Datasets:

hybrid-diff-ar
/

stack-v2-sparse-classes-10k

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08383286f37f34f683898e2b0b196b1cc9d8de5a	[ "if len(chordProgression) < 4:\n print('ERROR IN ChordProgression 2')\n return None\nelse:\n keysForReturn = []\n tempChords = []\n for chord in chordProgression:\n tempChords.append(chord[0])\n tempChords = np.array(tempChords)\n chords = [[tempChords[0], tempChords[1]], [tempChords[2],...	<\|body_start_0\|> if len(chordProgression) < 4: print('ERROR IN ChordProgression 2') return None else: keysForReturn = [] tempChords = [] for chord in chordProgression: tempChords.append(chord[0]) tempChords = np.arra...		SubMethods	[]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class SubMethods: def cherryIntro(self, keyProgression, chordProgression): """INTROで使われているメソッド""" <\|body_0\|> def cherryB(self, keyProgression, chordProgression): """サビで使われているメソッド""" <\|body_1\|> <\|end_skeleton\|> <\|body_start_0\|> if len(chordProgression) < 4...	stack_v2_sparse_classes_10k_train_000000	12,440	no_license	[ { "docstring": "INTROで使われているメソッド", "name": "cherryIntro", "signature": "def cherryIntro(self, keyProgression, chordProgression)" }, { "docstring": "サビで使われているメソッド", "name": "cherryB", "signature": "def cherryB(self, keyProgression, chordProgression)" } ]	2	stack_v2_sparse_classes_30k_train_005988	Implement the Python class `SubMethods` described below. Class description: Implement the SubMethods class. Method signatures and docstrings: - def cherryIntro(self, keyProgression, chordProgression): INTROで使われているメソッド - def cherryB(self, keyProgression, chordProgression): サビで使われているメソッド	Implement the Python class `SubMethods` described below. Class description: Implement the SubMethods class. Method signatures and docstrings: - def cherryIntro(self, keyProgression, chordProgression): INTROで使われているメソッド - def cherryB(self, keyProgression, chordProgression): サビで使われているメソッド <\|skeleton\|> class SubMethods:...	172f486048825d989aac69945c463dd150b84a88	<\|skeleton\|> class SubMethods: def cherryIntro(self, keyProgression, chordProgression): """INTROで使われているメソッド""" <\|body_0\|> def cherryB(self, keyProgression, chordProgression): """サビで使われているメソッド""" <\|body_1\|> <\|end_skeleton\|>	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class SubMethods: def cherryIntro(self, keyProgression, chordProgression): """INTROで使われているメソッド""" if len(chordProgression) < 4: print('ERROR IN ChordProgression 2') return None else: keysForReturn = [] tempChords = [] for chord in c...	the_stack_v2_python_sparse	SongGenerator/mikakunin/Composer/ChordProgression.py	ku70t6h1k6r1/auto_music	train	0
582f5f7cc2cbdc26dc47ba28039f489fab195fb4	[ "self.output_path = output_path\nself.max_concurrent_invocations_per_instance = max_concurrent_invocations_per_instance\nself.kms_key_id = kms_key_id\nself.notification_config = notification_config\nself.failure_path = failure_path", "request_dict = {'OutputConfig': {'S3OutputPath': self.output_path, 'S3FailurePa...	<\|body_start_0\|> self.output_path = output_path self.max_concurrent_invocations_per_instance = max_concurrent_invocations_per_instance self.kms_key_id = kms_key_id self.notification_config = notification_config self.failure_path = failure_path <\|end_body_0\|> <\|body_start_1\|> ...	Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference	AsyncInferenceConfig	[ "Apache-2.0" ]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class AsyncInferenceConfig: """Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference""" def __init__(self, output_path=N...	stack_v2_sparse_classes_10k_train_000001	4,694	permissive	[ { "docstring": "Initialize an AsyncInferenceConfig object for async inference configuration. Args: output_path (str): Optional. The Amazon S3 location that endpoints upload inference responses to. If no value is provided, Amazon SageMaker will use default Amazon S3 Async Inference output path. (Default: None) m...	2	null	Implement the Python class `AsyncInferenceConfig` described below. Class description: Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference ...	Implement the Python class `AsyncInferenceConfig` described below. Class description: Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference ...	8d5d7fd8ae1a917ed3e2b988d5e533bce244fd85	<\|skeleton\|> class AsyncInferenceConfig: """Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference""" def __init__(self, output_path=N...	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class AsyncInferenceConfig: """Configuration object passed in when deploying models to Amazon SageMaker Endpoints. This object specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference""" def __init__(self, output_path=None, max_conc...	the_stack_v2_python_sparse	src/sagemaker/async_inference/async_inference_config.py	aws/sagemaker-python-sdk	train	2,050
166e01d59ab41b7a1bc0e3e1ebd2ff273e943c2d	[ "\"\"\"\n 我的想法:\n Merge graph, 然後判斷此graph的toposort 是否唯一.\n\n a digraph has a unique topological ordering if and only if there is a\n (directed edge) between each pair of consecutive vertices in the\n topological order (i.e., the digraph has a Hamiltonian path).\n\n https://...	<\|body_start_0\|> """ 我的想法: Merge graph, 然後判斷此graph的toposort 是否唯一. a digraph has a unique topological ordering if and only if there is a (directed edge) between each pair of consecutive vertices in the topological order (i.e., the d...		Solution	[]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class Solution: def sequenceReconstruction(self, org, seqs): """:type org: List[int] :type seqs: List[List[int]] :rtype: bool""" <\|body_0\|> def rewrite(self, org, seqs): """:type org: List[int] :type seqs: List[List[int]] :rtype: bool""" <\|body_1\|> <\|end_skeleton\|...	stack_v2_sparse_classes_10k_train_000002	3,721	no_license	[ { "docstring": ":type org: List[int] :type seqs: List[List[int]] :rtype: bool", "name": "sequenceReconstruction", "signature": "def sequenceReconstruction(self, org, seqs)" }, { "docstring": ":type org: List[int] :type seqs: List[List[int]] :rtype: bool", "name": "rewrite", "signature": ...	2	null	Implement the Python class `Solution` described below. Class description: Implement the Solution class. Method signatures and docstrings: - def sequenceReconstruction(self, org, seqs): :type org: List[int] :type seqs: List[List[int]] :rtype: bool - def rewrite(self, org, seqs): :type org: List[int] :type seqs: List[L...	Implement the Python class `Solution` described below. Class description: Implement the Solution class. Method signatures and docstrings: - def sequenceReconstruction(self, org, seqs): :type org: List[int] :type seqs: List[List[int]] :rtype: bool - def rewrite(self, org, seqs): :type org: List[int] :type seqs: List[L...	6350568d16b0f8c49a020f055bb6d72e2705ea56	<\|skeleton\|> class Solution: def sequenceReconstruction(self, org, seqs): """:type org: List[int] :type seqs: List[List[int]] :rtype: bool""" <\|body_0\|> def rewrite(self, org, seqs): """:type org: List[int] :type seqs: List[List[int]] :rtype: bool""" <\|body_1\|> <\|end_skeleton\|...	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class Solution: def sequenceReconstruction(self, org, seqs): """:type org: List[int] :type seqs: List[List[int]] :rtype: bool""" """ 我的想法: Merge graph, 然後判斷此graph的toposort 是否唯一. a digraph has a unique topological ordering if and only if there is a ...	the_stack_v2_python_sparse	graph/444_Sequence_Reconstruction.py	vsdrun/lc_public	train	6
bd0f1abfcf830758fb58ba5e12d93d44f79d7085	[ "super(MultiHeadedAttention, self).__init__()\nassert d_model % h == 0\nself.d_k = d_model // h\nself.h = h\nself.linears = clones(nn.Linear(d_model, d_model), 4)\nself.attn = None\nself.dropout = nn.Dropout(p=dropout)", "if mask is not None:\n mask = mask.unsqueeze(1)\nnbatches = query.size(0)\nquery, key, va...	<\|body_start_0\|> super(MultiHeadedAttention, self).__init__() assert d_model % h == 0 self.d_k = d_model // h self.h = h self.linears = clones(nn.Linear(d_model, d_model), 4) self.attn = None self.dropout = nn.Dropout(p=dropout) <\|end_body_0\|> <\|body_start_1\|> ...	Multi-headed attention block.	MultiHeadedAttention	[]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class MultiHeadedAttention: """Multi-headed attention block.""" def __init__(self, h, d_model, dropout=0.1): """:param h: number of attention heads :param d_model: input/output dimensionality :param dropout: dropout probability""" <\|body_0\|> def forward(self, query, key, value...	stack_v2_sparse_classes_10k_train_000003	21,238	no_license	[ { "docstring": ":param h: number of attention heads :param d_model: input/output dimensionality :param dropout: dropout probability", "name": "__init__", "signature": "def __init__(self, h, d_model, dropout=0.1)" }, { "docstring": "Forward pass through the multi-head attention block. :param quer...	2	null	Implement the Python class `MultiHeadedAttention` described below. Class description: Multi-headed attention block. Method signatures and docstrings: - def __init__(self, h, d_model, dropout=0.1): :param h: number of attention heads :param d_model: input/output dimensionality :param dropout: dropout probability - def...	Implement the Python class `MultiHeadedAttention` described below. Class description: Multi-headed attention block. Method signatures and docstrings: - def __init__(self, h, d_model, dropout=0.1): :param h: number of attention heads :param d_model: input/output dimensionality :param dropout: dropout probability - def...	7e55a422588c1d1e00f35a3d3a3ff896cce59e18	<\|skeleton\|> class MultiHeadedAttention: """Multi-headed attention block.""" def __init__(self, h, d_model, dropout=0.1): """:param h: number of attention heads :param d_model: input/output dimensionality :param dropout: dropout probability""" <\|body_0\|> def forward(self, query, key, value...	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class MultiHeadedAttention: """Multi-headed attention block.""" def __init__(self, h, d_model, dropout=0.1): """:param h: number of attention heads :param d_model: input/output dimensionality :param dropout: dropout probability""" super(MultiHeadedAttention, self).__init__() assert d_mo...	the_stack_v2_python_sparse	generated/test_allegro_allRank.py	jansel/pytorch-jit-paritybench	train	35
36535093f9dc5d03333aa1536ca60195e30bb2ea	[ "self._log_startup(input_dict, output_dict, exec_properties)\nexclude_splits = json_utils.loads(exec_properties.get(standard_component_specs.EXCLUDE_SPLITS_KEY, 'null')) or []\nif not isinstance(exclude_splits, list):\n raise ValueError('exclude_splits in execution properties needs to be a list. Got %s instead.'...	<\|body_start_0\|> self._log_startup(input_dict, output_dict, exec_properties) exclude_splits = json_utils.loads(exec_properties.get(standard_component_specs.EXCLUDE_SPLITS_KEY, 'null')) or [] if not isinstance(exclude_splits, list): raise ValueError('exclude_splits in execution proper...	TensorFlow ExampleValidator component executor.	Executor	[ "Apache-2.0" ]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class Executor: """TensorFlow ExampleValidator component executor.""" def Do(self, input_dict: Dict[str, List[types.Artifact]], output_dict: Dict[str, List[types.Artifact]], exec_properties: Dict[str, Any]) -> None: """TensorFlow ExampleValidator executor entrypoint. This validates statist...	stack_v2_sparse_classes_10k_train_000004	7,025	permissive	[ { "docstring": "TensorFlow ExampleValidator executor entrypoint. This validates statistics against the schema. Args: input_dict: Input dict from input key to a list of artifacts, including: - statistics: A list of type `standard_artifacts.ExampleStatistics` generated by StatisticsGen. - schema: A list of type `...	2	null	Implement the Python class `Executor` described below. Class description: TensorFlow ExampleValidator component executor. Method signatures and docstrings: - def Do(self, input_dict: Dict[str, List[types.Artifact]], output_dict: Dict[str, List[types.Artifact]], exec_properties: Dict[str, Any]) -> None: TensorFlow Exa...	Implement the Python class `Executor` described below. Class description: TensorFlow ExampleValidator component executor. Method signatures and docstrings: - def Do(self, input_dict: Dict[str, List[types.Artifact]], output_dict: Dict[str, List[types.Artifact]], exec_properties: Dict[str, Any]) -> None: TensorFlow Exa...	1b328504fa08a70388691e4072df76f143631325	<\|skeleton\|> class Executor: """TensorFlow ExampleValidator component executor.""" def Do(self, input_dict: Dict[str, List[types.Artifact]], output_dict: Dict[str, List[types.Artifact]], exec_properties: Dict[str, Any]) -> None: """TensorFlow ExampleValidator executor entrypoint. This validates statist...	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class Executor: """TensorFlow ExampleValidator component executor.""" def Do(self, input_dict: Dict[str, List[types.Artifact]], output_dict: Dict[str, List[types.Artifact]], exec_properties: Dict[str, Any]) -> None: """TensorFlow ExampleValidator executor entrypoint. This validates statistics against t...	the_stack_v2_python_sparse	tfx/components/example_validator/executor.py	tensorflow/tfx	train	2,116
b70e73edb101e6303b655e31f58aa1ebc22cac70	[ "super(Decoder, self).__init__(parameters)\nself.num_layers = num_layers\nself.layer_list = add_conv_block(self.Conv, self.BatchNorm, in_channels=anatomy_factors, out_channels=self.base_filters)\nfor _ in range(self.num_layers - 2):\n self.layer_list += add_conv_block(self.Conv, self.BatchNorm, in_channels=self....	<\|body_start_0\|> super(Decoder, self).__init__(parameters) self.num_layers = num_layers self.layer_list = add_conv_block(self.Conv, self.BatchNorm, in_channels=anatomy_factors, out_channels=self.base_filters) for _ in range(self.num_layers - 2): self.layer_list += add_conv_bl...		Decoder	[ "Apache-2.0", "LicenseRef-scancode-unknown-license-reference" ]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class Decoder: def __init__(self, parameters, anatomy_factors, num_layers=5): """Decoder module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The number of anatomical factors to be considered. num_layers (int, optional): The number of laye...	stack_v2_sparse_classes_10k_train_000005	14,834	permissive	[ { "docstring": "Decoder module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The number of anatomical factors to be considered. num_layers (int, optional): The number of layers in the Decoder. Defaults to 5. Attributes: num_layers (int): The number of layer...	6	stack_v2_sparse_classes_30k_train_005006	Implement the Python class `Decoder` described below. Class description: Implement the Decoder class. Method signatures and docstrings: - def __init__(self, parameters, anatomy_factors, num_layers=5): Decoder module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): T...	Implement the Python class `Decoder` described below. Class description: Implement the Decoder class. Method signatures and docstrings: - def __init__(self, parameters, anatomy_factors, num_layers=5): Decoder module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): T...	72eb99f68205afd5f8d49a3bb6cfc08cfd467582	<\|skeleton\|> class Decoder: def __init__(self, parameters, anatomy_factors, num_layers=5): """Decoder module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The number of anatomical factors to be considered. num_layers (int, optional): The number of laye...	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class Decoder: def __init__(self, parameters, anatomy_factors, num_layers=5): """Decoder module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The number of anatomical factors to be considered. num_layers (int, optional): The number of layers in the Deco...	the_stack_v2_python_sparse	GANDLF/models/sdnet.py	mlcommons/GaNDLF	train	45
eec45e2f079cf9cee3b69e75401bc71597575f0c	[ "available_taxon_slugs: List[str] = []\nfor attr in attributes:\n available_taxon_slugs.extend(attr.field_map)\nreturn available_taxon_slugs", "if 'attributes' in values:\n attributes: List[FdqModelAttribute] = values['attributes']\n taxon_slugs = cls._get_available_attrs_taxon_slugs(attributes)\n tax...	<\|body_start_0\|> available_taxon_slugs: List[str] = [] for attr in attributes: available_taxon_slugs.extend(attr.field_map) return available_taxon_slugs <\|end_body_0\|> <\|body_start_1\|> if 'attributes' in values: attributes: List[FdqModelAttribute] = values['attri...		FdqModel	[ "MIT" ]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class FdqModel: def _get_available_attrs_taxon_slugs(cls, attributes: List[FdqModelAttribute]) -> List[str]: """Gets list of available taxon slugs for given attributes""" <\|body_0\|> def validate_unique_taxon_slugs(cls, values): """Validate that each taxon slug is used at m...	stack_v2_sparse_classes_10k_train_000006	8,280	permissive	[ { "docstring": "Gets list of available taxon slugs for given attributes", "name": "_get_available_attrs_taxon_slugs", "signature": "def _get_available_attrs_taxon_slugs(cls, attributes: List[FdqModelAttribute]) -> List[str]" }, { "docstring": "Validate that each taxon slug is used at most once i...	5	stack_v2_sparse_classes_30k_train_005494	Implement the Python class `FdqModel` described below. Class description: Implement the FdqModel class. Method signatures and docstrings: - def _get_available_attrs_taxon_slugs(cls, attributes: List[FdqModelAttribute]) -> List[str]: Gets list of available taxon slugs for given attributes - def validate_unique_taxon_s...	Implement the Python class `FdqModel` described below. Class description: Implement the FdqModel class. Method signatures and docstrings: - def _get_available_attrs_taxon_slugs(cls, attributes: List[FdqModelAttribute]) -> List[str]: Gets list of available taxon slugs for given attributes - def validate_unique_taxon_s...	210f037280793d5cb3b6d9d3e7ba3e22ca9b8bbc	<\|skeleton\|> class FdqModel: def _get_available_attrs_taxon_slugs(cls, attributes: List[FdqModelAttribute]) -> List[str]: """Gets list of available taxon slugs for given attributes""" <\|body_0\|> def validate_unique_taxon_slugs(cls, values): """Validate that each taxon slug is used at m...	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class FdqModel: def _get_available_attrs_taxon_slugs(cls, attributes: List[FdqModelAttribute]) -> List[str]: """Gets list of available taxon slugs for given attributes""" available_taxon_slugs: List[str] = [] for attr in attributes: available_taxon_slugs.extend(attr.field_map) ...	the_stack_v2_python_sparse	src/panoramic/cli/husky/core/federated/model/models.py	panoramichq/panoramic-cli	train	5
3bc49a85876c37d609f9dcebfa908b298719650a	[ "self.capacity = capacity\nself.dict = OrderedDict()\nself.curr_len = 0", "try:\n val = self.dict[key]\n del self.dict[key]\n self.dict[key] = val\n return val\nexcept KeyError:\n return -1", "try:\n del self.dict[key]\n self.dict[key] = value\nexcept KeyError:\n if self.curr_len == self...	<\|body_start_0\|> self.capacity = capacity self.dict = OrderedDict() self.curr_len = 0 <\|end_body_0\|> <\|body_start_1\|> try: val = self.dict[key] del self.dict[key] self.dict[key] = val return val except KeyError: return ...	Implement with OrderedDict	LRUCache1	[]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class LRUCache1: """Implement with OrderedDict""" def __init__(self, capacity): """:type capacity: int""" <\|body_0\|> def get(self, key): """:rtype: int""" <\|body_1\|> def set(self, key, value): """:type key: int :type value: int :rtype: nothing""" ...	stack_v2_sparse_classes_10k_train_000007	3,068	no_license	[ { "docstring": ":type capacity: int", "name": "__init__", "signature": "def __init__(self, capacity)" }, { "docstring": ":rtype: int", "name": "get", "signature": "def get(self, key)" }, { "docstring": ":type key: int :type value: int :rtype: nothing", "name": "set", "sig...	3	null	Implement the Python class `LRUCache1` described below. Class description: Implement with OrderedDict Method signatures and docstrings: - def __init__(self, capacity): :type capacity: int - def get(self, key): :rtype: int - def set(self, key, value): :type key: int :type value: int :rtype: nothing	Implement the Python class `LRUCache1` described below. Class description: Implement with OrderedDict Method signatures and docstrings: - def __init__(self, capacity): :type capacity: int - def get(self, key): :rtype: int - def set(self, key, value): :type key: int :type value: int :rtype: nothing <\|skeleton\|> class...	a64bca9c07a7be8d4060c4b96e89d8d429a7f1a3	<\|skeleton\|> class LRUCache1: """Implement with OrderedDict""" def __init__(self, capacity): """:type capacity: int""" <\|body_0\|> def get(self, key): """:rtype: int""" <\|body_1\|> def set(self, key, value): """:type key: int :type value: int :rtype: nothing""" ...	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class LRUCache1: """Implement with OrderedDict""" def __init__(self, capacity): """:type capacity: int""" self.capacity = capacity self.dict = OrderedDict() self.curr_len = 0 def get(self, key): """:rtype: int""" try: val = self.dict[key] ...	the_stack_v2_python_sparse	Company Interview/SC/LRU.py	geniousisme/CodingInterview	train	0
a5bec19a18ad7ebeda6e191272e9ba4e471ce6d9	[ "if not root:\n return ''\nres = []\nq = collections.deque([root])\nwhile q:\n node = q.popleft()\n if node:\n res.append(str(node.val))\n q.append(node.left)\n q.append(node.right)\n else:\n res.append(str(-1))\nreturn ','.join(res)", "if not data:\n return None\ndata_q...	<\|body_start_0\|> if not root: return '' res = [] q = collections.deque([root]) while q: node = q.popleft() if node: res.append(str(node.val)) q.append(node.left) q.append(node.right) else: ...		Codec	[]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class Codec: def serialize(self, root: Optional[TreeNode]) -> str: """Encodes a tree to a single string.""" <\|body_0\|> def deserialize(self, data: str) -> Optional[TreeNode]: """Decodes your encoded data to tree.""" <\|body_1\|> <\|end_skeleton\|> <\|body_start_0\|> ...	stack_v2_sparse_classes_10k_train_000008	1,442	no_license	[ { "docstring": "Encodes a tree to a single string.", "name": "serialize", "signature": "def serialize(self, root: Optional[TreeNode]) -> str" }, { "docstring": "Decodes your encoded data to tree.", "name": "deserialize", "signature": "def deserialize(self, data: str) -> Optional[TreeNode...	2	stack_v2_sparse_classes_30k_train_006182	Implement the Python class `Codec` described below. Class description: Implement the Codec class. Method signatures and docstrings: - def serialize(self, root: Optional[TreeNode]) -> str: Encodes a tree to a single string. - def deserialize(self, data: str) -> Optional[TreeNode]: Decodes your encoded data to tree.	Implement the Python class `Codec` described below. Class description: Implement the Codec class. Method signatures and docstrings: - def serialize(self, root: Optional[TreeNode]) -> str: Encodes a tree to a single string. - def deserialize(self, data: str) -> Optional[TreeNode]: Decodes your encoded data to tree. <...	c7a42753b2b16c7b9c66b8d7c2e67b683a15e27d	<\|skeleton\|> class Codec: def serialize(self, root: Optional[TreeNode]) -> str: """Encodes a tree to a single string.""" <\|body_0\|> def deserialize(self, data: str) -> Optional[TreeNode]: """Decodes your encoded data to tree.""" <\|body_1\|> <\|end_skeleton\|>	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class Codec: def serialize(self, root: Optional[TreeNode]) -> str: """Encodes a tree to a single string.""" if not root: return '' res = [] q = collections.deque([root]) while q: node = q.popleft() if node: res.append(str(no...	the_stack_v2_python_sparse	medium/449.py	brandoneng000/LeetCode	train	0
26aff93bc0df9aa22e1b2e111b25105004d5a7c8	[ "self._DebugPrintValue('Unknown1', f'0x{user_assist_entry.unknown1:08x}')\nself._DebugPrintDecimalValue('Number of executions', user_assist_entry.number_of_executions)\nif format_version == 5:\n self._DebugPrintDecimalValue('Application focus count', user_assist_entry.application_focus_count)\n self._DebugPri...	<\|body_start_0\|> self._DebugPrintValue('Unknown1', f'0x{user_assist_entry.unknown1:08x}') self._DebugPrintDecimalValue('Number of executions', user_assist_entry.number_of_executions) if format_version == 5: self._DebugPrintDecimalValue('Application focus count', user_assist_entry.app...	UserAssist data parser.	UserAssistDataParser	[ "Apache-2.0" ]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class UserAssistDataParser: """UserAssist data parser.""" def _DebugPrintEntry(self, format_version, user_assist_entry): """Prints UserAssist entry value debug information. Args: format_version (int): format version. user_assist_entry (user_assist_entry_v3\|user_assist_entry_v5): UserAssist...	stack_v2_sparse_classes_10k_train_000009	7,377	permissive	[ { "docstring": "Prints UserAssist entry value debug information. Args: format_version (int): format version. user_assist_entry (user_assist_entry_v3\|user_assist_entry_v5): UserAssist entry.", "name": "_DebugPrintEntry", "signature": "def _DebugPrintEntry(self, format_version, user_assist_entry)" }, ...	2	stack_v2_sparse_classes_30k_train_006023	Implement the Python class `UserAssistDataParser` described below. Class description: UserAssist data parser. Method signatures and docstrings: - def _DebugPrintEntry(self, format_version, user_assist_entry): Prints UserAssist entry value debug information. Args: format_version (int): format version. user_assist_entr...	Implement the Python class `UserAssistDataParser` described below. Class description: UserAssist data parser. Method signatures and docstrings: - def _DebugPrintEntry(self, format_version, user_assist_entry): Prints UserAssist entry value debug information. Args: format_version (int): format version. user_assist_entr...	d149aff1b8ff97e1cc8d7416fc583b964bad4ccd	<\|skeleton\|> class UserAssistDataParser: """UserAssist data parser.""" def _DebugPrintEntry(self, format_version, user_assist_entry): """Prints UserAssist entry value debug information. Args: format_version (int): format version. user_assist_entry (user_assist_entry_v3\|user_assist_entry_v5): UserAssist...	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class UserAssistDataParser: """UserAssist data parser.""" def _DebugPrintEntry(self, format_version, user_assist_entry): """Prints UserAssist entry value debug information. Args: format_version (int): format version. user_assist_entry (user_assist_entry_v3\|user_assist_entry_v5): UserAssist entry.""" ...	the_stack_v2_python_sparse	winregrc/userassist.py	libyal/winreg-kb	train	129
5bdbf11c4cfcb9a0185228801e2ea77cc24271a0	[ "self.directions = self._listify_input(input_string.lower())\nself.steps = [0, 0, 0, 0]\nself.facing = 0\nself.locations = [(0, 0)]\nself.new_loc = (0, 0)", "stripped_string = re.sub('\\\\s+', '', input_string.strip())\nsplit_list = stripped_string.split(',')\nreturn [(x[0], int(x[1:])) for x in split_list]", "...	<\|body_start_0\|> self.directions = self._listify_input(input_string.lower()) self.steps = [0, 0, 0, 0] self.facing = 0 self.locations = [(0, 0)] self.new_loc = (0, 0) <\|end_body_0\|> <\|body_start_1\|> stripped_string = re.sub('\\s+', '', input_string.strip()) split...	Class for turning walking directions into distance from start.	Walker	[ "MIT" ]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class Walker: """Class for turning walking directions into distance from start.""" def __init__(self, input_string): """Initialize.""" <\|body_0\|> def _listify_input(self, input_string): """Turn a string of inputs into a list.""" <\|body_1\|> def make_rotatio...	stack_v2_sparse_classes_10k_train_000010	2,294	permissive	[ { "docstring": "Initialize.", "name": "__init__", "signature": "def __init__(self, input_string)" }, { "docstring": "Turn a string of inputs into a list.", "name": "_listify_input", "signature": "def _listify_input(self, input_string)" }, { "docstring": "Turn left or right, and u...	6	stack_v2_sparse_classes_30k_train_003435	Implement the Python class `Walker` described below. Class description: Class for turning walking directions into distance from start. Method signatures and docstrings: - def __init__(self, input_string): Initialize. - def _listify_input(self, input_string): Turn a string of inputs into a list. - def make_rotation(se...	Implement the Python class `Walker` described below. Class description: Class for turning walking directions into distance from start. Method signatures and docstrings: - def __init__(self, input_string): Initialize. - def _listify_input(self, input_string): Turn a string of inputs into a list. - def make_rotation(se...	17c729af2af5f1d95ba6ff68771a82ca6d00b05d	<\|skeleton\|> class Walker: """Class for turning walking directions into distance from start.""" def __init__(self, input_string): """Initialize.""" <\|body_0\|> def _listify_input(self, input_string): """Turn a string of inputs into a list.""" <\|body_1\|> def make_rotatio...	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class Walker: """Class for turning walking directions into distance from start.""" def __init__(self, input_string): """Initialize.""" self.directions = self._listify_input(input_string.lower()) self.steps = [0, 0, 0, 0] self.facing = 0 self.locations = [(0, 0)] ...	the_stack_v2_python_sparse	2016/day01_no_time_for_a_taxicab/python/src/part2.py	tlake/advent-of-code	train	0
5460e94ca69e81da3dfbe356fc9545f03baab185	[ "if target not in nums:\n return -1\nreturn nums.index(target)", "left = 0\nright = len(nums) - 1\nif not nums:\n return -1\nwhile left + 1 < right:\n mid = (left + right) // 2\n if nums[mid] >= nums[left]:\n if nums[left] <= target <= nums[mid]:\n right = mid\n else:\n ...	<\|body_start_0\|> if target not in nums: return -1 return nums.index(target) <\|end_body_0\|> <\|body_start_1\|> left = 0 right = len(nums) - 1 if not nums: return -1 while left + 1 < right: mid = (left + right) // 2 if nums[mid...		Solution	[]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class Solution: def search(self, nums, target): """:type nums: List[int] :type target: int :rtype: int""" <\|body_0\|> def search_binary(self, nums, target): """:type nums: List[int] :type target: int :rtype: int""" <\|body_1\|> <\|end_skeleton\|> <\|body_start_0\|> ...	stack_v2_sparse_classes_10k_train_000011	2,370	no_license	[ { "docstring": ":type nums: List[int] :type target: int :rtype: int", "name": "search", "signature": "def search(self, nums, target)" }, { "docstring": ":type nums: List[int] :type target: int :rtype: int", "name": "search_binary", "signature": "def search_binary(self, nums, target)" }...	2	null	Implement the Python class `Solution` described below. Class description: Implement the Solution class. Method signatures and docstrings: - def search(self, nums, target): :type nums: List[int] :type target: int :rtype: int - def search_binary(self, nums, target): :type nums: List[int] :type target: int :rtype: int	Implement the Python class `Solution` described below. Class description: Implement the Solution class. Method signatures and docstrings: - def search(self, nums, target): :type nums: List[int] :type target: int :rtype: int - def search_binary(self, nums, target): :type nums: List[int] :type target: int :rtype: int ...	2d5fa4cd696d5035ea8859befeadc5cc436959c9	<\|skeleton\|> class Solution: def search(self, nums, target): """:type nums: List[int] :type target: int :rtype: int""" <\|body_0\|> def search_binary(self, nums, target): """:type nums: List[int] :type target: int :rtype: int""" <\|body_1\|> <\|end_skeleton\|>	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class Solution: def search(self, nums, target): """:type nums: List[int] :type target: int :rtype: int""" if target not in nums: return -1 return nums.index(target) def search_binary(self, nums, target): """:type nums: List[int] :type target: int :rtype: int""" ...	the_stack_v2_python_sparse	SourceCode/Python/Problem/00033.Search in Rotated Sorted Array.py	roger6blog/LeetCode	train	0
6744895894e45ee7455520b2fbc0baa617c56ff9	[ "if root is None:\n return ''\nreturn f'{root.val},{self.serialize(root.left)}{self.serialize(root.right)}'", "def insert(node, val):\n if node is None:\n return TreeNode(val)\n if val < node.val:\n node.left = insert(node.left, val)\n else:\n node.right = insert(node.right, val)\...	<\|body_start_0\|> if root is None: return '' return f'{root.val},{self.serialize(root.left)}{self.serialize(root.right)}' <\|end_body_0\|> <\|body_start_1\|> def insert(node, val): if node is None: return TreeNode(val) if val < node.val: ...		Codec	[]	stack_v2_sparse_python_classes_v1	<\|skeleton\|> class Codec: def serialize(self, root: Optional[TreeNode]) -> str: """Encodes a tree to a single string.""" <\|body_0\|> def deserialize(self, data: str) -> Optional[TreeNode]: """Decodes your encoded data to tree.""" <\|body_1\|> <\|end_skeleton\|> <\|body_start_0\|> ...	stack_v2_sparse_classes_10k_train_000012	2,004	no_license	[ { "docstring": "Encodes a tree to a single string.", "name": "serialize", "signature": "def serialize(self, root: Optional[TreeNode]) -> str" }, { "docstring": "Decodes your encoded data to tree.", "name": "deserialize", "signature": "def deserialize(self, data: str) -> Optional[TreeNode...	2	null	Implement the Python class `Codec` described below. Class description: Implement the Codec class. Method signatures and docstrings: - def serialize(self, root: Optional[TreeNode]) -> str: Encodes a tree to a single string. - def deserialize(self, data: str) -> Optional[TreeNode]: Decodes your encoded data to tree.	Implement the Python class `Codec` described below. Class description: Implement the Codec class. Method signatures and docstrings: - def serialize(self, root: Optional[TreeNode]) -> str: Encodes a tree to a single string. - def deserialize(self, data: str) -> Optional[TreeNode]: Decodes your encoded data to tree. <...	157cbaeeff74130e5105e58a6b4cdf66403a8a6f	<\|skeleton\|> class Codec: def serialize(self, root: Optional[TreeNode]) -> str: """Encodes a tree to a single string.""" <\|body_0\|> def deserialize(self, data: str) -> Optional[TreeNode]: """Decodes your encoded data to tree.""" <\|body_1\|> <\|end_skeleton\|>	stack_v2_sparse_classes_10k	data/stack_v2_sparse_classes_30k	class Codec: def serialize(self, root: Optional[TreeNode]) -> str: """Encodes a tree to a single string.""" if root is None: return '' return f'{root.val},{self.serialize(root.left)}{self.serialize(root.right)}' def deserialize(self, data: str) -> Optional[TreeNode]: ...	the_stack_v2_python_sparse	Leetcode/449. Serialize and Deserialize BST.py	xiaohuanlin/Algorithms	train	1

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YAML Metadata Warning:The task_categories "code-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Stack v2 Sparse Python Classes 10k

This is a 10,000-sample snapshot for Diffusion + Autoregressive hybrid code generation experiments.

Source

The data is extracted from bigcode/the-stack-v2-dedup, Python subset. The extraction uses Stack v2 metadata as source of truth, groups candidates by repo_name + revision_id, fetches files with git partial fetch + sparse checkout, then applies AST-level class filters.

Splits

train.jsonl: 9,000
val.jsonl: 500
test.jsonl: 500
all.jsonl: 10,000

Record Format

Each JSONL row is one Python class sample. Important fields include:

prompt: natural-language class implementation prompt
skeleton: class/method signatures and docstrings with <|body_i|> slots
bodies: list of method bodies without docstrings
bodies_text: body slots wrapped by <|body_start_i|> and <|end_body_i|>
full_text: skeleton plus body slots
solution: reconstructed class code
source_repo, source_path, revision_id, blob_id, detected_licenses: source metadata

Filters

2 to 6 methods per class
every method has a non-empty docstring
every method body has 3 to 30 non-empty lines
reconstructed class parses as Python AST
tests/docs/examples/vendor/generated files are excluded by metadata/path filters
simple ClassEval/HumanEval contamination filters are applied

Strict pyflakes is not used as a hard filter because isolated extracted classes often depend on module-level imports, constants, parent classes, or helper functions.

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