavelength limit to use for the check. upper_wave - The upper wavelength limit to use for the check. tolerance - The minimum acceptable consistency tolerance. Returns: True if the consistency is greater than the specified minimum allowed tolerance, False otherwise."""
        errors = []
        errors.append(self.wbb.check_file(lower_wave, upper_wave))
        errors.append(self.cbb.check_file(lower_wave, upper_wave))
        errors.append(self.sam.check_file(lower_wave, upper_wave))
        if not self.dwr is None:
            errors.append(self.dwr.check_file(lower_wave, upper_wave))
        errors = np.array(errors)
        if errors.max() > tolerance:
            return False
        else:
            return True


get

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

Class description:
Class that returns datastore statistics in JSON such as the number of a certain entity kind and the amount of total bytes.

Method signatures and docstrings:
- def get(self): Handler for GET request for the datastore statistics. Returns: The JSON output for testing.
- def convert_to_json(self, kind_entities): Converts KindStat entities to a json string. Args: kind_entities: A list of stats.KindStat. Returns: A JSON string containing kind statistic information.

Implement the Python class `DatastoreStats` described below.

Class description:
Class that returns datastore statistics in JSON such as the number of a certain entity kind and the amount of total bytes.

Method signatures and docstrings:
- def get(self): Handler for GET request for the datastore statistics. Returns: The JSON output for testing.
- def convert_to_json(self, kind_entities): Converts KindStat entities to a json string. Args: kind_entities: A list of stats.KindStat. Returns: A JSON string containing kind statistic information.

<|skeleton|>
class DatastoreStats:
    """Class that returns datastore statistics in JSON such as the number of a certain entity kind and the amount of total bytes."""

    def get(self):
        """Handler for GET request for the datastore statistics. Returns: The JSON output for testing."""
        <|body_0|>

    def convert_to_json(self, kind_entities):
        """Converts KindStat entities to a json string. Args: kind_entities: A list of stats.KindStat. Returns: A JSON string containing kind statistic information."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        is_cloud_admin = self.helper.is_user_cloud_admin()
        apps_user_is_admin_on = self.helper.get_owned_apps()
        app_name = self.request.get('appid')
        if not is_cloud_admin and app_name not in apps_user_is_admin_on:
            response = json.dumps({'error': True, 'message': 'Not authorized'})
            self.response.out.write(response)
            return
        query = KindStat.all(_app=app_name)
        time_stamp = datetime.datetime.now() - datetime.timedelta(days=self.MAX_DAYS_BACK)
        query.filter('timestamp >', time_stamp)
        items = query.fetch(self.MAX_KIND_STATS)
        response = self.convert_to_json(items)
        self.response.out.write(response)
        return
<|end_body_0|>

<|body_start_1|>
        items = []
        for ent in kind_entities:
            items.append({time.mktime(ent.timestamp.timetuple()): {ent.kind_name: {'bytes': ent.bytes, 'count': ent.count}}})
        return json.dumps(items)
<|end_body_1|>

Solution:
class DatastoreStats:
    """Class that returns datastore statistics in JSON such as the number of a certain entity kind and the amount of total bytes."""

    def get(self):
        """Handler for GET request for the datastore statistics. Returns: The JSON output for testing."""
        is_cloud_admin = self.helper.is_user_cloud_admin()
        apps_user_is_admin_on = self.helper.get_owned_apps()
        app_name = self.request.get('appid')
        if not is_cloud_admin and app_name not in apps_user_is_admin_on:
            response = json.dumps({'error': True, 'message': 'Not authorized'})
            self.response.out.write(response)
            return
        query = KindStat.all(_app=app_name)
        time_stamp = datetime.datetime.now() - datetime.timedelta(days=self.MAX_DAYS_BACK)
        query.filter('timestamp >', time_stamp)
        items = query.fetch(self.MAX_KIND_STATS)
        response = self.convert_to_json(items)
        self.response.out.write(response)
        return

    def convert_to_json(self, kind_entities):
        """Converts KindStat entities to a json string. Args: kind_entities: A list of stats.KindStat. Returns: A JSON string containing kind statistic information."""
        items = []
        for ent in kind_entities:
            items.append({time.mktime(ent.timestamp.timetuple()): {ent.kind_name: {'bytes': ent.bytes, 'count': ent.count}}})
        return json.dumps(items)


CreateTodo

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

Class description:
Missing associated documentation comment in .proto file.

Method signatures and docstrings:
- def CreateTodo(self, request, context): Missing associated documentation comment in .proto file.
- def GetTodos(self, request, context): Missing associated documentation comment in .proto file.
- def UpdateTodo(self, request, context): Missing associated documentation comment in .proto file.
- def DeleteTodo(self, request, context): Missing associated documentation comment in .proto file.

Implement the Python class `TodoServiceServicer` described below.

Class description:
Missing associated documentation comment in .proto file.

Method signatures and docstrings:
- def CreateTodo(self, request, context): Missing associated documentation comment in .proto file.
- def GetTodos(self, request, context): Missing associated documentation comment in .proto file.
- def UpdateTodo(self, request, context): Missing associated documentation comment in .proto file.
- def DeleteTodo(self, request, context): Missing associated documentation comment in .proto file.

<|skeleton|>
class TodoServiceServicer:
    """Missing associated documentation comment in .proto file."""

    def CreateTodo(self, request, context):
        """Missing associated documentation comment in .proto file."""
        <|body_0|>

    def GetTodos(self, request, context):
        """Missing associated documentation comment in .proto file."""
        <|body_1|>

    def UpdateTodo(self, request, context):
        """Missing associated documentation comment in .proto file."""
        <|body_2|>

    def DeleteTodo(self, request, context):
        """Missing associated documentation comment in .proto file."""
        <|body_3|>

<|end_skeleton|>

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

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

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

<|body_start_3|>
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')
<|end_body_3|>

Solution:
class TodoServiceServicer:
    """Missing associated documentation comment in .proto file."""

    def CreateTodo(self, request, context):
        """Missing associated documentation comment in .proto file."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def GetTodos(self, request, context):
        """Missing associated documentation comment in .proto file."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def UpdateTodo(self, request, context):
        """Missing associated documentation comment in .proto file."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def DeleteTodo(self, request, context):
        """Missing associated documentation comment in .proto file."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')


dist

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

Class description:
Statistical distribution

Method signatures and docstrings:
- def __new__(cls, name: str, *args, rng=None, dims: Optional[Dims]=None, initval=None, observed=None, total_size=None, transform=UNSET, **kwargs) -> TensorVariable: Adds a tensor variable corresponding to a PyMC distribution to the current model. Note that all remaining kwargs must be compatible with ``.dist()`` Parameters ---------- cls : type A PyMC distribution. name : str Name for the new model variable. rng : optional Random number generator to use with the RandomVariable. dims : tuple, optional A tuple of dimension names known to the model. When shape is not provided, the shape of dims is used to define the shape of the variable. initval : optional Numeric or symbolic untransformed initial value of matching shape, or one of the following initial value strategies: "moment", "prior". Depending on the sampler's settings, a random jitter may be added
- def dist(cls, dist_params, *, shape: Optional[Shape]=None, **kwargs) -> TensorVariable: Creates a tensor variable corresponding to the `cls` distribution. Parameters ---------- dist_params : array-like The inputs to the `RandomVariable` `Op`. shape : int, tuple, Variable, optional A tuple of sizes for each dimension of the new RV. **kwargs Keyword arguments that will be forwarded to the PyTensor RV Op. Most prominently: ``size`` or ``dtype``. Returns ------- rv : TensorVariable The created random variable tensor.

Implement the Python class `Distribution` described below.

Class description:
Statistical distribution

Method signatures and docstrings:
- def __new__(cls, name: str, *args, rng=None, dims: Optional[Dims]=None, initval=None, observed=None, total_size=None, transform=UNSET, **kwargs) -> TensorVariable: Adds a tensor variable corresponding to a PyMC distribution to the current model. Note that all remaining kwargs must be compatible with ``.dist()`` Parameters ---------- cls : type A PyMC distribution. name : str Name for the new model variable. rng : optional Random number generator to use with the RandomVariable. dims : tuple, optional A tuple of dimension names known to the model. When shape is not provided, the shape of dims is used to define the shape of the variable. initval : optional Numeric or symbolic untransformed initial value of matching shape, or one of the following initial value strategies: "moment", "prior". Depending on the sampler's settings, a random jitter may be added
- def dist(cls, dist_params, *, shape: Optional[Shape]=None, **kwargs) -> TensorVariable: Creates a tensor variable corresponding to the `cls` distribution. Parameters ---------- dist_params : array-like The inputs to the `RandomVariable` `Op`. shape : int, tuple, Variable, optional A tuple of sizes for each dimension of the new RV. **kwargs Keyword arguments that will be forwarded to the PyTensor RV Op. Most prominently: ``size`` or ``dtype``. Returns ------- rv : TensorVariable The created random variable tensor.

<|skeleton|>
class Distribution:
    """Statistical distribution"""

    def __new__(cls, name: str, *args, rng=None, dims: Optional[Dims]=None, initval=None, observed=None, total_size=None, transform=UNSET, **kwargs) -> TensorVariable:
        """Adds a tensor variable corresponding to a PyMC distribution to the current model. Note that all remaining kwargs must be compatible with ``.dist()`` Parameters ---------- cls : type A PyMC distribution. name : str Name for the new model variable. rng : optional Random number generator to use with the RandomVariable. dims : tuple, optional A tuple of dimension names known to the model. When shape is not provided, the shape of dims is used to define the shape of the variable. initval : optional Numeric or symbolic untransformed initial value of matching shape, or one of the following initial value strategies: "moment", "prior". Depending on the sampler's settings, a random jitter may be added"""
        <|body_0|>

    def dist(cls, dist_params, *, shape: Optional[Shape]=None, **kwargs) -> TensorVariable:
        """Creates a tensor variable corresponding to the `cls` distribution. Parameters ---------- dist_params : array-like The inputs to the `RandomVariable` `Op`. shape : int, tuple, Variable, optional A tuple of sizes for each dimension of the new RV. **kwargs Keyword arguments that will be forwarded to the PyTensor RV Op. Most prominently: ``size`` or ``dtype``. Returns ------- rv : TensorVariable The created random variable tensor."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            from pymc.model import Model
            model = Model.get_context()
        except TypeError:
            raise TypeError("No model on context stack, which is needed to instantiate distributions. Add variable inside a 'with model:' block, or use the '.dist' syntax for a standalone distribution.")
        if 'testval' in kwargs:
            initval = kwargs.pop('testval')
            warnings.warn('The `testval` argument is deprecated; use `initval`.', FutureWarning, stacklevel=2)
        if not isinstance(name, string_types):
            raise TypeError(f'Name needs to be a string but got: {name}')
        dims = convert_dims(dims)
        if observed is not None:
            observed = convert_observed_data(observed)
        if kwargs.get('size') is None and kwargs.get('shape') is None:
            if dims is not None:
                kwargs['shape'] = shape_from_dims(dims, model)
            elif observed is not None:
                kwargs['shape'] = tuple(observed.shape)
        rv_out = cls.dist(*args, **kwargs)
        rv_out = model.register_rv(rv_out, name, observed, total_size, dims=dims, transform=transform, initval=initval)
        rv_out.str_repr = types.MethodType(str_for_dist, rv_out)
        rv_out._repr_latex_ = types.MethodType(functools.partial(str_for_dist, formatting='latex'), rv_out)
        rv_out.logp = _make_nice_attr_error('rv.logp(x)', 'pm.logp(rv, x)')
        rv_out.logcdf = _make_nice_attr_error('rv.logcdf(x)', 'pm.logcdf(rv, x)')
        rv_out.random = _make_nice_attr_error('rv.random()', 'pm.draw(rv)')
        return rv_out
<|end_body_0|>

<|body_start_1|>
        if 'testval' in kwargs:
            kwargs.pop('testval')
            warnings.warn("The `.dist(testval=...)` argument is deprecated and has no effect. Initial values for sampling/optimization can be specified with `initval` in a modelcontext. For using PyTensor's test value features, you must assign the `.tag.test_value` yourself.", FutureWarning, stacklevel=2)
        if 'initval' in kwargs:
            raise TypeError('Unexpected keyword argument `initval`. This argument is not available for the `.dist()` API.')
        if 'dims' in kwargs:
            raise NotImplementedError('The use of a `.dist(dims=...)` API is not supported.')
        size = kwargs.pop('size', None)
        if shape is not None and size is not None:
            raise ValueError(f'Passing both `shape` ({shape}) and `size` ({size}) is not supported!')
        shape = convert_shape(shape)
        size = convert_size(size)
        ndim_supp = getattr(cls.rv_op, 'ndim_supp', None)
        if ndim_supp is None:
            ndim_supp = cls.rv_op(*dist_params, **kwargs).owner.op.ndim_supp
        create_size = find_size(shape=shape, size=size, ndim_supp=ndim_supp)
        rv_out = cls.rv_op(*dist_params, size=create_size, **kwargs)
        rv_out.logp = _make_nice_attr_error('rv.logp(x)', 'pm.logp(rv, x)')
        rv_out.logcdf = _make_nice_attr_error('rv.logcdf(x)', 'pm.logcdf(rv, x)')
        rv_out.random = _make_nice_attr_error('rv.random()', 'pm.draw(rv)')
        _add_future_warning_tag(rv_out)
        return rv_out
<|end_body_1|>

Solution:
class Distribution:
    """Statistical distribution"""

    def __new__(cls, name: str, *args, rng=None, dims: Optional[Dims]=None, initval=None, observed=None, total_size=None, transform=UNSET, **kwargs) -> TensorVariable:
        """Adds a tensor variable corresponding to a PyMC distribution to the current model. Note that all remaining kwargs must be compatible with ``.dist()`` Parameters ---------- cls : type A PyMC distribution. name : str Name for the new model variable. rng : optional Random number generator to use with the RandomVariable. dims : tuple, optional A tuple of dimension names known to the model. When shape is not provided, the shape of dims is used to define the shape of the variable. initval : optional Numeric or symbolic untransformed initial value of matching shape, or one of the following initial value strategies: "moment", "prior". Depending on the sampler's settings, a random jitter may be added"""
        try:
            from pymc.model import Model
            model = Model.get_context()
        except TypeError:
            raise TypeError("No model on context stack, which is needed to instantiate distributions. Add variable inside a 'with model:' block, or use the '.dist' syntax for a standalone distribution.")
        if 'testval' in kwargs:
            initval = kwargs.pop('testval')
            warnings.warn('The `testval` argument is deprecated; use `initval`.', FutureWarning, stacklevel=2)
        if not isinstance(name, string_types):
            raise TypeError(f'Name needs to be a string but got: {name}')
        dims = convert_dims(dims)
        if observed is not None:
            observed = convert_observed_data(observed)
        if kwargs.get('size') is None and kwargs.get('shape') is None:
            if dims is not None:
                kwargs['shape'] = shape_from_dims(dims, model)
            elif observed is not None:
                kwargs['shape'] = tuple(observed.shape)
        rv_out = cls.dist(*args, **kwargs)
        rv_out = model.register_rv(rv_out, name, observed, total_size, dims=dims, transform=transform, initval=initval)
        rv_out.str_repr = types.MethodType(str_for_dist, rv_out)
        rv_out._repr_latex_ = types.MethodType(functools.partial(str_for_dist, formatting='latex'), rv_out)
        rv_out.logp = _make_nice_attr_error('rv.logp(x)', 'pm.logp(rv, x)')
        rv_out.logcdf = _make_nice_attr_error('rv.logcdf(x)', 'pm.logcdf(rv, x)')
        rv_out.random = _make_nice_attr_error('rv.random()', 'pm.draw(rv)')
        return rv_out

    def dist(cls, dist_params, *, shape: Optional[Shape]=None, **kwargs) -> TensorVariable:
        """Creates a tensor variable corresponding to the `cls` distribution. Parameters ---------- dist_params : array-like The inputs to the `RandomVariable` `Op`. shape : int, tuple, Variable, optional A tuple of sizes for each dimension of the new RV. **kwargs Keyword arguments that will be forwarded to the PyTensor RV Op. Most prominently: ``size`` or ``dtype``. Returns ------- rv : TensorVariable The created random variable tensor."""
        if 'testval' in kwargs:
            kwargs.pop('testval')
            warnings.warn("The `.dist(testval=...)` argument is deprecated and has no effect. Initial values for sampling/optimization can be specified with `initval` in a modelcontext. For using PyTensor's test value features, you must assign the `.tag.test_value` yourself.", FutureWarning, stacklevel=2)
        if 'initval' in kwargs:
            raise TypeError('Unexpected keyword argument `initval`. This argument is not available for the `.dist()` API.')
        if 'dims' in kwargs:
            raise NotImplementedError('The use of a `.dist(dims=...)` API is not supported.')
        size = kwargs.pop('size', None)
        if shape is not None and size is not None:
            raise ValueError(f'Passing both `shape` ({shape}) and `size` ({size}) is not supported!')
        shape = convert_shape(shape)
        size = convert_size(size)
        ndim_supp = getattr(cls.rv_op, 'ndim_supp', None)
        if ndim_supp is None:
            ndim_supp = cls.rv_op(*dist_params, **kwargs).owner.op.ndim_supp
        create_size = find_size(shape=shape, size=size, ndim_supp=ndim_supp)
        rv_out = cls.rv_op(*dist_params, size=create_size, **kwargs)
        rv_out.logp = _make_nice_attr_error('rv.logp(x)', 'pm.logp(rv, x)')
        rv_out.logcdf = _make_nice_attr_error('rv.logcdf(x)', 'pm.logcdf(rv, x)')
        rv_out.random = _make_nice_attr_error('rv.random()', 'pm.draw(rv)')
        _add_future_warning_tag(rv_out)
        return rv_out


provenance

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

Class description:
Implement the clean_energy class.

Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets and store in mongodb collections.
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event.

Implement the Python class `clean_energy` described below.

Class description:
Implement the clean_energy class.

Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets and store in mongodb collections.
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event.

<|skeleton|>
class clean_energy:

    def execute(trial=False):
        """Retrieve some data sets and store in mongodb collections."""
        <|body_0|>

    def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
        """Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        startTime = datetime.datetime.now()
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ajr10_williami', 'ajr10_williami')
        repo.dropCollection('ajr10_williami.cleaned_energy_cambridge')
        repo.createCollection('ajr10_williami.cleaned_energy_cambridge')
        repo.dropCollection('ajr10_williami.cleaned_energy_boston')
        repo.createCollection('ajr10_williami.cleaned_energy_boston')
        energy_cambridge = repo['ajr10_williami.energy_cambridge'].find()
        energy_boston = repo['ajr10_williami.energy_boston'].find()
        for cambridge_energy in energy_cambridge:
            CO2 = cambridge_energy['co2_kg']
            mmbtu = cambridge_energy['use_mmbtu']
            new_energy = {}
            new_energy['CO2'] = CO2
            new_energy['mmbtu'] = mmbtu
            repo['ajr10_williami.cleaned_energy_cambridge'].insert(new_energy)
        for boston_energy in energy_boston:
            CO2 = boston_energy['emission_co2']
            mmbtu = boston_energy['use_mmbtu']
            new_energy = {}
            new_energy['CO2'] = CO2
            new_energy['mmbtu'] = mmbtu
            repo['ajr10_williami.cleaned_energy_boston'].insert(new_energy)
        repo.logout()
        endTime = datetime.datetime.now()
        return {'start': startTime, 'end': endTime}
<|end_body_0|>

<|body_start_1|>
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ajr10_williami', 'ajr10_williami')
        doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
        doc.add_namespace('dat', 'http://datamechanics.io/data/')
        doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
        doc.add_namespace('log', 'http://datamechanics.io/log/')
        doc.add_namespace('awc', 'ajr10_williami')
        this_script = doc.agent('alg:ajr10_williami#clean_energy', {prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
        energy_cambridge_resource = doc.entity('awc:energy_cambridge', {'prov:label': 'ajr10_williami.cleaned_energy_cambridge, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        energy_boston_resource = doc.entity('awc:energy_boston', {'prov:label': 'ajr10_williami.cleaned_energy_boston, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        get_energys_cambridge = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        get_energys_boston = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        doc.wasAssociatedWith(get_energys_cambridge, this_script)
        doc.wasAssociatedWith(get_energys_boston, this_script)
        doc.usage(get_energys_cambridge, energy_cambridge_resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '?type=Get+Energys+Cambridge'})
        doc.usage(get_energys_boston, energy_boston_resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '?type=Get+Energys+Boston'})
        energys_cambridge = doc.entity('dat:ajr10_williami#cleaned_energy_cambridge', {prov.model.PROV_LABEL: 'Energys Cambridge', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(energys_cambridge, this_script)
        doc.wasGeneratedBy(energys_cambridge, get_energys_cambridge, endTime)
        doc.wasDerivedFrom(energys_cambridge, energy_cambridge_resource, get_energys_cambridge, get_energys_cambridge, get_energys_cambridge)
        energys_boston = doc.entity('dat:ajr10_williami#cleaned_energy_boston', {prov.model.PROV_LABEL: 'Energys Boston', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(energys_boston, this_script)
        doc.wasGeneratedBy(energys_boston, get_energys_boston, endTime)
        doc.wasDerivedFrom(energys_boston, energy_boston_resource, get_energys_boston, get_energys_boston, get_energys_boston)
        repo.logout()
        return doc
<|end_body_1|>

Solution:
class clean_energy:
    def execute(trial=False):
        """Retrieve some data sets and store in mongodb collections."""
        startTime = datetime.datetime.now()
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ajr10_williami', 'ajr10_williami')
        repo.dropCollection('ajr10_williami.cleaned_energy_cambridge')
        repo.createCollection('ajr10_williami.cleaned_energy_cambridge')
        repo.dropCollection('ajr10_williami.cleaned_energy_boston')
        repo.createCollection('ajr10_williami.cleaned_energy_boston')
        energy_cambridge = repo['ajr10_williami.energy_cambridge'].find()
        energy_boston = repo['ajr10_williami.energy_boston'].find()
        for cambridge_energy in energy_cambridge:
            CO2 = cambridge_energy['co2_kg']
            mmbtu = cambridge_energy['use_mmbtu']
            new_energy = {}
            new_energy['CO2'] = CO2
            new_energy['mmbtu'] = mmbtu
            repo['ajr10_williami.cleaned_energy_cambridge'].insert(new_energy)
        for boston_energy in energy_boston:
            CO2 = boston_energy['emission_co2']
            mmbtu = boston_energy['use_mmbtu']
            new_energy = {}
            new_energy['CO2'] = CO2
            new_energy['mmbtu'] = mmbtu
            repo['ajr10_williami.cleaned_energy_boston'].insert(new_energy)
        repo.logout()
        endTime = datetime.datetime.now()
        return {'start': startTime, 'end': endTime}

    def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
        """Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event."""
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ajr10_williami', 'ajr10_williami')
        doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
        doc.add_namespace('dat', 'http://datamechanics.io/data/')
        doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
        doc.add_namespace('log', 'http://datamechanics.io/log/')
        doc.add_namespace('awc', 'ajr10_williami')
        this_script = doc.agent('alg:ajr10_williami#clean_energy', {prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
        energy_cambridge_resource = doc.entity('awc:energy_cambridge', {'prov:label': 'ajr10_williami.cleaned_energy_cambridge, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        energy_boston_resource = doc.entity('awc:energy_boston', {'prov:label': 'ajr10_williami.cleaned_energy_boston, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        get_energys_cambridge = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        get_energys_boston = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        doc.wasAssociatedWith(get_energys_cambridge, this_script)
        doc.wasAssociatedWith(get_energys_boston, this_script)
        doc.usage(get_energys_cambridge, energy_cambridge_resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '?type=Get+Energys+Cambridge'})
        doc.usage(get_energys_boston, energy_boston_resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '?type=Get+Energys+Boston'})
        energys_cambridge = doc.entity('dat:ajr10_williami#cleaned_energy_cambridge', {prov.model.PROV_LABEL: 'Energys Cambridge', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(energys_cambridge, this_script)
        doc.wasGeneratedBy(energys_cambridge, get_energys_cambridge, endTime)
        doc.wasDerivedFrom(energys_cambridge, energy_cambridge_resource, get_energys_cambridge, get_energys_cambridge, get_energys_cambridge)
        energys_boston = doc.entity('dat:ajr10_williami#cleaned_energy_boston', {prov.model.PROV_LABEL: 'Energys Boston', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(energys_boston, this_script)
        doc.wasGeneratedBy(energys_boston, get_energys_boston, endTime)
        doc.wasDerivedFrom(energys_boston, energy_boston_resource, get_energys_boston, get_energys_boston, get_energys_boston)
        repo.logout()
        return doc


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

Class description:
OAuth ToolProvider that works with Django requests.

Method signatures and docstrings:
- def success_redirect(self, msg='', log=''): Shortcut redirecting view to LTI Consumer with messages.
- def error_redirect(self, errormsg='', errorlog=''): Shortcut for redirecting view to LTI Consumer with errors.

Implement the Python class `DjangoToolProvider` described below.

Class description:
OAuth ToolProvider that works with Django requests.

Method signatures and docstrings:
- def success_redirect(self, msg='', log=''): Shortcut redirecting view to LTI Consumer with messages.
- def error_redirect(self, errormsg='', errorlog=''): Shortcut for redirecting view to LTI Consumer with errors.

<|skeleton|>
class DjangoToolProvider:
    """OAuth ToolProvider that works with Django requests."""

    def success_redirect(self, msg='', log=''):
        """Shortcut redirecting view to LTI Consumer with messages."""
        <|body_0|>

    def error_redirect(self, errormsg='', errorlog=''):
        """Shortcut for redirecting view to LTI Consumer with errors."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.lti_msg = msg
        self.lti_log = log
        return redirect(self.build_return_url())
<|end_body_0|>

<|body_start_1|>
        self.lti_errormsg = errormsg
        self.lti_errorlog = errorlog
        return redirect(self.build_return_url())
<|end_body_1|>

Solution:
class DjangoToolProvider:
    """OAuth ToolProvider that works with Django requests."""

    def success_redirect(self, msg='', log=''):
        """Shortcut redirecting view to LTI Consumer with messages."""
        self.lti_msg = msg
        self.lti_log = log
        return redirect(self.build_return_url())

    def error_redirect(self, errormsg='', errorlog=''):
        """Shortcut for redirecting view to LTI Consumer with errors."""
        self.lti_errormsg = errormsg
        self.lti_errorlog = errorlog
        return redirect(self.build_return_url())


DeleteJob

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

Class description:
The JobController provides methods to manage jobs.

Method signatures and docstrings:
- def SubmitJob(self, request, context): Submits a job to a cluster.
- def GetJob(self, request, context): Gets the resource representation for a job in a project.
- def ListJobs(self, request, context): Lists regions/{region}/jobs in a project.
- def UpdateJob(self, request, context): Updates a job in a project.
- def CancelJob(self, request, context): Starts a job cancellation request. To access the job resource after cancellation, call [regions/{region}/jobs.list](/dataproc/docs/reference/rest/v1/projects.regions.jobs/list) or [regions/{region}/jobs.get](/dataproc/docs/reference/rest/v1/projects.regions.jobs/get).
- def DeleteJob(self, request, context): Deletes the job from the project. If the job is active, the delete fails, and the response returns `FAILED_PRECONDITION`.

Implement the Python class `JobControllerServicer` described below.

Class description:
The JobController provides methods to manage jobs.

Method signatures and docstrings:
- def SubmitJob(self, request, context): Submits a job to a cluster.
- def GetJob(self, request, context): Gets the resource representation for a job in a project.
- def ListJobs(self, request, context): Lists regions/{region}/jobs in a project.
- def UpdateJob(self, request, context): Updates a job in a project.
- def CancelJob(self, request, context): Starts a job cancellation request. To access the job resource after cancellation, call [regions/{region}/jobs.list](/dataproc/docs/reference/rest/v1/projects.regions.jobs/list) or [regions/{region}/jobs.get](/dataproc/docs/reference/rest/v1/projects.regions.jobs/get).
- def DeleteJob(self, request, context): Deletes the job from the project. If the job is active, the delete fails, and the response returns `FAILED_PRECONDITION`.

<|skeleton|>
class JobControllerServicer:
    """The JobController provides methods to manage jobs."""

    def SubmitJob(self, request, context):
        """Submits a job to a cluster."""
        <|body_0|>

    def GetJob(self, request, context):
        """Gets the resource representation for a job in a project."""
        <|body_1|>

    def ListJobs(self, request, context):
        """Lists regions/{region}/jobs in a project."""
        <|body_2|>

    def UpdateJob(self, request, context):
        """Updates a job in a project."""
        <|body_3|>

    def CancelJob(self, request, context):
        """Starts a job cancellation request. To access the job resource after cancellation, call [regions/{region}/jobs.list](/dataproc/docs/reference/rest/v1/projects.regions.jobs/list) or [regions/{region}/jobs.get](/dataproc/docs/reference/rest/v1/projects.regions.jobs/get)."""
        <|body_4|>

    def DeleteJob(self, request, context):
        """Deletes the job from the project. If the job is active, the delete fails, and the response returns `FAILED_PRECONDITION`."""
        <|body_5|>

<|end_skeleton|>

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

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

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

<|body_start_3|>
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')
<|end_body_3|>

<|body_start_4|>
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')
<|end_body_4|>

<|body_start_5|>
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')
<|end_body_5|>

Solution:
class JobControllerServicer:
    """The JobController provides methods to manage jobs."""

    def SubmitJob(self, request, context):
        """Submits a job to a cluster."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def GetJob(self, request, context):
        """Gets the resource representation for a job in a project."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def ListJobs(self, request, context):
        """Lists regions/{region}/jobs in a project."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def UpdateJob(self, request, context):
        """Updates a job in a project."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def CancelJob(self, request, context):
        """Starts a job cancellation request. To access the job resource after cancellation, call [regions/{region}/jobs.list](/dataproc/docs/reference/rest/v1/projects.regions.jobs/list) or [regions/{region}/jobs.get](/dataproc/docs/reference/rest/v1/projects.regions.jobs/get)."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def DeleteJob(self, request, context):
        """Deletes the job from the project. If the job is active, the delete fails, and the response returns `FAILED_PRECONDITION`."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')


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

Class description:
This class used to store purchase of products

Method signatures and docstrings:
- def create_purchase_order(self, products, vendor_name): func :- Used to create new purchase order. params :- products' names, quantity, type and price - dictionary params :- vendor name - string returns :- Error msg if more than purchase price else purchase number
- def confirm_purchase_order(self, purchase_order): func :- Used to manage purchased products params :- purchase order - string returns :- True or False as Order confirms

Implement the Python class `Purchase` described below.

Class description:
This class used to store purchase of products

Method signatures and docstrings:
- def create_purchase_order(self, products, vendor_name): func :- Used to create new purchase order. params :- products' names, quantity, type and price - dictionary params :- vendor name - string returns :- Error msg if more than purchase price else purchase number
- def confirm_purchase_order(self, purchase_order): func :- Used to manage purchased products params :- purchase order - string returns :- True or False as Order confirms

<|skeleton|>
class Purchase:
    """This class used to store purchase of products"""

    def create_purchase_order(self, products, vendor_name):
        """func :- Used to create new purchase order. params :- products' names, quantity, type and price - dictionary params :- vendor name - string returns :- Error msg if more than purchase price else purchase number"""
        <|body_0|>

    def confirm_purchase_order(self, purchase_order):
        """func :- Used to manage purchased products params :- purchase order - string returns :- True or False as Order confirms"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        tempDictionary = {}
        tempDictionary['total_price'] = 0
        for product in products:
            if not product['name'] in self.products_data:
                new_product = super(Purchase, self).__init__(product['name'], product['type'], 0, product['price'])
            product_price = product['quantity'] * product['price']
            tax = product_price * self.products_data[product['name']]['purchase_tax'] / 100
            product['total_price'] = product_price + tax
            tempDictionary['total_price'] += product['total_price']
            if self.products_data[product['name']]['max_purchase'] < product['total_price']:
                if self.products_data[product['name']]['onhand'] == 0:
                    del self.products_data[product['name']]
                print('Purchase price limit exceeds...')
                return False
        tempDictionary['date'] = datetime.datetime.utcnow()
        tempDictionary['state'] = 'Confirm'
        tempDictionary['products'] = products
        tempDictionary['vendor_name'] = vendor_name
        order = list(sorted(self.purchase_details.keys()))
        if order == []:
            key = 'PO/0001'
        else:
            number = re.findall('\\d+', order[-1])[0]
            number = int(number) + 1
            key = 'PO/' + str(number).zfill(4)
        self.purchase_details[key] = tempDictionary
        return key
<|end_body_0|>

<|body_start_1|>
        order = self.purchase_details[purchase_order]
        order['state'] = 'Done'
        for product in order['products']:
            pro_data = self.products_data[product['name']]
            pro_data['onhand'] += product['quantity']
            self.products_data[product['name']]['purchase_order'].append(purchase_order)
        return True
<|end_body_1|>

Solution:
class Purchase:
    """This class used to store purchase of products"""

    def create_purchase_order(self, products, vendor_name):
        """func :- Used to create new purchase order. params :- products' names, quantity, type and price - dictionary params :- vendor name - string returns :- Error msg if more than purchase price else purchase number"""
        tempDictionary = {}
        tempDictionary['total_price'] = 0
        for product in products:
            if not product['name'] in self.products_data:
                new_product = super(Purchase, self).__init__(product['name'], product['type'], 0, product['price'])
            product_price = product['quantity'] * product['price']
            tax = product_price * self.products_data[product['name']]['purchase_tax'] / 100
            product['total_price'] = product_price + tax
            tempDictionary['total_price'] += product['total_price']
            if self.products_data[product['name']]['max_purchase'] < product['total_price']:
                if self.products_data[product['name']]['onhand'] == 0:
                    del self.products_data[product['name']]
                print('Purchase price limit exceeds...')
                return False
        tempDictionary['date'] = datetime.datetime.utcnow()
        tempDictionary['state'] = 'Confirm'
        tempDictionary['products'] = products
        tempDictionary['vendor_name'] = vendor_name
        order = list(sorted(self.purchase_details.keys()))
        if order == []:
            key = 'PO/0001'
        else:
            number = re.findall('\\d+', order[-1])[0]
            number = int(number) + 1
            key = 'PO/' + str(number).zfill(4)
        self.purchase_details[key] = tempDictionary
        return key

    def confirm_purchase_order(self, purchase_order):
        """func :- Used to manage purchased products params :- purchase order - string returns :- True or False as Order confirms"""
        order = self.purchase_details[purchase_order]
        order['state'] = 'Done'
        for product in order['products']:
            pro_data = self.products_data[product['name']]
            pro_data['onhand'] += product['quantity']
            self.products_data[product['name']]['purchase_order'].append(purchase_order)
        return True


forward

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

Class description:
Simple Network with atleast 2 conv2d layers and two linear layers.

Method signatures and docstrings:
- def __init__(self): Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means Hints: 1. Refer to https://pytorch.org/docs/stable/nn.html for layers 2. Remember to use non-linearities in your network. Network without non-linearities is not deep. 3. You will get 4D tensor for an image input from self.conv_layers. You need to process it and make it a compatible tensor input for self.fc_layers.
- def forward(self, x: torch.tensor) -> torch.tensor: Perform the forward pass with the net Note: do not perform soft-max or convert to probabilities in this function Args: - x: the input image [Dim: (N,C,H,W)] Returns: - y: the output (raw scores) of the net [Dim: (N,15)]

Implement the Python class `SimpleNet` described below.

Class description:
Simple Network with atleast 2 conv2d layers and two linear layers.

Method signatures and docstrings:
- def __init__(self): Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means Hints: 1. Refer to https://pytorch.org/docs/stable/nn.html for layers 2. Remember to use non-linearities in your network. Network without non-linearities is not deep. 3. You will get 4D tensor for an image input from self.conv_layers. You need to process it and make it a compatible tensor input for self.fc_layers.
- def forward(self, x: torch.tensor) -> torch.tensor: Perform the forward pass with the net Note: do not perform soft-max or convert to probabilities in this function Args: - x: the input image [Dim: (N,C,H,W)] Returns: - y: the output (raw scores) of the net [Dim: (N,15)]

<|skeleton|>
class SimpleNet:
    """Simple Network with atleast 2 conv2d layers and two linear layers."""

    def __init__(self):
        """Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means Hints: 1. Refer to https://pytorch.org/docs/stable/nn.html for layers 2. Remember to use non-linearities in your network. Network without non-linearities is not deep. 3. You will get 4D tensor for an image input from self.conv_layers. You need to process it and make it a compatible tensor input for self.fc_layers."""
        <|body_0|>

    def forward(self, x: torch.tensor) -> torch.tensor:
        """Perform the forward pass with the net Note: do not perform soft-max or convert to probabilities in this function Args: - x: the input image [Dim: (N,C,H,W)] Returns: - y: the output (raw scores) of the net [Dim: (N,15)]"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.conv_layers = nn.Sequential()
        self.fc_layers = nn.Sequential()
        self.loss_criterion = None
        self.loss_criterion = nn.CrossEntropyLoss(reduction='sum')
        self.conv_layers = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5, bias=False), nn.MaxPool2d(kernel_size=3, stride=3), nn.ReLU(), nn.Conv2d(in_channels=10, out_channels=20, kernel_size=5, bias=False), nn.MaxPool2d(kernel_size=3, stride=3), nn.ReLU())
        self.fc_layers = nn.Sequential(nn.Flatten(), nn.Linear(500, 100), nn.Linear(100, 15))
<|end_body_0|>

<|body_start_1|>
        conv_features = None
        flattened_conv_features = None
        model_output = None
        conv_out = self.conv_layers(x)
        model_output = self.fc_layers(conv_out)
        return model_output
<|end_body_1|>

Solution:
class SimpleNet:
    """Simple Network with atleast 2 conv2d layers and two linear layers."""

    def __init__(self):
        """Init function to define the layers and loss function Note: Use 'sum' reduction in the loss_criterion. Read Pytorch documention to understand what it means Hints: 1. Refer to https://pytorch.org/docs/stable/nn.html for layers 2. Remember to use non-linearities in your network. Network without non-linearities is not deep. 3. You will get 4D tensor for an image input from self.conv_layers. You need to process it and make it a compatible tensor input for self.fc_layers."""
        super().__init__()
        self.conv_layers = nn.Sequential()
        self.fc_layers = nn.Sequential()
        self.loss_criterion = None
        self.loss_criterion = nn.CrossEntropyLoss(reduction='sum')
        self.conv_layers = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5, bias=False), nn.MaxPool2d(kernel_size=3, stride=3), nn.ReLU(), nn.Conv2d(in_channels=10, out_channels=20, kernel_size=5, bias=False), nn.MaxPool2d(kernel_size=3, stride=3), nn.ReLU())
        self.fc_layers = nn.Sequential(nn.Flatten(), nn.Linear(500, 100), nn.Linear(100, 15))

    def forward(self, x: torch.tensor) -> torch.tensor:
        """Perform the forward pass with the net Note: do not perform soft-max or convert to probabilities in this function Args: - x: the input image [Dim: (N,C,H,W)] Returns: - y: the output (raw scores) of the net [Dim: (N,15)]"""
        conv_features = None
        flattened_conv_features = None
        model_output = None
        conv_out = self.conv_layers(x)
        model_output = self.fc_layers(conv_out)
        return model_output


draw

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

Class description:
Implement the Circle class.

Method signatures and docstrings:
- def __init__(self, turtle, color, x, y, radius): This constructor initializes variables and an inherited class that will be needed for each circle.
- def draw(self): Draws the circle that is represented by the stored variables.

Implement the Python class `Circle` described below.

Class description:
Implement the Circle class.

Method signatures and docstrings:
- def __init__(self, turtle, color, x, y, radius): This constructor initializes variables and an inherited class that will be needed for each circle.
- def draw(self): Draws the circle that is represented by the stored variables.

<|skeleton|>
class Circle:

    def __init__(self, turtle, color, x, y, radius):
        """This constructor initializes variables and an inherited class that will be needed for each circle."""
        <|body_0|>

    def draw(self):
        """Draws the circle that is represented by the stored variables."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        Shape.__init__(self, turtle, color)
        self._x = x
        self._y = y
        self._radius = radius
<|end_body_0|>

<|body_start_1|>
        circumference = 2 * 3.14159 * self._radius
        self._turtle.up()
        self._turtle.move(self._x + self._radius, self._y)
        self._turtle.setDirection(90)
        self._turtle.down()
        for i in xrange(120):
            self._turtle.move(circumference / 120)
            self._turtle.turn(3)
        self._turtle.up()
<|end_body_1|>

Solution:
class Circle:
    def __init__(self, turtle, color, x, y, radius):
        """This constructor initializes variables and an inherited class that will be needed for each circle."""
        Shape.__init__(self, turtle, color)
        self._x = x
        self._y = y
        self._radius = radius

    def draw(self):
        """Draws the circle that is represented by the stored variables."""
        circumference = 2 * 3.14159 * self._radius
        self._turtle.up()
        self._turtle.move(self._x + self._radius, self._y)
        self._turtle.setDirection(90)
        self._turtle.down()
        for i in xrange(120):
            self._turtle.move(circumference / 120)
            self._turtle.turn(3)
        self._turtle.up()


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

Class description:
Implement the ExamRoom class.

Method signatures and docstrings:
- def __init__(self, N): :type N: int
- def seat(self): :rtype: int
- def leave(self, p): :type p: int :rtype: void

Implement the Python class `ExamRoom` described below.

Class description:
Implement the ExamRoom class.

Method signatures and docstrings:
- def __init__(self, N): :type N: int
- def seat(self): :rtype: int
- def leave(self, p): :type p: int :rtype: void

<|skeleton|>
class ExamRoom:

    def __init__(self, N):
        """:type N: int"""
        <|body_0|>

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

    def leave(self, p):
        """:type p: int :rtype: void"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        global n
        n = N
        self.q = Queue.PriorityQueue()
        self.q.put(Ran(0, n - 1))
<|end_body_0|>

<|body_start_1|>
        ran = self.q.get()
        l, r = (ran.l, ran.r)
        if l == 0:
            if l + 1 <= r:
                self.q.put(Ran(l + 1, r))
            return l
        elif r == n - 1:
            if r - 1 >= l:
                self.q.put(Ran(l, r - 1))
            return r
        ll = r - l + 1
        mid = 0
        if ll % 2 == 0:
            mid = (ll - 1) / 2 + l
        else:
            mid = ll / 2 + l
        if mid - 1 >= l:
            self.q.put(Ran(l, mid - 1))
        if mid + 1 <= r:
            self.q.put(Ran(mid + 1, r))
        return mid
<|end_body_1|>

<|body_start_2|>
        newq = Queue.PriorityQueue()
        L = None
        R = None
        while not self.q.empty():
            ran = self.q.get()
            l, r = (ran.l, ran.r)
            if r + 1 == p:
                L = l
            elif l - 1 == p:
                R = r
            else:
                newq.put(ran)
        if L != None and R != None:
            newq.put(Ran(L, R))
        elif L != None:
            newq.put(Ran(L, p))
        elif R != None:
            newq.put(Ran(p, R))
        else:
            newq.put(Ran(p, p))
        self.q = newq
<|end_body_2|>

Solution:
class ExamRoom:
    def __init__(self, N):
        """:type N: int"""
        global n
        n = N
        self.q = Queue.PriorityQueue()
        self.q.put(Ran(0, n - 1))

    def seat(self):
        """:rtype: int"""
        ran = self.q.get()
        l, r = (ran.l, ran.r)
        if l == 0:
            if l + 1 <= r:
                self.q.put(Ran(l + 1, r))
            return l
        elif r == n - 1:
            if r - 1 >= l:
                self.q.put(Ran(l, r - 1))
            return r
        ll = r - l + 1
        mid = 0
        if ll % 2 == 0:
            mid = (ll - 1) / 2 + l
        else:
            mid = ll / 2 + l
        if mid - 1 >= l:
            self.q.put(Ran(l, mid - 1))
        if mid + 1 <= r:
            self.q.put(Ran(mid + 1, r))
        return mid

    def leave(self, p):
        """:type p: int :rtype: void"""
        newq = Queue.PriorityQueue()
        L = None
        R = None
        while not self.q.empty():
            ran = self.q.get()
            l, r = (ran.l, ran.r)
            if r + 1 == p:
                L = l
            elif l - 1 == p:
                R = r
            else:
                newq.put(ran)
        if L != None and R != None:
            newq.put(Ran(L, R))
        elif L != None:
            newq.put(Ran(L, p))
        elif R != None:
            newq.put(Ran(p, R))
        else:
            newq.put(Ran(p, p))
        self.q = newq


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

Class description:
A factory for creating data sources

Method signatures and docstrings:
- def _merge_path(self, job_id, data_source_dict): Merges the path, job id, and subpath. Returns it as a string
- def create_data_source(self, job_id, data_source_dict): Creates one data source given a job id, and a data_source_dict (from DataSources.json)
- def create_data_source_list(self, job_id_list, data_source_dict): Creates a list of data sources given a list of ids and a data_source_dict

Implement the Python class `DataSourceFactory` described below.

Class description:
A factory for creating data sources

Method signatures and docstrings:
- def _merge_path(self, job_id, data_source_dict): Merges the path, job id, and subpath. Returns it as a string
- def create_data_source(self, job_id, data_source_dict): Creates one data source given a job id, and a data_source_dict (from DataSources.json)
- def create_data_source_list(self, job_id_list, data_source_dict): Creates a list of data sources given a list of ids and a data_source_dict

<|skeleton|>
class DataSourceFactory:
    """A factory for creating data sources"""

    def _merge_path(self, job_id, data_source_dict):
        """Merges the path, job id, and subpath. Returns it as a string"""
        <|body_0|>

    def create_data_source(self, job_id, data_source_dict):
        """Creates one data source given a job id, and a data_source_dict (from DataSources.json)"""
        <|body_1|>

    def create_data_source_list(self, job_id_list, data_source_dict):
        """Creates a list of data sources given a list of ids and a data_source_dict"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if 'Sub Path' in data_source_dict:
            merged_path = data_source_dict['Path'] + job_id + data_source_dict['Subpath']
            del data_source_dict['Subpath']
        else:
            merged_path = data_source_dict['Path']
        return merged_path
<|end_body_0|>

<|body_start_1|>
        data_source_args = {}
        service = service_factory.create_service(data_source_dict)
        path = self._merge_path(job_id, data_source_dict)
        data_source_args['Path'] = path
        data_source_args['Source Name'] = data_source_dict['Source Name']
        data_source_args['Status'] = data_source_dict['Status']
        data_source = ds.DataSource(job_id, service, data_source_args)
        return data_source
<|end_body_1|>

<|body_start_2|>
        data_sources = list()
        for job_id in job_id_list:
            data_source = self.create_data_source(job_id, data_source_dict)
            data_sources.append(data_source)
        return data_sources
<|end_body_2|>

Solution:
class DataSourceFactory:
    """A factory for creating data sources"""

    def _merge_path(self, job_id, data_source_dict):
        """Merges the path, job id, and subpath. Returns it as a string"""
        if 'Sub Path' in data_source_dict:
            merged_path = data_source_dict['Path'] + job_id + data_source_dict['Subpath']
            del data_source_dict['Subpath']
        else:
            merged_path = data_source_dict['Path']
        return merged_path

    def create_data_source(self, job_id, data_source_dict):
        """Creates one data source given a job id, and a data_source_dict (from DataSources.json)"""
        data_source_args = {}
        service = service_factory.create_service(data_source_dict)
        path = self._merge_path(job_id, data_source_dict)
        data_source_args['Path'] = path
        data_source_args['Source Name'] = data_source_dict['Source Name']
        data_source_args['Status'] = data_source_dict['Status']
        data_source = ds.DataSource(job_id, service, data_source_args)
        return data_source

    def create_data_source_list(self, job_id_list, data_source_dict):
        """Creates a list of data sources given a list of ids and a data_source_dict"""
        data_sources = list()
        for job_id in job_id_list:
            data_source = self.create_data_source(job_id, data_source_dict)
            data_sources.append(data_source)
        return data_sources


migrate

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

Class description:
Implement the BackupMigrationExecutor class.

Method signatures and docstrings:
- def migrate(self, targets, plan=None, fake=False, fake_initial=False): Migrates the database up to the given targets. Django first needs to create all project states before a migration is (un)applied and in a second step run all the database operations.
- def _migrate_all_forwards(self, plan, full_plan, fake, fake_initial): Take a list of 2-tuples of the form (migration instance, False) and apply them in the order they occur in the full_plan.

Implement the Python class `BackupMigrationExecutor` described below.

Class description:
Implement the BackupMigrationExecutor class.

Method signatures and docstrings:
- def migrate(self, targets, plan=None, fake=False, fake_initial=False): Migrates the database up to the given targets. Django first needs to create all project states before a migration is (un)applied and in a second step run all the database operations.
- def _migrate_all_forwards(self, plan, full_plan, fake, fake_initial): Take a list of 2-tuples of the form (migration instance, False) and apply them in the order they occur in the full_plan.

<|skeleton|>
class BackupMigrationExecutor:

    def migrate(self, targets, plan=None, fake=False, fake_initial=False):
        """Migrates the database up to the given targets. Django first needs to create all project states before a migration is (un)applied and in a second step run all the database operations."""
        <|body_0|>

    def _migrate_all_forwards(self, plan, full_plan, fake, fake_initial):
        """Take a list of 2-tuples of the form (migration instance, False) and apply them in the order they occur in the full_plan."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if plan is None:
            plan = self.migration_plan(targets)
        full_plan = self.migration_plan(self.loader.graph.leaf_nodes(), clean_start=True)
        state = self._migrate_all_forwards(plan, full_plan, fake=fake, fake_initial=fake_initial)
        self.check_replacements()
        return state
<|end_body_0|>

<|body_start_1|>
        migrations_to_run = {m[0] for m in plan}
        state = ProjectState(real_apps=list(self.loader.unmigrated_apps))
        for migration, _ in full_plan:
            if not migrations_to_run:
                break
            if migration in migrations_to_run:
                if 'apps' not in state.__dict__:
                    if self.progress_callback:
                        self.progress_callback('render_start')
                    state.apps
                    if self.progress_callback:
                        self.progress_callback('render_success')
                state = self.apply_migration(state, migration, fake=fake, fake_initial=fake_initial)
                migrations_to_run.remove(migration)
        return state
<|end_body_1|>

Solution:
class BackupMigrationExecutor:
    def migrate(self, targets, plan=None, fake=False, fake_initial=False):
        """Migrates the database up to the given targets. Django first needs to create all project states before a migration is (un)applied and in a second step run all the database operations."""
        if plan is None:
            plan = self.migration_plan(targets)
        full_plan = self.migration_plan(self.loader.graph.leaf_nodes(), clean_start=True)
        state = self._migrate_all_forwards(plan, full_plan, fake=fake, fake_initial=fake_initial)
        self.check_replacements()
        return state

    def _migrate_all_forwards(self, plan, full_plan, fake, fake_initial):
        """Take a list of 2-tuples of the form (migration instance, False) and apply them in the order they occur in the full_plan."""
        migrations_to_run = {m[0] for m in plan}
        state = ProjectState(real_apps=list(self.loader.unmigrated_apps))
        for migration, _ in full_plan:
            if not migrations_to_run:
                break
            if migration in migrations_to_run:
                if 'apps' not in state.__dict__:
                    if self.progress_callback:
                        self.progress_callback('render_start')
                    state.apps
                    if self.progress_callback:
                        self.progress_callback('render_success')
                state = self.apply_migration(state, migration, fake=fake, fake_initial=fake_initial)
                migrations_to_run.remove(migration)
        return state