serialize

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

Class description:
Implement the UnifiedRoleManagementPolicy class.

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

Implement the Python class `UnifiedRoleManagementPolicy` described below.

Class description:
Implement the UnifiedRoleManagementPolicy class.

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

<|skeleton|>
class UnifiedRoleManagementPolicy:

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

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

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

<|end_skeleton|>

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

<|body_start_1|>
        from .entity import Entity
        from .identity import Identity
        from .unified_role_management_policy_rule import UnifiedRoleManagementPolicyRule
        from .entity import Entity
        from .identity import Identity
        from .unified_role_management_policy_rule import UnifiedRoleManagementPolicyRule
        fields: Dict[str, Callable[[Any], None]] = {'description': lambda n: setattr(self, 'description', n.get_str_value()), 'displayName': lambda n: setattr(self, 'display_name', n.get_str_value()), 'effectiveRules': lambda n: setattr(self, 'effective_rules', n.get_collection_of_object_values(UnifiedRoleManagementPolicyRule)), 'isOrganizationDefault': lambda n: setattr(self, 'is_organization_default', n.get_bool_value()), 'lastModifiedBy': lambda n: setattr(self, 'last_modified_by', n.get_object_value(Identity)), 'lastModifiedDateTime': lambda n: setattr(self, 'last_modified_date_time', n.get_datetime_value()), 'rules': lambda n: setattr(self, 'rules', n.get_collection_of_object_values(UnifiedRoleManagementPolicyRule)), 'scopeId': lambda n: setattr(self, 'scope_id', n.get_str_value()), 'scopeType': lambda n: setattr(self, 'scope_type', n.get_str_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields
<|end_body_1|>

<|body_start_2|>
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_str_value('description', self.description)
        writer.write_str_value('displayName', self.display_name)
        writer.write_collection_of_object_values('effectiveRules', self.effective_rules)
        writer.write_bool_value('isOrganizationDefault', self.is_organization_default)
        writer.write_object_value('lastModifiedBy', self.last_modified_by)
        writer.write_datetime_value('lastModifiedDateTime', self.last_modified_date_time)
        writer.write_collection_of_object_values('rules', self.rules)
        writer.write_str_value('scopeId', self.scope_id)
        writer.write_str_value('scopeType', self.scope_type)
<|end_body_2|>

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

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        from .entity import Entity
        from .identity import Identity
        from .unified_role_management_policy_rule import UnifiedRoleManagementPolicyRule
        from .entity import Entity
        from .identity import Identity
        from .unified_role_management_policy_rule import UnifiedRoleManagementPolicyRule
        fields: Dict[str, Callable[[Any], None]] = {'description': lambda n: setattr(self, 'description', n.get_str_value()), 'displayName': lambda n: setattr(self, 'display_name', n.get_str_value()), 'effectiveRules': lambda n: setattr(self, 'effective_rules', n.get_collection_of_object_values(UnifiedRoleManagementPolicyRule)), 'isOrganizationDefault': lambda n: setattr(self, 'is_organization_default', n.get_bool_value()), 'lastModifiedBy': lambda n: setattr(self, 'last_modified_by', n.get_object_value(Identity)), 'lastModifiedDateTime': lambda n: setattr(self, 'last_modified_date_time', n.get_datetime_value()), 'rules': lambda n: setattr(self, 'rules', n.get_collection_of_object_values(UnifiedRoleManagementPolicyRule)), 'scopeId': lambda n: setattr(self, 'scope_id', n.get_str_value()), 'scopeType': lambda n: setattr(self, 'scope_type', n.get_str_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_str_value('description', self.description)
        writer.write_str_value('displayName', self.display_name)
        writer.write_collection_of_object_values('effectiveRules', self.effective_rules)
        writer.write_bool_value('isOrganizationDefault', self.is_organization_default)
        writer.write_object_value('lastModifiedBy', self.last_modified_by)
        writer.write_datetime_value('lastModifiedDateTime', self.last_modified_date_time)
        writer.write_collection_of_object_values('rules', self.rules)
        writer.write_str_value('scopeId', self.scope_id)
        writer.write_str_value('scopeType', self.scope_type)


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

Class description:
Implementation of the 'DeviceTree_PartitionSlice' model. TODO: type description here. Attributes: disk_file_name (string): The disk to use. length (long|int): The length of data for the LVM volume (for which this device tree is being built) in bytes. It does not include size of the LVM meta data. lvm_data_offset (long|int): Each LVM partition starts with LVM meta data. After the meta data there can be data for one or more LVM volumes. This field indicates the offset in bytes (relative to partition) where data for various LVM volumes starts on the partition. NOTE: If this device tree represents first LVM volume on the partition, 'lvm_data_offset' is equal to 'offset'. offset (long|int): This

Method signatures and docstrings:
- def __init__(self, disk_file_name=None, length=None, lvm_data_offset=None, offset=None, partition_number=None): Constructor for the DeviceTree_PartitionSlice class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `DeviceTree_PartitionSlice` described below.

Class description:
Implementation of the 'DeviceTree_PartitionSlice' model. TODO: type description here. Attributes: disk_file_name (string): The disk to use. length (long|int): The length of data for the LVM volume (for which this device tree is being built) in bytes. It does not include size of the LVM meta data. lvm_data_offset (long|int): Each LVM partition starts with LVM meta data. After the meta data there can be data for one or more LVM volumes. This field indicates the offset in bytes (relative to partition) where data for various LVM volumes starts on the partition. NOTE: If this device tree represents first LVM volume on the partition, 'lvm_data_offset' is equal to 'offset'. offset (long|int): This

Method signatures and docstrings:
- def __init__(self, disk_file_name=None, length=None, lvm_data_offset=None, offset=None, partition_number=None): Constructor for the DeviceTree_PartitionSlice class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class DeviceTree_PartitionSlice:
    """Implementation of the 'DeviceTree_PartitionSlice' model. TODO: type description here. Attributes: disk_file_name (string): The disk to use. length (long|int): The length of data for the LVM volume (for which this device tree is being built) in bytes. It does not include size of the LVM meta data. lvm_data_offset (long|int): Each LVM partition starts with LVM meta data. After the meta data there can be data for one or more LVM volumes. This field indicates the offset in bytes (relative to partition) where data for various LVM volumes starts on the partition. NOTE: If this device tree represents first LVM volume on the partition, 'lvm_data_offset' is equal to 'offset'. offset (long|int): This"""

    def __init__(self, disk_file_name=None, length=None, lvm_data_offset=None, offset=None, partition_number=None):
        """Constructor for the DeviceTree_PartitionSlice class"""
        <|body_0|>

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

<|end_skeleton|>

<|body_start_0|>
        self.disk_file_name = disk_file_name
        self.length = length
        self.lvm_data_offset = lvm_data_offset
        self.offset = offset
        self.partition_number = partition_number
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        disk_file_name = dictionary.get('diskFileName')
        length = dictionary.get('length')
        lvm_data_offset = dictionary.get('lvmDataOffset')
        offset = dictionary.get('offset')
        partition_number = dictionary.get('partitionNumber')
        return cls(disk_file_name, length, lvm_data_offset, offset, partition_number)
<|end_body_1|>

Solution:
class DeviceTree_PartitionSlice:
    """Implementation of the 'DeviceTree_PartitionSlice' model. TODO: type description here. Attributes: disk_file_name (string): The disk to use. length (long|int): The length of data for the LVM volume (for which this device tree is being built) in bytes. It does not include size of the LVM meta data. lvm_data_offset (long|int): Each LVM partition starts with LVM meta data. After the meta data there can be data for one or more LVM volumes. This field indicates the offset in bytes (relative to partition) where data for various LVM volumes starts on the partition. NOTE: If this device tree represents first LVM volume on the partition, 'lvm_data_offset' is equal to 'offset'. offset (long|int): This"""

    def __init__(self, disk_file_name=None, length=None, lvm_data_offset=None, offset=None, partition_number=None):
        """Constructor for the DeviceTree_PartitionSlice class"""
        self.disk_file_name = disk_file_name
        self.length = length
        self.lvm_data_offset = lvm_data_offset
        self.offset = offset
        self.partition_number = partition_number

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        disk_file_name = dictionary.get('diskFileName')
        length = dictionary.get('length')
        lvm_data_offset = dictionary.get('lvmDataOffset')
        offset = dictionary.get('offset')
        partition_number = dictionary.get('partitionNumber')
        return cls(disk_file_name, length, lvm_data_offset, offset, partition_number)


backward

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

Class description:
Implement the ThreeInterpolate class.

Method signatures and docstrings:
- def forward(ctx, features, idx, weight): Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor (B, c, m) Features descriptors to be interpolated from idx : torch.Tensor (B, n, 3) three nearest neighbors of the target features in features weight : torch.Tensor (B, n, 3) weights Returns ------- torch.Tensor (B, c, n) tensor of the interpolated features
- def backward(ctx, grad_out): Parameters ---------- grad_out : torch.Tensor (B, c, n) tensor with gradients of ouputs Returns ------- grad_features : torch.Tensor (B, c, m) tensor with gradients of features None None

Implement the Python class `ThreeInterpolate` described below.

Class description:
Implement the ThreeInterpolate class.

Method signatures and docstrings:
- def forward(ctx, features, idx, weight): Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor (B, c, m) Features descriptors to be interpolated from idx : torch.Tensor (B, n, 3) three nearest neighbors of the target features in features weight : torch.Tensor (B, n, 3) weights Returns ------- torch.Tensor (B, c, n) tensor of the interpolated features
- def backward(ctx, grad_out): Parameters ---------- grad_out : torch.Tensor (B, c, n) tensor with gradients of ouputs Returns ------- grad_features : torch.Tensor (B, c, m) tensor with gradients of features None None

<|skeleton|>
class ThreeInterpolate:

    def forward(ctx, features, idx, weight):
        """Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor (B, c, m) Features descriptors to be interpolated from idx : torch.Tensor (B, n, 3) three nearest neighbors of the target features in features weight : torch.Tensor (B, n, 3) weights Returns ------- torch.Tensor (B, c, n) tensor of the interpolated features"""
        <|body_0|>

    def backward(ctx, grad_out):
        """Parameters ---------- grad_out : torch.Tensor (B, c, n) tensor with gradients of ouputs Returns ------- grad_features : torch.Tensor (B, c, m) tensor with gradients of features None None"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        B, c, m = features.size()
        n = idx.size(1)
        ctx.three_interpolate_for_backward = (idx, weight, m)
        return _ext.three_interpolate(features, idx, weight)
<|end_body_0|>

<|body_start_1|>
        idx, weight, m = ctx.three_interpolate_for_backward
        grad_features = _ext.three_interpolate_grad(grad_out.contiguous(), idx, weight, m)
        return (grad_features, None, None)
<|end_body_1|>

Solution:
class ThreeInterpolate:
    def forward(ctx, features, idx, weight):
        """Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor (B, c, m) Features descriptors to be interpolated from idx : torch.Tensor (B, n, 3) three nearest neighbors of the target features in features weight : torch.Tensor (B, n, 3) weights Returns ------- torch.Tensor (B, c, n) tensor of the interpolated features"""
        B, c, m = features.size()
        n = idx.size(1)
        ctx.three_interpolate_for_backward = (idx, weight, m)
        return _ext.three_interpolate(features, idx, weight)

    def backward(ctx, grad_out):
        """Parameters ---------- grad_out : torch.Tensor (B, c, n) tensor with gradients of ouputs Returns ------- grad_features : torch.Tensor (B, c, m) tensor with gradients of features None None"""
        idx, weight, m = ctx.three_interpolate_for_backward
        grad_features = _ext.three_interpolate_grad(grad_out.contiguous(), idx, weight, m)
        return (grad_features, None, None)


invertTree

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def invertTree(self, root): :type root: TreeNode :rtype: TreeNode Recursive solution
- def invertTree1(self, root): :type root: TreeNode :rtype: TreeNode

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def invertTree(self, root): :type root: TreeNode :rtype: TreeNode Recursive solution
- def invertTree1(self, root): :type root: TreeNode :rtype: TreeNode

<|skeleton|>
class Solution:

    def invertTree(self, root):
        """:type root: TreeNode :rtype: TreeNode Recursive solution"""
        <|body_0|>

    def invertTree1(self, root):
        """:type root: TreeNode :rtype: TreeNode"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not root:
            return None
        left = self.invertTree(root.left)
        right = self.invertTree(root.right)
        root.left = right
        root.right = left
        return root
<|end_body_0|>

<|body_start_1|>
        if not root:
            return None
        q = deque([root])
        while len(q) > 0:
            node = q.popleft()
            temp = node.left
            node.left = node.right
            node.right = temp
            if node.left != None:
                q.append(node.left)
            if node.right != None:
                q.append(node.right)
        return root
<|end_body_1|>

Solution:
class Solution:
    def invertTree(self, root):
        """:type root: TreeNode :rtype: TreeNode Recursive solution"""
        if not root:
            return None
        left = self.invertTree(root.left)
        right = self.invertTree(root.right)
        root.left = right
        root.right = left
        return root

    def invertTree1(self, root):
        """:type root: TreeNode :rtype: TreeNode"""
        if not root:
            return None
        q = deque([root])
        while len(q) > 0:
            node = q.popleft()
            temp = node.left
            node.left = node.right
            node.right = temp
            if node.left != None:
                q.append(node.left)
            if node.right != None:
                q.append(node.right)
        return root


sample

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

Class description:
A minimal replay buffer used by Decision Transformer (DT) to process episodes into max_seq_len length segments and do shuffling. Stores MultiAgentSample.

Method signatures and docstrings:
- def __init__(self, capacity: int=20, max_seq_len: int=20, max_ep_len: int=1000): Args: capacity: Maximum number of episodes the buffer can store. max_seq_len: Length of segments that are sampled. max_ep_len: Maximum length of episodes added.
- def add(self, batch: SampleBatchType): Add a MultiAgentBatch of episodes. Replace if full. Args: batch: MultiAgentBatch of full episodes.
- def sample(self, batch_size: int) -> MultiAgentBatch: Sample segments from the buffer. Args: batch_size: number of segments to sample. Returns: MultiAgentBatch of segments with keys and shape { OBS: [batch_size, max_seq_len, obs_dim], ACTIONS: [batch_size, max_seq_len, act_dim], RETURNS_TO_GO: [batch_size, max_seq_len + 1, 1], T: [batch_size, max_seq_len], ATTENTION_MASKS: [batch_size, max_seq_len], }

Implement the Python class `MultiAgentSegmentationBuffer` described below.

Class description:
A minimal replay buffer used by Decision Transformer (DT) to process episodes into max_seq_len length segments and do shuffling. Stores MultiAgentSample.

Method signatures and docstrings:
- def __init__(self, capacity: int=20, max_seq_len: int=20, max_ep_len: int=1000): Args: capacity: Maximum number of episodes the buffer can store. max_seq_len: Length of segments that are sampled. max_ep_len: Maximum length of episodes added.
- def add(self, batch: SampleBatchType): Add a MultiAgentBatch of episodes. Replace if full. Args: batch: MultiAgentBatch of full episodes.
- def sample(self, batch_size: int) -> MultiAgentBatch: Sample segments from the buffer. Args: batch_size: number of segments to sample. Returns: MultiAgentBatch of segments with keys and shape { OBS: [batch_size, max_seq_len, obs_dim], ACTIONS: [batch_size, max_seq_len, act_dim], RETURNS_TO_GO: [batch_size, max_seq_len + 1, 1], T: [batch_size, max_seq_len], ATTENTION_MASKS: [batch_size, max_seq_len], }

<|skeleton|>
class MultiAgentSegmentationBuffer:
    """A minimal replay buffer used by Decision Transformer (DT) to process episodes into max_seq_len length segments and do shuffling. Stores MultiAgentSample."""

    def __init__(self, capacity: int=20, max_seq_len: int=20, max_ep_len: int=1000):
        """Args: capacity: Maximum number of episodes the buffer can store. max_seq_len: Length of segments that are sampled. max_ep_len: Maximum length of episodes added."""
        <|body_0|>

    def add(self, batch: SampleBatchType):
        """Add a MultiAgentBatch of episodes. Replace if full. Args: batch: MultiAgentBatch of full episodes."""
        <|body_1|>

    def sample(self, batch_size: int) -> MultiAgentBatch:
        """Sample segments from the buffer. Args: batch_size: number of segments to sample. Returns: MultiAgentBatch of segments with keys and shape { OBS: [batch_size, max_seq_len, obs_dim], ACTIONS: [batch_size, max_seq_len, act_dim], RETURNS_TO_GO: [batch_size, max_seq_len + 1, 1], T: [batch_size, max_seq_len], ATTENTION_MASKS: [batch_size, max_seq_len], }"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        def new_buffer():
            return SegmentationBuffer(capacity, max_seq_len, max_ep_len)
        self.buffers = defaultdict(new_buffer)
<|end_body_0|>

<|body_start_1|>
        batch = batch.copy()
        batch = batch.as_multi_agent()
        for policy_id, sample_batch in batch.policy_batches.items():
            self.buffers[policy_id].add(sample_batch)
<|end_body_1|>

<|body_start_2|>
        samples = {}
        for policy_id, buffer in self.buffers.items():
            samples[policy_id] = buffer.sample(batch_size)
        return MultiAgentBatch(samples, batch_size)
<|end_body_2|>

Solution:
class MultiAgentSegmentationBuffer:
    """A minimal replay buffer used by Decision Transformer (DT) to process episodes into max_seq_len length segments and do shuffling. Stores MultiAgentSample."""

    def __init__(self, capacity: int=20, max_seq_len: int=20, max_ep_len: int=1000):
        """Args: capacity: Maximum number of episodes the buffer can store. max_seq_len: Length of segments that are sampled. max_ep_len: Maximum length of episodes added."""
        def new_buffer():
            return SegmentationBuffer(capacity, max_seq_len, max_ep_len)
        self.buffers = defaultdict(new_buffer)

    def add(self, batch: SampleBatchType):
        """Add a MultiAgentBatch of episodes. Replace if full. Args: batch: MultiAgentBatch of full episodes."""
        batch = batch.copy()
        batch = batch.as_multi_agent()
        for policy_id, sample_batch in batch.policy_batches.items():
            self.buffers[policy_id].add(sample_batch)

    def sample(self, batch_size: int) -> MultiAgentBatch:
        """Sample segments from the buffer. Args: batch_size: number of segments to sample. Returns: MultiAgentBatch of segments with keys and shape { OBS: [batch_size, max_seq_len, obs_dim], ACTIONS: [batch_size, max_seq_len, act_dim], RETURNS_TO_GO: [batch_size, max_seq_len + 1, 1], T: [batch_size, max_seq_len], ATTENTION_MASKS: [batch_size, max_seq_len], }"""
        samples = {}
        for policy_id, buffer in self.buffers.items():
            samples[policy_id] = buffer.sample(batch_size)
        return MultiAgentBatch(samples, batch_size)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def reverseList1(self, head): :type head: ListNode :rtype: ListNode
- def reverseList(self, head): :type head: ListNode :rtype: ListNode

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def reverseList1(self, head): :type head: ListNode :rtype: ListNode
- def reverseList(self, head): :type head: ListNode :rtype: ListNode

<|skeleton|>
class Solution:

    def reverseList1(self, head):
        """:type head: ListNode :rtype: ListNode"""
        <|body_0|>

    def reverseList(self, head):
        """:type head: ListNode :rtype: ListNode"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not head:
            return head
        cur = head
        pre = None
        r = cur
        while cur:
            t = cur.next
            cur.next = pre
            r = cur
            pre = cur
            cur = t
        return r
<|end_body_0|>

<|body_start_1|>
        if not head or not head.next:
            return head
        node = self.reverseList(head.next)
        head.next.next = head
        head.next = None
        return node
<|end_body_1|>

Solution:
class Solution:
    def reverseList1(self, head):
        """:type head: ListNode :rtype: ListNode"""
        if not head:
            return head
        cur = head
        pre = None
        r = cur
        while cur:
            t = cur.next
            cur.next = pre
            r = cur
            pre = cur
            cur = t
        return r

    def reverseList(self, head):
        """:type head: ListNode :rtype: ListNode"""
        if not head or not head.next:
            return head
        node = self.reverseList(head.next)
        head.next.next = head
        head.next = None
        return node


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

Class description:
Represents the configuration for a knot package

Method signatures and docstrings:
- def __init__(self, widgetConfigs, modConfigs, positioningConfigs, sizingConfigs, serviceConfigs): Initialize the widget config with its widgets, positioning policies and sizing policies
- def setPackageFilename(self, filename): Set the package filename

Implement the Python class `PackageConfig` described below.

Class description:
Represents the configuration for a knot package

Method signatures and docstrings:
- def __init__(self, widgetConfigs, modConfigs, positioningConfigs, sizingConfigs, serviceConfigs): Initialize the widget config with its widgets, positioning policies and sizing policies
- def setPackageFilename(self, filename): Set the package filename

<|skeleton|>
class PackageConfig:
    """Represents the configuration for a knot package"""

    def __init__(self, widgetConfigs, modConfigs, positioningConfigs, sizingConfigs, serviceConfigs):
        """Initialize the widget config with its widgets, positioning policies and sizing policies"""
        <|body_0|>

    def setPackageFilename(self, filename):
        """Set the package filename"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.widgetConfigs = ConvertConfigsToDictionary(widgetConfigs)
        self.modConfigs = ConvertConfigsToDictionary(modConfigs)
        self.positioningConfigs = ConvertConfigsToDictionary(positioningConfigs)
        self.sizingConfigs = ConvertConfigsToDictionary(sizingConfigs)
        self.serviceConfigs = ConvertConfigsToDictionary(serviceConfigs)
<|end_body_0|>

<|body_start_1|>
        self.filename = filename
        for name in self.widgetConfigs:
            self.widgetConfigs[name].setPackageFilename(filename)
<|end_body_1|>

Solution:
class PackageConfig:
    """Represents the configuration for a knot package"""

    def __init__(self, widgetConfigs, modConfigs, positioningConfigs, sizingConfigs, serviceConfigs):
        """Initialize the widget config with its widgets, positioning policies and sizing policies"""
        self.widgetConfigs = ConvertConfigsToDictionary(widgetConfigs)
        self.modConfigs = ConvertConfigsToDictionary(modConfigs)
        self.positioningConfigs = ConvertConfigsToDictionary(positioningConfigs)
        self.sizingConfigs = ConvertConfigsToDictionary(sizingConfigs)
        self.serviceConfigs = ConvertConfigsToDictionary(serviceConfigs)

    def setPackageFilename(self, filename):
        """Set the package filename"""
        self.filename = filename
        for name in self.widgetConfigs:
            self.widgetConfigs[name].setPackageFilename(filename)


evalRPNArray

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def evalRPN(self, tokens: List[str]) -> int: 栈
- def evalRPNArray(self, tokens: List[str]) -> int: 数组模拟栈

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def evalRPN(self, tokens: List[str]) -> int: 栈
- def evalRPNArray(self, tokens: List[str]) -> int: 数组模拟栈

<|skeleton|>
class Solution:

    def evalRPN(self, tokens: List[str]) -> int:
        """栈"""
        <|body_0|>

    def evalRPNArray(self, tokens: List[str]) -> int:
        """数组模拟栈"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        stack = []
        for token in tokens:
            if token not in ('+', '-', '*', '/'):
                stack.append(token)
            else:
                x, y = (int(stack.pop()), int(stack.pop()))
                if token == '+':
                    stack.append(x + y)
                elif token == '-':
                    stack.append(y - x)
                elif token == '*':
                    stack.append(x * y)
                else:
                    stack.append(y / x)
        return int(stack[0])
<|end_body_0|>

<|body_start_1|>
        op_to_binary_fn = {'+': add, '-': sub, '*': mul, '/': lambda x, y: int(x / y)}
        n = len(tokens)
        stack = [0] * ((n + 1) // 2)
        index = -1
        for token in tokens:
            try:
                num = int(token)
                index += 1
                stack[index] = num
            except ValueError:
                index -= 1
                stack[index] = op_to_binary_fn[token](stack[index], stack[index + 1])
        return stack[0]
<|end_body_1|>

Solution:
class Solution:
    def evalRPN(self, tokens: List[str]) -> int:
        """栈"""
        stack = []
        for token in tokens:
            if token not in ('+', '-', '*', '/'):
                stack.append(token)
            else:
                x, y = (int(stack.pop()), int(stack.pop()))
                if token == '+':
                    stack.append(x + y)
                elif token == '-':
                    stack.append(y - x)
                elif token == '*':
                    stack.append(x * y)
                else:
                    stack.append(y / x)
        return int(stack[0])

    def evalRPNArray(self, tokens: List[str]) -> int:
        """数组模拟栈"""
        op_to_binary_fn = {'+': add, '-': sub, '*': mul, '/': lambda x, y: int(x / y)}
        n = len(tokens)
        stack = [0] * ((n + 1) // 2)
        index = -1
        for token in tokens:
            try:
                num = int(token)
                index += 1
                stack[index] = num
            except ValueError:
                index -= 1
                stack[index] = op_to_binary_fn[token](stack[index], stack[index + 1])
        return stack[0]


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

Class description:
A file-based VPD partition.

Method signatures and docstrings:
- def Delete(self, *keys): See Partition.Delete.
- def Update(self, items, log=True): See Partition.Update.

Implement the Python class `MutableFileBasedPartition` described below.

Class description:
A file-based VPD partition.

Method signatures and docstrings:
- def Delete(self, *keys): See Partition.Delete.
- def Update(self, items, log=True): See Partition.Update.

<|skeleton|>
class MutableFileBasedPartition:
    """A file-based VPD partition."""

    def Delete(self, *keys):
        """See Partition.Delete."""
        <|body_0|>

    def Update(self, items, log=True):
        """See Partition.Update."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        for key in keys:
            file_path = self._device.path.join(self._path, key)
            if self._device.path.exists(file_path):
                self._device.CheckCall(['rm', '-f', file_path])
                return
<|end_body_0|>

<|body_start_1|>
        for k, v in items.items():
            file_name = self._device.path.join(self._path, k)
            if v is not None:
                dir_name = self._device.path.dirname(file_name)
                self._device.CheckCall(['mkdir', '-p', dir_name])
                self._device.WriteFile(file_name, v)
            else:
                self._device.CheckCall(['rm', '-f', file_name])
        self._device.CheckCall(['sync'])
<|end_body_1|>

Solution:
class MutableFileBasedPartition:
    """A file-based VPD partition."""

    def Delete(self, *keys):
        """See Partition.Delete."""
        for key in keys:
            file_path = self._device.path.join(self._path, key)
            if self._device.path.exists(file_path):
                self._device.CheckCall(['rm', '-f', file_path])
                return

    def Update(self, items, log=True):
        """See Partition.Update."""
        for k, v in items.items():
            file_name = self._device.path.join(self._path, k)
            if v is not None:
                dir_name = self._device.path.dirname(file_name)
                self._device.CheckCall(['mkdir', '-p', dir_name])
                self._device.WriteFile(file_name, v)
            else:
                self._device.CheckCall(['rm', '-f', file_name])
        self._device.CheckCall(['sync'])


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

Class description:
General code for a character basis (irreducible and induced trivial). This is a basis of the symmetric functions that has the property that ``self(la).character_to_frobenius_image(n)`` is equal to ``other([n-sum(la)]+la)``. It should also have the property that the (outer) structure constants are the analogue of the stable Kronecker coefficients on the ``other`` basis (where ``other`` is either the Schur or homogeneous bases). These bases are introduced in [OZ2015]_. EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: s = Sym.s() sage: h = Sym.h() sage: ht = SymmetricFunctions(QQ).ht() sage: st = SymmetricFunctions(QQ).st() sage: ht(s[2,1]) ht[1, 1] + ht[2, 1] - ht[3] sage: s(ht[2,1]) s[1] -

Method signatures and docstrings:
- def __init__(self, Sym, other_basis, bname, pfix): Initialize the basis and register coercions. The coercions are set up between the ``other_basis``. INPUT: - ``Sym`` -- an instance of the symmetric function algebra - ``other_basis`` -- a basis of Sym - ``bname`` -- the name for this basis (convention: ends in "character") - ``pfix`` -- a prefix to use for the basis EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: ht = SymmetricFunctions(QQ).ht(); ht Symmetric Functions over Rational Field in the induced trivial character basis sage: st = SymmetricFunctions(QQ).st(); st Symmetric Functions over Rational Field in the irreducible symmetric group character basis sage: TestSuite(ht).run()
- def _self_to_other_on_basis(self, lam): Convert a character-basis element to the ``self._other`` basis. This is a recursive procedure that is calculated by the assumption that the leading term of ``self(lam)`` is ``other(lam)`` and ``evalsf(self(lam),n) == other([n-sum(lam)]+lam)``. INPUT: - ``lam`` -- a partition OUTPUT: - an expression in the ``self._other`` basis EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: ht = SymmetricFunctions(QQ).ht() sage: ht._self_to_other_on_basis(Partition([2,1])) h[1] - 2*h[1, 1] + h[2, 1] sage: st = SymmetricFunctions(QQ).st() sage: st._self_to_other_on_basis(Partition([2,1])) 3*s[1] - 2*s[1, 1] - 2*s[2] + s[2, 1] TESTS:: sage: h = SymmetricFunctions(QQ).h() sage: ht = SymmetricFunctions(QQ).h

Implement the Python class `character_basis` described below.

Class description:
General code for a character basis (irreducible and induced trivial). This is a basis of the symmetric functions that has the property that ``self(la).character_to_frobenius_image(n)`` is equal to ``other([n-sum(la)]+la)``. It should also have the property that the (outer) structure constants are the analogue of the stable Kronecker coefficients on the ``other`` basis (where ``other`` is either the Schur or homogeneous bases). These bases are introduced in [OZ2015]_. EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: s = Sym.s() sage: h = Sym.h() sage: ht = SymmetricFunctions(QQ).ht() sage: st = SymmetricFunctions(QQ).st() sage: ht(s[2,1]) ht[1, 1] + ht[2, 1] - ht[3] sage: s(ht[2,1]) s[1] -

Method signatures and docstrings:
- def __init__(self, Sym, other_basis, bname, pfix): Initialize the basis and register coercions. The coercions are set up between the ``other_basis``. INPUT: - ``Sym`` -- an instance of the symmetric function algebra - ``other_basis`` -- a basis of Sym - ``bname`` -- the name for this basis (convention: ends in "character") - ``pfix`` -- a prefix to use for the basis EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: ht = SymmetricFunctions(QQ).ht(); ht Symmetric Functions over Rational Field in the induced trivial character basis sage: st = SymmetricFunctions(QQ).st(); st Symmetric Functions over Rational Field in the irreducible symmetric group character basis sage: TestSuite(ht).run()
- def _self_to_other_on_basis(self, lam): Convert a character-basis element to the ``self._other`` basis. This is a recursive procedure that is calculated by the assumption that the leading term of ``self(lam)`` is ``other(lam)`` and ``evalsf(self(lam),n) == other([n-sum(lam)]+lam)``. INPUT: - ``lam`` -- a partition OUTPUT: - an expression in the ``self._other`` basis EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: ht = SymmetricFunctions(QQ).ht() sage: ht._self_to_other_on_basis(Partition([2,1])) h[1] - 2*h[1, 1] + h[2, 1] sage: st = SymmetricFunctions(QQ).st() sage: st._self_to_other_on_basis(Partition([2,1])) 3*s[1] - 2*s[1, 1] - 2*s[2] + s[2, 1] TESTS:: sage: h = SymmetricFunctions(QQ).h() sage: ht = SymmetricFunctions(QQ).h

<|skeleton|>
class character_basis:
    """General code for a character basis (irreducible and induced trivial). This is a basis of the symmetric functions that has the property that ``self(la).character_to_frobenius_image(n)`` is equal to ``other([n-sum(la)]+la)``. It should also have the property that the (outer) structure constants are the analogue of the stable Kronecker coefficients on the ``other`` basis (where ``other`` is either the Schur or homogeneous bases). These bases are introduced in [OZ2015]_. EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: s = Sym.s() sage: h = Sym.h() sage: ht = SymmetricFunctions(QQ).ht() sage: st = SymmetricFunctions(QQ).st() sage: ht(s[2,1]) ht[1, 1] + ht[2, 1] - ht[3] sage: s(ht[2,1]) s[1] -"""

    def __init__(self, Sym, other_basis, bname, pfix):
        """Initialize the basis and register coercions. The coercions are set up between the ``other_basis``. INPUT: - ``Sym`` -- an instance of the symmetric function algebra - ``other_basis`` -- a basis of Sym - ``bname`` -- the name for this basis (convention: ends in "character") - ``pfix`` -- a prefix to use for the basis EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: ht = SymmetricFunctions(QQ).ht(); ht Symmetric Functions over Rational Field in the induced trivial character basis sage: st = SymmetricFunctions(QQ).st(); st Symmetric Functions over Rational Field in the irreducible symmetric group character basis sage: TestSuite(ht).run()"""
        <|body_0|>

    def _self_to_other_on_basis(self, lam):
        """Convert a character-basis element to the ``self._other`` basis. This is a recursive procedure that is calculated by the assumption that the leading term of ``self(lam)`` is ``other(lam)`` and ``evalsf(self(lam),n) == other([n-sum(lam)]+lam)``. INPUT: - ``lam`` -- a partition OUTPUT: - an expression in the ``self._other`` basis EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: ht = SymmetricFunctions(QQ).ht() sage: ht._self_to_other_on_basis(Partition([2,1])) h[1] - 2*h[1, 1] + h[2, 1] sage: st = SymmetricFunctions(QQ).st() sage: st._self_to_other_on_basis(Partition([2,1])) 3*s[1] - 2*s[1, 1] - 2*s[2] + s[2, 1] TESTS:: sage: h = SymmetricFunctions(QQ).h() sage: ht = SymmetricFunctions(QQ).h"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        SFA_generic.__init__(self, Sym, basis_name=bname, prefix=pfix, graded=False)
        self._other = other_basis
        self.module_morphism(self._self_to_other_on_basis, codomain=self._other).register_as_coercion()
        self.register_coercion(SetMorphism(Hom(self._other, self), self._other_to_self))
<|end_body_0|>

<|body_start_1|>
        if not lam:
            return self._other([])
        n = sum(lam) + lam[0]
        sim = self._other(self._other(lam).character_to_frobenius_image(n))
        return self._other(lam) - sum((c * self._self_to_other_on_basis(Partition(mu[1:])) for mu, c in sim if mu[1:] != lam))
<|end_body_1|>

Solution:
class character_basis:
    """General code for a character basis (irreducible and induced trivial). This is a basis of the symmetric functions that has the property that ``self(la).character_to_frobenius_image(n)`` is equal to ``other([n-sum(la)]+la)``. It should also have the property that the (outer) structure constants are the analogue of the stable Kronecker coefficients on the ``other`` basis (where ``other`` is either the Schur or homogeneous bases). These bases are introduced in [OZ2015]_. EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: s = Sym.s() sage: h = Sym.h() sage: ht = SymmetricFunctions(QQ).ht() sage: st = SymmetricFunctions(QQ).st() sage: ht(s[2,1]) ht[1, 1] + ht[2, 1] - ht[3] sage: s(ht[2,1]) s[1] -"""

    def __init__(self, Sym, other_basis, bname, pfix):
        """Initialize the basis and register coercions. The coercions are set up between the ``other_basis``. INPUT: - ``Sym`` -- an instance of the symmetric function algebra - ``other_basis`` -- a basis of Sym - ``bname`` -- the name for this basis (convention: ends in "character") - ``pfix`` -- a prefix to use for the basis EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: ht = SymmetricFunctions(QQ).ht(); ht Symmetric Functions over Rational Field in the induced trivial character basis sage: st = SymmetricFunctions(QQ).st(); st Symmetric Functions over Rational Field in the irreducible symmetric group character basis sage: TestSuite(ht).run()"""
        SFA_generic.__init__(self, Sym, basis_name=bname, prefix=pfix, graded=False)
        self._other = other_basis
        self.module_morphism(self._self_to_other_on_basis, codomain=self._other).register_as_coercion()
        self.register_coercion(SetMorphism(Hom(self._other, self), self._other_to_self))

    def _self_to_other_on_basis(self, lam):
        """Convert a character-basis element to the ``self._other`` basis. This is a recursive procedure that is calculated by the assumption that the leading term of ``self(lam)`` is ``other(lam)`` and ``evalsf(self(lam),n) == other([n-sum(lam)]+lam)``. INPUT: - ``lam`` -- a partition OUTPUT: - an expression in the ``self._other`` basis EXAMPLES:: sage: Sym = SymmetricFunctions(QQ) sage: ht = SymmetricFunctions(QQ).ht() sage: ht._self_to_other_on_basis(Partition([2,1])) h[1] - 2*h[1, 1] + h[2, 1] sage: st = SymmetricFunctions(QQ).st() sage: st._self_to_other_on_basis(Partition([2,1])) 3*s[1] - 2*s[1, 1] - 2*s[2] + s[2, 1] TESTS:: sage: h = SymmetricFunctions(QQ).h() sage: ht = SymmetricFunctions(QQ).h"""
        if not lam:
            return self._other([])
        n = sum(lam) + lam[0]
        sim = self._other(self._other(lam).character_to_frobenius_image(n))
        return self._other(lam) - sum((c * self._self_to_other_on_basis(Partition(mu[1:])) for mu, c in sim if mu[1:] != lam))


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

Class description:
Implement the QueryDiff class.

Method signatures and docstrings:
- def __init__(self, run1_file, run2_file, qrels, metric): :param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of qrels file :param metric: metric :return:
- def dump_differences(self, output_file): Outputs query-level differences between two methods into a tab-separated file. The first method is considered the baseline, the differences are with respect to that. Output format: queryID res1 res2 diff(res2-res1)

Implement the Python class `QueryDiff` described below.

Class description:
Implement the QueryDiff class.

Method signatures and docstrings:
- def __init__(self, run1_file, run2_file, qrels, metric): :param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of qrels file :param metric: metric :return:
- def dump_differences(self, output_file): Outputs query-level differences between two methods into a tab-separated file. The first method is considered the baseline, the differences are with respect to that. Output format: queryID res1 res2 diff(res2-res1)

<|skeleton|>
class QueryDiff:

    def __init__(self, run1_file, run2_file, qrels, metric):
        """:param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of qrels file :param metric: metric :return:"""
        <|body_0|>

    def dump_differences(self, output_file):
        """Outputs query-level differences between two methods into a tab-separated file. The first method is considered the baseline, the differences are with respect to that. Output format: queryID res1 res2 diff(res2-res1)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.__run1_file = run1_file
        self.__run2_file = run2_file
        self.__qrels = qrels
        self.__metric = metric
<|end_body_0|>

<|body_start_1|>
        te_method1 = TrecEval()
        te_method1.evaluate(self.__qrels, self.__run1_file)
        te_method2 = TrecEval()
        te_method2.evaluate(self.__qrels, self.__run2_file)
        data = []
        for query_id in te_method1.get_query_ids():
            res1 = te_method1.get_score(query_id, self.__metric)
            res2 = te_method2.get_score(query_id, self.__metric)
            data.append([query_id, res1, res2, round(res2 - res1, 4)])
        sorted_data = sorted(data, key=lambda l: l[3], reverse=True)
        FileUtils.dump_tsv(output_file, sorted_data, header=['queryID', 'method1', 'method2', 'diff'])
<|end_body_1|>

Solution:
class QueryDiff:
    def __init__(self, run1_file, run2_file, qrels, metric):
        """:param run1_file: name of run1 file (baseline) :param run2_file: name of run2 file (new method) :param qrels: name of qrels file :param metric: metric :return:"""
        self.__run1_file = run1_file
        self.__run2_file = run2_file
        self.__qrels = qrels
        self.__metric = metric

    def dump_differences(self, output_file):
        """Outputs query-level differences between two methods into a tab-separated file. The first method is considered the baseline, the differences are with respect to that. Output format: queryID res1 res2 diff(res2-res1)"""
        te_method1 = TrecEval()
        te_method1.evaluate(self.__qrels, self.__run1_file)
        te_method2 = TrecEval()
        te_method2.evaluate(self.__qrels, self.__run2_file)
        data = []
        for query_id in te_method1.get_query_ids():
            res1 = te_method1.get_score(query_id, self.__metric)
            res2 = te_method2.get_score(query_id, self.__metric)
            data.append([query_id, res1, res2, round(res2 - res1, 4)])
        sorted_data = sorted(data, key=lambda l: l[3], reverse=True)
        FileUtils.dump_tsv(output_file, sorted_data, header=['queryID', 'method1', 'method2', 'diff'])


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

Class description:
Implement the ExcelUtil class.

Method signatures and docstrings:
- def __init__(self, excel_path=None, sheet_name=None): 获取excel工作表
- def get_data(self) -> object: 获取文件数据 每一行数据一个list，所有的数据一个大list :return:

Implement the Python class `ExcelUtil` described below.

Class description:
Implement the ExcelUtil class.

Method signatures and docstrings:
- def __init__(self, excel_path=None, sheet_name=None): 获取excel工作表
- def get_data(self) -> object: 获取文件数据 每一行数据一个list，所有的数据一个大list :return:

<|skeleton|>
class ExcelUtil:

    def __init__(self, excel_path=None, sheet_name=None):
        """获取excel工作表"""
        <|body_0|>

    def get_data(self) -> object:
        """获取文件数据 每一行数据一个list，所有的数据一个大list :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if excel_path == None:
            current_path = os.path.abspath(os.path.dirname(__file__))
            self.excel_path = current_path + '/../data/casedata.xlsx'
        else:
            self.excel_path = excel_path
        if sheet_name == None:
            self.sheet_name = 'Sheet'
        else:
            self.sheet_name = sheet_name
        self.data = load_workbook(self.excel_path)
        self.sheet = self.data[self.sheet_name]
<|end_body_0|>

<|body_start_1|>
        rows = self.sheet.rows
        row_num = self.sheet.max_row
        col_num = self.sheet.max_column
        if row_num <= 1:
            print('总行数小于1，没有数据')
        else:
            case_all = []
            for row in rows:
                case = []
                for i in range(col_num):
                    case.append(row[i].value)
                case_all.append(case)
            return case_all
<|end_body_1|>

Solution:
class ExcelUtil:
    def __init__(self, excel_path=None, sheet_name=None):
        """获取excel工作表"""
        if excel_path == None:
            current_path = os.path.abspath(os.path.dirname(__file__))
            self.excel_path = current_path + '/../data/casedata.xlsx'
        else:
            self.excel_path = excel_path
        if sheet_name == None:
            self.sheet_name = 'Sheet'
        else:
            self.sheet_name = sheet_name
        self.data = load_workbook(self.excel_path)
        self.sheet = self.data[self.sheet_name]

    def get_data(self) -> object:
        """获取文件数据 每一行数据一个list，所有的数据一个大list :return:"""
        rows = self.sheet.rows
        row_num = self.sheet.max_row
        col_num = self.sheet.max_column
        if row_num <= 1:
            print('总行数小于1，没有数据')
        else:
            case_all = []
            for row in rows:
                case = []
                for i in range(col_num):
                    case.append(row[i].value)
                case_all.append(case)
            return case_all


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

Class description:
Check contract (validator) after function processing. Validate output result.

Method signatures and docstrings:
- def patched_function(self, *args, **kwargs): Step 3. Wrapped function calling.
- async def async_patched_function(self, *args, **kwargs): Step 3. Wrapped function calling.
- def patched_generator(self, *args, **kwargs): Step 3. Wrapped function calling.

Implement the Python class `Post` described below.

Class description:
Check contract (validator) after function processing. Validate output result.

Method signatures and docstrings:
- def patched_function(self, *args, **kwargs): Step 3. Wrapped function calling.
- async def async_patched_function(self, *args, **kwargs): Step 3. Wrapped function calling.
- def patched_generator(self, *args, **kwargs): Step 3. Wrapped function calling.

<|skeleton|>
class Post:
    """Check contract (validator) after function processing. Validate output result."""

    def patched_function(self, *args, **kwargs):
        """Step 3. Wrapped function calling."""
        <|body_0|>

    async def async_patched_function(self, *args, **kwargs):
        """Step 3. Wrapped function calling."""
        <|body_1|>

    def patched_generator(self, *args, **kwargs):
        """Step 3. Wrapped function calling."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        result = self.function(*args, **kwargs)
        self.validate(result)
        return result
<|end_body_0|>

<|body_start_1|>
        result = await self.function(*args, **kwargs)
        self.validate(result)
        return result
<|end_body_1|>

<|body_start_2|>
        for result in self.function(*args, **kwargs):
            self.validate(result)
            yield result
<|end_body_2|>

Solution:
class Post:
    """Check contract (validator) after function processing. Validate output result."""

    def patched_function(self, *args, **kwargs):
        """Step 3. Wrapped function calling."""
        result = self.function(*args, **kwargs)
        self.validate(result)
        return result

    async def async_patched_function(self, *args, **kwargs):
        """Step 3. Wrapped function calling."""
        result = await self.function(*args, **kwargs)
        self.validate(result)
        return result

    def patched_generator(self, *args, **kwargs):
        """Step 3. Wrapped function calling."""
        for result in self.function(*args, **kwargs):
            self.validate(result)
            yield result


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

Class description:
Apply a custom defined transform to data. .. warning:: The explicit purpose of this Bokeh Model is to embed *raw JavaScript code* for a browser to execute. If any part of the code is derived from untrusted user inputs, then you must take appropriate care to sanitize the user input prior to passing to Bokeh.

Method signatures and docstrings:
- def from_py_func(cls, func, v_func): Create a ``CustomJSTransform`` instance from a pair of Python functions. The function is translated to JavaScript using PScript. The python functions must have no positional arguments. It's possible to pass Bokeh models (e.g. a ``ColumnDataSource``) as keyword arguments to the functions. The ``func`` function namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` function namespace will contain the variable ``xs`` (the untransformed vector) at render time. .. warning:: The vectorized function, ``v_func``, must return an array of the same length as the input ``xs`` array. Example: .. code-block:: python def transform(): from pscript.stubs import Mat
- def from_coffeescript(cls, func, v_func, args={}): Create a ``CustomJSTransform`` instance from a pair of CoffeeScript snippets. The function bodies are translated to JavaScript functions using node and therefore require return statements. The ``func`` snippet namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` snippet namespace will contain the variable ``xs`` (the untransformed vector) at render time. Example: .. code-block:: coffeescript func = "return Math.cos(x)" v_func = "return [Math.cos(x) for x in xs]" transform = CustomJSTransform.from_coffeescript(func, v_func) Args: func (str) : a coffeescript snippet to transform a single ``x`` value v_func (str) : a coffeescript snippet function to

Implement the Python class `CustomJSTransform` described below.

Class description:
Apply a custom defined transform to data. .. warning:: The explicit purpose of this Bokeh Model is to embed *raw JavaScript code* for a browser to execute. If any part of the code is derived from untrusted user inputs, then you must take appropriate care to sanitize the user input prior to passing to Bokeh.

Method signatures and docstrings:
- def from_py_func(cls, func, v_func): Create a ``CustomJSTransform`` instance from a pair of Python functions. The function is translated to JavaScript using PScript. The python functions must have no positional arguments. It's possible to pass Bokeh models (e.g. a ``ColumnDataSource``) as keyword arguments to the functions. The ``func`` function namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` function namespace will contain the variable ``xs`` (the untransformed vector) at render time. .. warning:: The vectorized function, ``v_func``, must return an array of the same length as the input ``xs`` array. Example: .. code-block:: python def transform(): from pscript.stubs import Mat
- def from_coffeescript(cls, func, v_func, args={}): Create a ``CustomJSTransform`` instance from a pair of CoffeeScript snippets. The function bodies are translated to JavaScript functions using node and therefore require return statements. The ``func`` snippet namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` snippet namespace will contain the variable ``xs`` (the untransformed vector) at render time. Example: .. code-block:: coffeescript func = "return Math.cos(x)" v_func = "return [Math.cos(x) for x in xs]" transform = CustomJSTransform.from_coffeescript(func, v_func) Args: func (str) : a coffeescript snippet to transform a single ``x`` value v_func (str) : a coffeescript snippet function to

<|skeleton|>
class CustomJSTransform:
    """Apply a custom defined transform to data. .. warning:: The explicit purpose of this Bokeh Model is to embed *raw JavaScript code* for a browser to execute. If any part of the code is derived from untrusted user inputs, then you must take appropriate care to sanitize the user input prior to passing to Bokeh."""

    def from_py_func(cls, func, v_func):
        """Create a ``CustomJSTransform`` instance from a pair of Python functions. The function is translated to JavaScript using PScript. The python functions must have no positional arguments. It's possible to pass Bokeh models (e.g. a ``ColumnDataSource``) as keyword arguments to the functions. The ``func`` function namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` function namespace will contain the variable ``xs`` (the untransformed vector) at render time. .. warning:: The vectorized function, ``v_func``, must return an array of the same length as the input ``xs`` array. Example: .. code-block:: python def transform(): from pscript.stubs import Mat"""
        <|body_0|>

    def from_coffeescript(cls, func, v_func, args={}):
        """Create a ``CustomJSTransform`` instance from a pair of CoffeeScript snippets. The function bodies are translated to JavaScript functions using node and therefore require return statements. The ``func`` snippet namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` snippet namespace will contain the variable ``xs`` (the untransformed vector) at render time. Example: .. code-block:: coffeescript func = "return Math.cos(x)" v_func = "return [Math.cos(x) for x in xs]" transform = CustomJSTransform.from_coffeescript(func, v_func) Args: func (str) : a coffeescript snippet to transform a single ``x`` value v_func (str) : a coffeescript snippet function to"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        from bokeh.util.deprecation import deprecated
        deprecated("'from_py_func' is deprecated and will be removed in an eventual 2.0 release. Use CustomJSTransform directly instead.")
        if not isinstance(func, FunctionType) or not isinstance(v_func, FunctionType):
            raise ValueError('CustomJSTransform.from_py_func only accepts function objects.')
        pscript = import_required('pscript', dedent('                To use Python functions for CustomJSTransform, you need PScript\n                \'("conda install -c conda-forge pscript" or "pip install pscript")'))

        def pscript_compile(func):
            sig = signature(func)
            all_names, default_values = get_param_info(sig)
            if len(all_names) - len(default_values) != 0:
                raise ValueError('Function may only contain keyword arguments.')
            if default_values and (not any((isinstance(value, Model) for value in default_values))):
                raise ValueError('Default value must be a Bokeh Model.')
            func_kwargs = dict(zip(all_names, default_values))
            code = pscript.py2js(func, 'transformer') + 'return transformer(%s);\n' % ', '.join(all_names)
            return (code, func_kwargs)
        jsfunc, func_kwargs = pscript_compile(func)
        v_jsfunc, v_func_kwargs = pscript_compile(v_func)
        func_kwargs.update(v_func_kwargs)
        return cls(func=jsfunc, v_func=v_jsfunc, args=func_kwargs)
<|end_body_0|>

<|body_start_1|>
        compiled = nodejs_compile(func, lang='coffeescript', file='???')
        if 'error' in compiled:
            raise CompilationError(compiled.error)
        v_compiled = nodejs_compile(v_func, lang='coffeescript', file='???')
        if 'error' in v_compiled:
            raise CompilationError(v_compiled.error)
        return cls(func=compiled.code, v_func=v_compiled.code, args=args)
<|end_body_1|>

Solution:
class CustomJSTransform:
    """Apply a custom defined transform to data. .. warning:: The explicit purpose of this Bokeh Model is to embed *raw JavaScript code* for a browser to execute. If any part of the code is derived from untrusted user inputs, then you must take appropriate care to sanitize the user input prior to passing to Bokeh."""

    def from_py_func(cls, func, v_func):
        """Create a ``CustomJSTransform`` instance from a pair of Python functions. The function is translated to JavaScript using PScript. The python functions must have no positional arguments. It's possible to pass Bokeh models (e.g. a ``ColumnDataSource``) as keyword arguments to the functions. The ``func`` function namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` function namespace will contain the variable ``xs`` (the untransformed vector) at render time. .. warning:: The vectorized function, ``v_func``, must return an array of the same length as the input ``xs`` array. Example: .. code-block:: python def transform(): from pscript.stubs import Mat"""
        from bokeh.util.deprecation import deprecated
        deprecated("'from_py_func' is deprecated and will be removed in an eventual 2.0 release. Use CustomJSTransform directly instead.")
        if not isinstance(func, FunctionType) or not isinstance(v_func, FunctionType):
            raise ValueError('CustomJSTransform.from_py_func only accepts function objects.')
        pscript = import_required('pscript', dedent('                To use Python functions for CustomJSTransform, you need PScript\n                \'("conda install -c conda-forge pscript" or "pip install pscript")'))

        def pscript_compile(func):
            sig = signature(func)
            all_names, default_values = get_param_info(sig)
            if len(all_names) - len(default_values) != 0:
                raise ValueError('Function may only contain keyword arguments.')
            if default_values and (not any((isinstance(value, Model) for value in default_values))):
                raise ValueError('Default value must be a Bokeh Model.')
            func_kwargs = dict(zip(all_names, default_values))
            code = pscript.py2js(func, 'transformer') + 'return transformer(%s);\n' % ', '.join(all_names)
            return (code, func_kwargs)
        jsfunc, func_kwargs = pscript_compile(func)
        v_jsfunc, v_func_kwargs = pscript_compile(v_func)
        func_kwargs.update(v_func_kwargs)
        return cls(func=jsfunc, v_func=v_jsfunc, args=func_kwargs)

    def from_coffeescript(cls, func, v_func, args={}):
        """Create a ``CustomJSTransform`` instance from a pair of CoffeeScript snippets. The function bodies are translated to JavaScript functions using node and therefore require return statements. The ``func`` snippet namespace will contain the variable ``x`` (the untransformed value) at render time. The ``v_func`` snippet namespace will contain the variable ``xs`` (the untransformed vector) at render time. Example: .. code-block:: coffeescript func = "return Math.cos(x)" v_func = "return [Math.cos(x) for x in xs]" transform = CustomJSTransform.from_coffeescript(func, v_func) Args: func (str) : a coffeescript snippet to transform a single ``x`` value v_func (str) : a coffeescript snippet function to"""
        compiled = nodejs_compile(func, lang='coffeescript', file='???')
        if 'error' in compiled:
            raise CompilationError(compiled.error)
        v_compiled = nodejs_compile(v_func, lang='coffeescript', file='???')
        if 'error' in v_compiled:
            raise CompilationError(v_compiled.error)
        return cls(func=compiled.code, v_func=v_compiled.code, args=args)


deserialize

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

Class description:
Implement the Codec class.

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

Implement the Python class `Codec` described below.

Class description:
Implement the Codec class.

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

<|skeleton|>
class Codec:

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

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

<|end_skeleton|>

<|body_start_0|>
        def rserialize(root, string):
            """ a recursive helper function for the serialize() function."""
            if root is None:
                string += 'None,'
            else:
                string += str(root.val) + ','
                string = rserialize(root.left, string)
                string = rserialize(root.right, string)
            return string
        return rserialize(root, '')
<|end_body_0|>

<|body_start_1|>
        def rdeserialize(l):
            """ a recursive helper function for deserialization."""
            if l[0] == 'None':
                l.pop(0)
                return None
            root = Node(l[0])
            l.pop(0)
            root.left = rdeserialize(l)
            root.right = rdeserialize(l)
            return root
        data_list = data.split(',')
        root = rdeserialize(data_list)
        return root
<|end_body_1|>

Solution:
class Codec:
    def serialize(self, root):
        """Encodes a tree to a single string. :type root: Node :rtype: str"""
        def rserialize(root, string):
            """ a recursive helper function for the serialize() function."""
            if root is None:
                string += 'None,'
            else:
                string += str(root.val) + ','
                string = rserialize(root.left, string)
                string = rserialize(root.right, string)
            return string
        return rserialize(root, '')

    def deserialize(self, data):
        """Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
        def rdeserialize(l):
            """ a recursive helper function for deserialization."""
            if l[0] == 'None':
                l.pop(0)
                return None
            root = Node(l[0])
            l.pop(0)
            root.left = rdeserialize(l)
            root.right = rdeserialize(l)
            return root
        data_list = data.split(',')
        root = rdeserialize(data_list)
        return root


predict

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

Class description:
Implement the MRI_BET class.

Method signatures and docstrings:
- def __init__(self, weight_path: str=None): Initialize the model with its weight. Args: (string) weight_path : model's weight path
- def _preprocessing(self, path: str, img_type: str, min_percent: float=40.0, max_percent: float=98.5, out_min: int=0, out_max: int=1) -> torch.Tensor: Preprocess the image from the path Args: (string) path : absolute path of data (string) img_type : MRI modality type for applying different preprocessing according to MRI modality (float) min_percent : Min percentile to compute, which must be between 0 and 100 inclusive (default : 40) (float) max_percent : Max percentile to compute, which must be between 0 and 100 inclusive (default : 98.5) (integer) out_min : minimum value of output (default : 0) (integer) out_max : maximum value of output (default : 1) Return: (numpy ndarray) data with shape (1, h, w, d, 1)
- def _postprocessing(self, data: np.array) -> np.array: Postprocess the predicted data to reduce FP:wq Args: (numpy ndarray) 3d data with shape (h, w, d) Return: (numpy ndarray) 3d data with shape (h, w, d)
- def predict(self, path: str, img_type: str='T1', save_path: Optional[str]=None, thres: float=0.5) -> Tuple[np.array, np.array]: Brain tissue segmentation Args: (string) path : absolute path of data (string) img_type : MRI modality type('T1' for T1-weighted MRI, 'MRA' for MR angiography) (string) save_path : If save_path is set, the mask and the skull-stripped image will be saved. (float) thres : probability threshold to make a mask pixel white (default : 0.5) Return: (numpy ndarray) 3d brain tissue mask

Implement the Python class `MRI_BET` described below.

Class description:
Implement the MRI_BET class.

Method signatures and docstrings:
- def __init__(self, weight_path: str=None): Initialize the model with its weight. Args: (string) weight_path : model's weight path
- def _preprocessing(self, path: str, img_type: str, min_percent: float=40.0, max_percent: float=98.5, out_min: int=0, out_max: int=1) -> torch.Tensor: Preprocess the image from the path Args: (string) path : absolute path of data (string) img_type : MRI modality type for applying different preprocessing according to MRI modality (float) min_percent : Min percentile to compute, which must be between 0 and 100 inclusive (default : 40) (float) max_percent : Max percentile to compute, which must be between 0 and 100 inclusive (default : 98.5) (integer) out_min : minimum value of output (default : 0) (integer) out_max : maximum value of output (default : 1) Return: (numpy ndarray) data with shape (1, h, w, d, 1)
- def _postprocessing(self, data: np.array) -> np.array: Postprocess the predicted data to reduce FP:wq Args: (numpy ndarray) 3d data with shape (h, w, d) Return: (numpy ndarray) 3d data with shape (h, w, d)
- def predict(self, path: str, img_type: str='T1', save_path: Optional[str]=None, thres: float=0.5) -> Tuple[np.array, np.array]: Brain tissue segmentation Args: (string) path : absolute path of data (string) img_type : MRI modality type('T1' for T1-weighted MRI, 'MRA' for MR angiography) (string) save_path : If save_path is set, the mask and the skull-stripped image will be saved. (float) thres : probability threshold to make a mask pixel white (default : 0.5) Return: (numpy ndarray) 3d brain tissue mask

<|skeleton|>
class MRI_BET:

    def __init__(self, weight_path: str=None):
        """Initialize the model with its weight. Args: (string) weight_path : model's weight path"""
        <|body_0|>

    def _preprocessing(self, path: str, img_type: str, min_percent: float=40.0, max_percent: float=98.5, out_min: int=0, out_max: int=1) -> torch.Tensor:
        """Preprocess the image from the path Args: (string) path : absolute path of data (string) img_type : MRI modality type for applying different preprocessing according to MRI modality (float) min_percent : Min percentile to compute, which must be between 0 and 100 inclusive (default : 40) (float) max_percent : Max percentile to compute, which must be between 0 and 100 inclusive (default : 98.5) (integer) out_min : minimum value of output (default : 0) (integer) out_max : maximum value of output (default : 1) Return: (numpy ndarray) data with shape (1, h, w, d, 1)"""
        <|body_1|>

    def _postprocessing(self, data: np.array) -> np.array:
        """Postprocess the predicted data to reduce FP:wq Args: (numpy ndarray) 3d data with shape (h, w, d) Return: (numpy ndarray) 3d data with shape (h, w, d)"""
        <|body_2|>

    def predict(self, path: str, img_type: str='T1', save_path: Optional[str]=None, thres: float=0.5) -> Tuple[np.array, np.array]:
        """Brain tissue segmentation Args: (string) path : absolute path of data (string) img_type : MRI modality type('T1' for T1-weighted MRI, 'MRA' for MR angiography) (string) save_path : If save_path is set, the mask and the skull-stripped image will be saved. (float) thres : probability threshold to make a mask pixel white (default : 0.5) Return: (numpy ndarray) 3d brain tissue mask"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.model = MRIBET().cuda()
        if weight_path is not None:
            weight = torch.load(weight_path, map_location='cuda:0')
            self.model.load_state_dict(weight['net'])
<|end_body_0|>

<|body_start_1|>
        read_data = nib.load(path)
        data = read_data.get_fdata().astype(np.float32)
        if img_type == 'T1':
            pass
        elif img_type == 'MRA':
            w_min = np.percentile(data, min_percent)
            w_max = np.percentile(data, max_percent)
            width = w_max - w_min + 1
            center = w_min + width / 2
            data = ((data - center) / width + 0.5) * (out_max - out_min)
            data = np.piecewise(data, [data <= out_min, data >= out_max], [out_min, out_max, lambda data: data])
        data = torch.from_numpy(data)[np.newaxis, np.newaxis, ...]
        data = data.cuda()
        return (data, read_data)
<|end_body_1|>

<|body_start_2|>
        kernel = np.ones((5, 5), np.int8)
        data = [cv2.morphologyEx(data[:, :, z], cv2.MORPH_OPEN, kernel) for z in range(data.shape[2])]
        data = np.transpose(np.asarray(data), (1, 2, 0))
        img_labels, num_labels = ndimage.label(data)
        sizes = ndimage.sum(data, img_labels, range(num_labels + 1))
        remove_cluster = sizes < np.max(sizes)
        remove_pixel = remove_cluster[img_labels]
        data[remove_pixel] = 0
        data[data > 0] = 1
        data = ndimage.binary_fill_holes(data).astype(np.float32)
        return data
<|end_body_2|>

<|body_start_3|>
        data, read_data = self._preprocessing(path, img_type=img_type)
        self.model.eval()
        mask = np.zeros(np.squeeze(data).shape)
        for z in range(mask.shape[2]):
            output = self.model(data[:, :, :, :, z])
            output = torch.sigmoid(output)
            output = output.cpu().detach().numpy().squeeze()
            mask[:, :, z] = np.where(output >= thres, 1, 0)
        mask = self._postprocessing(mask)
        mask = mask.astype(np.uint8)
        if save_path:
            mask_save = nib.Nifti1Image(mask, read_data.affine, read_data.header)
            nib.save(mask_save, save_path)
            org_img = read_data.get_fdata()
            img_strip_save = nib.Nifti1Image(np.where(mask == 1, org_img, 0), read_data.affine, read_data.header)
            nib.save(img_strip_save, path.replace('.nii', '_stripped.nii'))
        return (np.squeeze(data), mask)
<|end_body_3|>

Solution:
class MRI_BET:
    def __init__(self, weight_path: str=None):
        """Initialize the model with its weight. Args: (string) weight_path : model's weight path"""
        self.model = MRIBET().cuda()
        if weight_path is not None:
            weight = torch.load(weight_path, map_location='cuda:0')
            self.model.load_state_dict(weight['net'])

    def _preprocessing(self, path: str, img_type: str, min_percent: float=40.0, max_percent: float=98.5, out_min: int=0, out_max: int=1) -> torch.Tensor:
        """Preprocess the image from the path Args: (string) path : absolute path of data (string) img_type : MRI modality type for applying different preprocessing according to MRI modality (float) min_percent : Min percentile to compute, which must be between 0 and 100 inclusive (default : 40) (float) max_percent : Max percentile to compute, which must be between 0 and 100 inclusive (default : 98.5) (integer) out_min : minimum value of output (default : 0) (integer) out_max : maximum value of output (default : 1) Return: (numpy ndarray) data with shape (1, h, w, d, 1)"""
        read_data = nib.load(path)
        data = read_data.get_fdata().astype(np.float32)
        if img_type == 'T1':
            pass
        elif img_type == 'MRA':
            w_min = np.percentile(data, min_percent)
            w_max = np.percentile(data, max_percent)
            width = w_max - w_min + 1
            center = w_min + width / 2
            data = ((data - center) / width + 0.5) * (out_max - out_min)
            data = np.piecewise(data, [data <= out_min, data >= out_max], [out_min, out_max, lambda data: data])
        data = torch.from_numpy(data)[np.newaxis, np.newaxis, ...]
        data = data.cuda()
        return (data, read_data)

    def _postprocessing(self, data: np.array) -> np.array:
        """Postprocess the predicted data to reduce FP:wq Args: (numpy ndarray) 3d data with shape (h, w, d) Return: (numpy ndarray) 3d data with shape (h, w, d)"""
        kernel = np.ones((5, 5), np.int8)
        data = [cv2.morphologyEx(data[:, :, z], cv2.MORPH_OPEN, kernel) for z in range(data.shape[2])]
        data = np.transpose(np.asarray(data), (1, 2, 0))
        img_labels, num_labels = ndimage.label(data)
        sizes = ndimage.sum(data, img_labels, range(num_labels + 1))
        remove_cluster = sizes < np.max(sizes)
        remove_pixel = remove_cluster[img_labels]
        data[remove_pixel] = 0
        data[data > 0] = 1
        data = ndimage.binary_fill_holes(data).astype(np.float32)
        return data

    def predict(self, path: str, img_type: str='T1', save_path: Optional[str]=None, thres: float=0.5) -> Tuple[np.array, np.array]:
        """Brain tissue segmentation Args: (string) path : absolute path of data (string) img_type : MRI modality type('T1' for T1-weighted MRI, 'MRA' for MR angiography) (string) save_path : If save_path is set, the mask and the skull-stripped image will be saved. (float) thres : probability threshold to make a mask pixel white (default : 0.5) Return: (numpy ndarray) 3d brain tissue mask"""
        data, read_data = self._preprocessing(path, img_type=img_type)
        self.model.eval()
        mask = np.zeros(np.squeeze(data).shape)
        for z in range(mask.shape[2]):
            output = self.model(data[:, :, :, :, z])
            output = torch.sigmoid(output)
            output = output.cpu().detach().numpy().squeeze()
            mask[:, :, z] = np.where(output >= thres, 1, 0)
        mask = self._postprocessing(mask)
        mask = mask.astype(np.uint8)
        if save_path:
            mask_save = nib.Nifti1Image(mask, read_data.affine, read_data.header)
            nib.save(mask_save, save_path)
            org_img = read_data.get_fdata()
            img_strip_save = nib.Nifti1Image(np.where(mask == 1, org_img, 0), read_data.affine, read_data.header)
            nib.save(img_strip_save, path.replace('.nii', '_stripped.nii'))
        return (np.squeeze(data), mask)


connect

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def connect(self, root): ' 修已改版bfs 只使用常数项空间 经有上一层的next了，所以就不需要队列来存储了 :type root: Node :rtype: Node
- def connect2(self, root): bfs遍历每一层 下一个节点在同一个节点的时候next指向下一个节点 不符合常数空间要求 :type root: Node :rtype: Node
- def connect3(self, root): ' 递归版 :type root: Node :rtype: Node

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def connect(self, root): ' 修已改版bfs 只使用常数项空间 经有上一层的next了，所以就不需要队列来存储了 :type root: Node :rtype: Node
- def connect2(self, root): bfs遍历每一层 下一个节点在同一个节点的时候next指向下一个节点 不符合常数空间要求 :type root: Node :rtype: Node
- def connect3(self, root): ' 递归版 :type root: Node :rtype: Node

<|skeleton|>
class Solution:

    def connect(self, root):
        """' 修已改版bfs 只使用常数项空间 经有上一层的next了，所以就不需要队列来存储了 :type root: Node :rtype: Node"""
        <|body_0|>

    def connect2(self, root):
        """bfs遍历每一层 下一个节点在同一个节点的时候next指向下一个节点 不符合常数空间要求 :type root: Node :rtype: Node"""
        <|body_1|>

    def connect3(self, root):
        """' 递归版 :type root: Node :rtype: Node"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if not root or not root.left:
            return root
        pre = root
        while pre.left:
            cur = pre
            while cur:
                cur.left.next = cur.right
                if cur.next:
                    cur.right.next = cur.next.left
                cur = cur.next
            pre = pre.left
        return root
<|end_body_0|>

<|body_start_1|>
        if not root:
            return root
        queue = [(root, 0)]
        while queue:
            cur, depth = queue.pop(0)
            if cur.left:
                queue.append((cur.left, depth + 1))
            if cur.right:
                queue.append((cur.right, depth + 1))
            if queue and queue[0][1] == depth:
                cur.next = queue[0][0]
        return root
<|end_body_1|>

<|body_start_2|>
        if not root or not root.left:
            return root
        root.left.next = root.right
        if root.next:
            root.right.next = root.next.left
        self.connect3(root.left)
        self.connect3(root.right)
        return root
<|end_body_2|>

Solution:
class Solution:
    def connect(self, root):
        """' 修已改版bfs 只使用常数项空间 经有上一层的next了，所以就不需要队列来存储了 :type root: Node :rtype: Node"""
        if not root or not root.left:
            return root
        pre = root
        while pre.left:
            cur = pre
            while cur:
                cur.left.next = cur.right
                if cur.next:
                    cur.right.next = cur.next.left
                cur = cur.next
            pre = pre.left
        return root

    def connect2(self, root):
        """bfs遍历每一层 下一个节点在同一个节点的时候next指向下一个节点 不符合常数空间要求 :type root: Node :rtype: Node"""
        if not root:
            return root
        queue = [(root, 0)]
        while queue:
            cur, depth = queue.pop(0)
            if cur.left:
                queue.append((cur.left, depth + 1))
            if cur.right:
                queue.append((cur.right, depth + 1))
            if queue and queue[0][1] == depth:
                cur.next = queue[0][0]
        return root

    def connect3(self, root):
        """' 递归版 :type root: Node :rtype: Node"""
        if not root or not root.left:
            return root
        root.left.next = root.right
        if root.next:
            root.right.next = root.next.left
        self.connect3(root.left)
        self.connect3(root.right)
        return root

116_填充每个节点的下一个右侧节点指针.py


Run

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

Class description:
Create a Cloud Spanner instance.

Method signatures and docstrings:
- def Args(parser): Args is called by calliope to gather arguments for this command. Please add arguments in alphabetical order except for no- or a clear- pair for that argument which can follow the argument itself. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed.
- def Run(self, args): This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. Returns: Some value that we want to have printed later.

Implement the Python class `Create` described below.

Class description:
Create a Cloud Spanner instance.

Method signatures and docstrings:
- def Args(parser): Args is called by calliope to gather arguments for this command. Please add arguments in alphabetical order except for no- or a clear- pair for that argument which can follow the argument itself. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed.
- def Run(self, args): This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. Returns: Some value that we want to have printed later.

<|skeleton|>
class Create:
    """Create a Cloud Spanner instance."""

    def Args(parser):
        """Args is called by calliope to gather arguments for this command. Please add arguments in alphabetical order except for no- or a clear- pair for that argument which can follow the argument itself. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed."""
        <|body_0|>

    def Run(self, args):
        """This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. Returns: Some value that we want to have printed later."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        flags.Instance().AddToParser(parser)
        flags.Config().AddToParser(parser)
        flags.Description().AddToParser(parser)
        resource_args.AddExpireBehaviorArg(parser)
        resource_args.AddInstanceTypeArg(parser)
        group_parser = parser.add_argument_group(mutex=True, required=False)
        flags.Nodes().AddToParser(group_parser)
        flags.ProcessingUnits().AddToParser(group_parser)
        base.ASYNC_FLAG.AddToParser(parser)
        parser.display_info.AddCacheUpdater(flags.InstanceCompleter)
<|end_body_0|>

<|body_start_1|>
        instance_type = resource_args.GetInstanceType(args)
        expire_behavior = resource_args.GetExpireBehavior(args)
        op = instances.Create(args.instance, args.config, args.description, args.nodes, args.processing_units, instance_type, expire_behavior)
        if args.async_:
            return op
        instance_operations.Await(op, 'Creating instance')
<|end_body_1|>

Solution:
class Create:
    """Create a Cloud Spanner instance."""

    def Args(parser):
        """Args is called by calliope to gather arguments for this command. Please add arguments in alphabetical order except for no- or a clear- pair for that argument which can follow the argument itself. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed."""
        flags.Instance().AddToParser(parser)
        flags.Config().AddToParser(parser)
        flags.Description().AddToParser(parser)
        resource_args.AddExpireBehaviorArg(parser)
        resource_args.AddInstanceTypeArg(parser)
        group_parser = parser.add_argument_group(mutex=True, required=False)
        flags.Nodes().AddToParser(group_parser)
        flags.ProcessingUnits().AddToParser(group_parser)
        base.ASYNC_FLAG.AddToParser(parser)
        parser.display_info.AddCacheUpdater(flags.InstanceCompleter)

    def Run(self, args):
        """This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. Returns: Some value that we want to have printed later."""
        instance_type = resource_args.GetInstanceType(args)
        expire_behavior = resource_args.GetExpireBehavior(args)
        op = instances.Create(args.instance, args.config, args.description, args.nodes, args.processing_units, instance_type, expire_behavior)
        if args.async_:
            return op
        instance_operations.Await(op, 'Creating instance')


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

Class description:
Implement the TestMethods class.

Method signatures and docstrings:
- def test_is_prime_valid(self): Valid primes are 2, 3, 5, 7.
- def test_is_prime_invalid(self): Valid primes are 2, 3, 5, 7.
- def test_example_1(self): Example 1: Input: n = 10 Output: 4 Explanation: There are 4 prime numbers less than 10, they are 2, 3, 5, 7.
- def test_example_2(self): Example 2: Input: n = 0 Output: 0
- def test_example_3(self): Example 3: Input: n = 1 Output: 0

Implement the Python class `TestMethods` described below.

Class description:
Implement the TestMethods class.

Method signatures and docstrings:
- def test_is_prime_valid(self): Valid primes are 2, 3, 5, 7.
- def test_is_prime_invalid(self): Valid primes are 2, 3, 5, 7.
- def test_example_1(self): Example 1: Input: n = 10 Output: 4 Explanation: There are 4 prime numbers less than 10, they are 2, 3, 5, 7.
- def test_example_2(self): Example 2: Input: n = 0 Output: 0
- def test_example_3(self): Example 3: Input: n = 1 Output: 0

<|skeleton|>
class TestMethods:

    def test_is_prime_valid(self):
        """Valid primes are 2, 3, 5, 7."""
        <|body_0|>

    def test_is_prime_invalid(self):
        """Valid primes are 2, 3, 5, 7."""
        <|body_1|>

    def test_example_1(self):
        """Example 1: Input: n = 10 Output: 4 Explanation: There are 4 prime numbers less than 10, they are 2, 3, 5, 7."""
        <|body_2|>

    def test_example_2(self):
        """Example 2: Input: n = 0 Output: 0"""
        <|body_3|>

    def test_example_3(self):
        """Example 3: Input: n = 1 Output: 0"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        sol = solution.Solution()
        self.assertTrue(sol.isPrime(2))
        self.assertTrue(sol.isPrime(3))
        self.assertTrue(sol.isPrime(7))
<|end_body_0|>

<|body_start_1|>
        sol = solution.Solution()
        self.assertFalse(sol.isPrime(1))
        self.assertFalse(sol.isPrime(4))
        self.assertFalse(sol.isPrime(6))
<|end_body_1|>

<|body_start_2|>
        sol = solution.Solution()
        n = 10
        self.assertEqual(4, sol.countPrimes(n))
<|end_body_2|>

<|body_start_3|>
        sol = solution.Solution()
        n = 0
        self.assertEqual(0, sol.countPrimes(n))
<|end_body_3|>

<|body_start_4|>
        sol = solution.Solution()
        n = 1
        self.assertEqual(0, sol.countPrimes(n))
<|end_body_4|>

Solution:
class TestMethods:
    def test_is_prime_valid(self):
        """Valid primes are 2, 3, 5, 7."""
        sol = solution.Solution()
        self.assertTrue(sol.isPrime(2))
        self.assertTrue(sol.isPrime(3))
        self.assertTrue(sol.isPrime(7))

    def test_is_prime_invalid(self):
        """Valid primes are 2, 3, 5, 7."""
        sol = solution.Solution()
        self.assertFalse(sol.isPrime(1))
        self.assertFalse(sol.isPrime(4))
        self.assertFalse(sol.isPrime(6))

    def test_example_1(self):
        """Example 1: Input: n = 10 Output: 4 Explanation: There are 4 prime numbers less than 10, they are 2, 3, 5, 7."""
        sol = solution.Solution()
        n = 10
        self.assertEqual(4, sol.countPrimes(n))

    def test_example_2(self):
        """Example 2: Input: n = 0 Output: 0"""
        sol = solution.Solution()
        n = 0
        self.assertEqual(0, sol.countPrimes(n))

    def test_example_3(self):
        """Example 3: Input: n = 1 Output: 0"""
        sol = solution.Solution()
        n = 1
        self.assertEqual(0, sol.countPrimes(n))


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

Class description:
Unit tests for the Controller class

Method signatures and docstrings:
- def setUp(self): setUp method for Controller tests
- def test_tick(self): Test for the tick function

Implement the Python class `ControllerTests` described below.

Class description:
Unit tests for the Controller class

Method signatures and docstrings:
- def setUp(self): setUp method for Controller tests
- def test_tick(self): Test for the tick function

<|skeleton|>
class ControllerTests:
    """Unit tests for the Controller class"""

    def setUp(self):
        """setUp method for Controller tests"""
        <|body_0|>

    def test_tick(self):
        """Test for the tick function"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.pump = Pump('127.0.0.1', 8000)
        self.sensor = Sensor('127.0.0.1', 8000)
        self.decider = Decider(100, 0.05)
        self.controller = Controller(self.sensor, self.pump, self.decider)
<|end_body_0|>

<|body_start_1|>
        self.sensor.measure = MagicMock(return_value=90)
        self.pump.get_state = MagicMock(return_value=Pump.PUMP_OFF)
        self.decider.decide = MagicMock(return_value=Pump.PUMP_IN)
        self.pump.set_state = MagicMock(return_value=True)
        self.controller.tick()
        self.sensor.measure.assert_called_with()
        self.pump.get_state.assert_called_with()
        self.decider.decide.assert_called_with(90, self.pump.PUMP_OFF, self.controller.actions)
        self.pump.set_state.assert_called_with(self.pump.PUMP_IN)
<|end_body_1|>

Solution:
class ControllerTests:
    """Unit tests for the Controller class"""

    def setUp(self):
        """setUp method for Controller tests"""
        self.pump = Pump('127.0.0.1', 8000)
        self.sensor = Sensor('127.0.0.1', 8000)
        self.decider = Decider(100, 0.05)
        self.controller = Controller(self.sensor, self.pump, self.decider)

    def test_tick(self):
        """Test for the tick function"""
        self.sensor.measure = MagicMock(return_value=90)
        self.pump.get_state = MagicMock(return_value=Pump.PUMP_OFF)
        self.decider.decide = MagicMock(return_value=Pump.PUMP_IN)
        self.pump.set_state = MagicMock(return_value=True)
        self.controller.tick()
        self.sensor.measure.assert_called_with()
        self.pump.get_state.assert_called_with()
        self.decider.decide.assert_called_with(90, self.pump.PUMP_OFF, self.controller.actions)
        self.pump.set_state.assert_called_with(self.pump.PUMP_IN)


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

Class description:
An offset-calculation method based on fits headers.

Method signatures and docstrings:
- def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any): Initializes new fits header offsets.
- async def __call__(self, image: Image) -> Image: Processes an image and sets x/y pixel offset to reference in offset attribute. Args: image: Image to process. Returns: Original image. Raises: ValueError: If offset could not be found.

Implement the Python class `FitsHeaderOffsets` described below.

Class description:
An offset-calculation method based on fits headers.

Method signatures and docstrings:
- def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any): Initializes new fits header offsets.
- async def __call__(self, image: Image) -> Image: Processes an image and sets x/y pixel offset to reference in offset attribute. Args: image: Image to process. Returns: Original image. Raises: ValueError: If offset could not be found.

<|skeleton|>
class FitsHeaderOffsets:
    """An offset-calculation method based on fits headers."""

    def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any):
        """Initializes new fits header offsets."""
        <|body_0|>

    async def __call__(self, image: Image) -> Image:
        """Processes an image and sets x/y pixel offset to reference in offset attribute. Args: image: Image to process. Returns: Original image. Raises: ValueError: If offset could not be found."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        Offsets.__init__(self, **kwargs)
        self.center = center
        self.target = target
<|end_body_0|>

<|body_start_1|>
        target = [image.header[x] for x in self.target]
        center = [image.header[x] for x in self.center]
        image.meta['offsets'] = np.subtract(target, center)
        return image
<|end_body_1|>

Solution:
class FitsHeaderOffsets:
    """An offset-calculation method based on fits headers."""

    def __init__(self, target: Tuple[str, str], center: Tuple[str, str]=('DET-CPX1', 'DET-CPX2'), **kwargs: Any):
        """Initializes new fits header offsets."""
        Offsets.__init__(self, **kwargs)
        self.center = center
        self.target = target

    async def __call__(self, image: Image) -> Image:
        """Processes an image and sets x/y pixel offset to reference in offset attribute. Args: image: Image to process. Returns: Original image. Raises: ValueError: If offset could not be found."""
        target = [image.header[x] for x in self.target]
        center = [image.header[x] for x in self.center]
        image.meta['offsets'] = np.subtract(target, center)
        return image