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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.contacts.Contact
from pydantic import BaseModel, Field from typing import Any, Dict, List, Optional class Contact(BaseModel): """Model for a Nextcloud contact.""" uid: str = Field(description='Contact UID') fn: str = Field(description='Full name (formatted name)') given_name: Optional[str] = Field(None, description='Gi...
class Contact(BaseModel): '''Model for a Nextcloud contact.''' @property def primary_email(self) -> Optional[str]: '''Get the primary email address.''' pass @property def primary_phone(self) -> Optional[str]: '''Get the primary phone number.''' pass
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nextcloud_mcp_server.models.contacts.ContactField
from pydantic import BaseModel, Field from typing import Any, Dict, List, Optional class ContactField(BaseModel): """Model for a contact field (email, phone, etc.).""" type: str = Field(description="Field type (e.g., 'email', 'phone', 'address')") value: str = Field(description='Field value') label: Op...
class ContactField(BaseModel): '''Model for a contact field (email, phone, etc.).''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.contacts.CreateAddressBookResponse
from .base import BaseResponse, StatusResponse from pydantic import BaseModel, Field class CreateAddressBookResponse(BaseResponse): """Response model for address book creation.""" addressbook: AddressBook = Field(description='The created address book')
class CreateAddressBookResponse(BaseResponse): '''Response model for address book creation.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.contacts.CreateContactResponse
from .base import BaseResponse, StatusResponse from pydantic import BaseModel, Field class CreateContactResponse(BaseResponse): """Response model for contact creation.""" contact: Contact = Field(description='The created contact') addressbook: str = Field(description='Address book the contact was created i...
class CreateContactResponse(BaseResponse): '''Response model for contact creation.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.contacts.DeleteAddressBookResponse
from pydantic import BaseModel, Field from .base import BaseResponse, StatusResponse class DeleteAddressBookResponse(StatusResponse): """Response model for address book deletion.""" deleted_name: str = Field(description='Name of the deleted address book')
class DeleteAddressBookResponse(StatusResponse): '''Response model for address book deletion.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.contacts.DeleteContactResponse
from pydantic import BaseModel, Field from .base import BaseResponse, StatusResponse class DeleteContactResponse(StatusResponse): """Response model for contact deletion.""" deleted_uid: str = Field(description='UID of the deleted contact') addressbook: str = Field(description='Address book the contact was ...
class DeleteContactResponse(StatusResponse): '''Response model for contact deletion.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.contacts.ListAddressBooksResponse
from .base import BaseResponse, StatusResponse from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field class ListAddressBooksResponse(BaseResponse): """Response model for listing address books.""" addressbooks: List[AddressBook] = Field(description='List of available address books') ...
class ListAddressBooksResponse(BaseResponse): '''Response model for listing address books.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.contacts.ListContactsResponse
from .base import BaseResponse, StatusResponse from pydantic import BaseModel, Field from typing import Any, Dict, List, Optional class ListContactsResponse(BaseResponse): """Response model for listing contacts.""" contacts: List[Contact] = Field(description='List of contacts') addressbook: str = Field(des...
class ListContactsResponse(BaseResponse): '''Response model for listing contacts.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.contacts.UpdateContactResponse
from .base import BaseResponse, StatusResponse from pydantic import BaseModel, Field class UpdateContactResponse(BaseResponse): """Response model for contact updates.""" contact: Contact = Field(description='The updated contact') addressbook: str = Field(description='Address book the contact belongs to')
class UpdateContactResponse(BaseResponse): '''Response model for contact updates.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.notes.AppendContentResponse
from pydantic import BaseModel, Field from .base import BaseResponse, IdResponse, StatusResponse class AppendContentResponse(BaseResponse): """Response model for appending content to a note.""" id: int = Field(description='The updated note ID') title: str = Field(description='The updated note title') c...
class AppendContentResponse(BaseResponse): '''Response model for appending content to a note.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.notes.CreateNoteResponse
from pydantic import BaseModel, Field from .base import BaseResponse, IdResponse, StatusResponse class CreateNoteResponse(IdResponse): """Response model for note creation.""" title: str = Field(description='The created note title') category: str = Field(description='The created note category') etag: st...
class CreateNoteResponse(IdResponse): '''Response model for note creation.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.notes.DeleteNoteResponse
from .base import BaseResponse, IdResponse, StatusResponse from pydantic import BaseModel, Field class DeleteNoteResponse(StatusResponse): """Response model for note deletion.""" deleted_id: int = Field(description='ID of the deleted note')
class DeleteNoteResponse(StatusResponse): '''Response model for note deletion.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.notes.Note
from datetime import datetime from pydantic import BaseModel, Field class Note(BaseModel): """Model for a Nextcloud note.""" id: int = Field(description='Note ID') title: str = Field(description='Note title') content: str = Field(description='Note content in markdown') category: str = Field(default...
class Note(BaseModel): '''Model for a Nextcloud note.''' @property def modified_datetime(self) -> datetime: '''Convert Unix timestamp to datetime.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.notes.NoteSearchResult
from pydantic import BaseModel, Field from typing import List, Optional class NoteSearchResult(BaseModel): """Model for note search results (limited fields).""" id: int = Field(description='Note ID') title: str = Field(description='Note title') category: str = Field(default='', description='Note catego...
class NoteSearchResult(BaseModel): '''Model for note search results (limited fields).''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.notes.NotesSettings
from pydantic import BaseModel, Field class NotesSettings(BaseModel): """Model for Notes app settings.""" notesPath: str = Field(description='Path to notes directory') fileSuffix: str = Field(description='File suffix for notes') noteMode: str = Field(description='Note mode setting')
class NotesSettings(BaseModel): '''Model for Notes app settings.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.notes.SearchNotesResponse
from .base import BaseResponse, IdResponse, StatusResponse from pydantic import BaseModel, Field from typing import List, Optional class SearchNotesResponse(BaseResponse): """Response model for note search.""" results: List[NoteSearchResult] = Field(description='Search results') query: str = Field(descript...
class SearchNotesResponse(BaseResponse): '''Response model for note search.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.notes.UpdateNoteResponse
from pydantic import BaseModel, Field from .base import BaseResponse, IdResponse, StatusResponse class UpdateNoteResponse(BaseResponse): """Response model for note updates.""" id: int = Field(description='The updated note ID') title: str = Field(description='The updated note title') category: str = Fie...
class UpdateNoteResponse(BaseResponse): '''Response model for note updates.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.tables.CreateRowResponse
from .base import BaseResponse, IdResponse, StatusResponse from pydantic import BaseModel, Field class CreateRowResponse(IdResponse): """Response model for row creation.""" row: TableRow = Field(description='The created row') table_id: int = Field(description='Table ID the row was created in')
class CreateRowResponse(IdResponse): '''Response model for row creation.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.tables.DeleteRowResponse
from .base import BaseResponse, IdResponse, StatusResponse from pydantic import BaseModel, Field class DeleteRowResponse(StatusResponse): """Response model for row deletion.""" deleted_id: int = Field(description='ID of the deleted row')
class DeleteRowResponse(StatusResponse): '''Response model for row deletion.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.tables.GetSchemaResponse
from pydantic import BaseModel, Field from .base import BaseResponse, IdResponse, StatusResponse class GetSchemaResponse(BaseResponse): """Response model for getting table schema.""" table_schema: TableSchema = Field(description='Table schema information')
class GetSchemaResponse(BaseResponse): '''Response model for getting table schema.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.tables.ListTablesResponse
from pydantic import BaseModel, Field from .base import BaseResponse, IdResponse, StatusResponse from typing import Any, Dict, List, Optional class ListTablesResponse(BaseResponse): """Response model for listing tables.""" tables: List[Table] = Field(description='List of available tables') total_count: int...
class ListTablesResponse(BaseResponse): '''Response model for listing tables.''' pass
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cbcoutinho/nextcloud-mcp-server
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nextcloud_mcp_server.models.tables.ReadTableResponse
from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field from .base import BaseResponse, IdResponse, StatusResponse class ReadTableResponse(BaseResponse): """Response model for reading table rows.""" rows: List[TableRow] = Field(description='Table rows') table_id: int = Field(desc...
class ReadTableResponse(BaseResponse): '''Response model for reading table rows.''' pass
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nextcloud_mcp_server.models.tables.Table
from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field class Table(BaseModel): """Model for a Nextcloud table.""" id: int = Field(description='Table ID') title: str = Field(description='Table title') emoji: Optional[str] = Field(None, description='Table emoji') ownership...
class Table(BaseModel): '''Model for a Nextcloud table.''' pass
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nextcloud_mcp_server.models.tables.TableColumn
from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field class TableColumn(BaseModel): """Model for a table column definition.""" id: int = Field(description='Column ID') title: str = Field(description='Column title') type: str = Field(description='Column type (text, number, d...
class TableColumn(BaseModel): '''Model for a table column definition.''' pass
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nextcloud_mcp_server.models.tables.TableRow
from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field class TableRow(BaseModel): """Model for a table row.""" id: int = Field(description='Row ID') created_by: Optional[str] = Field(None, description='User who created the row') created_at: Optional[str] = Field(None, descri...
class TableRow(BaseModel): '''Model for a table row.''' pass
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nextcloud_mcp_server.models.tables.TableSchema
from pydantic import BaseModel, Field from typing import Any, Dict, List, Optional class TableSchema(BaseModel): """Model for complete table schema including columns and views.""" table: Table = Field(description='Table information') columns: List[TableColumn] = Field(description='Table columns') views...
class TableSchema(BaseModel): '''Model for complete table schema including columns and views.''' pass
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nextcloud_mcp_server.models.tables.TableView
from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field class TableView(BaseModel): """Model for a table view.""" id: int = Field(description='View ID') title: str = Field(description='View title') emoji: Optional[str] = Field(None, description='View emoji') description: ...
class TableView(BaseModel): '''Model for a table view.''' pass
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cbcoutinho/nextcloud-mcp-server
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cbcoutinho_nextcloud-mcp-server/nextcloud_mcp_server/models/tables.py
nextcloud_mcp_server.models.tables.UpdateRowResponse
from .base import BaseResponse, IdResponse, StatusResponse from pydantic import BaseModel, Field class UpdateRowResponse(BaseResponse): """Response model for row updates.""" row: TableRow = Field(description='The updated row')
class UpdateRowResponse(BaseResponse): '''Response model for row updates.''' pass
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cbcoutinho/nextcloud-mcp-server
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cbcoutinho_nextcloud-mcp-server/nextcloud_mcp_server/models/webdav.py
nextcloud_mcp_server.models.webdav.CreateDirectoryResponse
from .base import BaseResponse, StatusResponse from pydantic import BaseModel, Field class CreateDirectoryResponse(StatusResponse): """Response model for directory creation.""" path: str = Field(description='Directory path that was created') created: bool = Field(description='Whether directory was created ...
class CreateDirectoryResponse(StatusResponse): '''Response model for directory creation.''' pass
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cbcoutinho/nextcloud-mcp-server
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cbcoutinho_nextcloud-mcp-server/nextcloud_mcp_server/models/webdav.py
nextcloud_mcp_server.models.webdav.DeleteResourceResponse
from typing import List, Optional from .base import BaseResponse, StatusResponse from pydantic import BaseModel, Field class DeleteResourceResponse(StatusResponse): """Response model for resource deletion.""" path: str = Field(description='Path that was deleted') was_directory: bool = Field(description='Wh...
class DeleteResourceResponse(StatusResponse): '''Response model for resource deletion.''' pass
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cbcoutinho/nextcloud-mcp-server
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cbcoutinho_nextcloud-mcp-server/nextcloud_mcp_server/models/webdav.py
nextcloud_mcp_server.models.webdav.DirectoryListing
from typing import List, Optional from .base import BaseResponse, StatusResponse from pydantic import BaseModel, Field class DirectoryListing(BaseResponse): """Response model for directory listings.""" path: str = Field(description='Directory path') items: List[FileInfo] = Field(description='Files and dire...
class DirectoryListing(BaseResponse): '''Response model for directory listings.''' pass
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cbcoutinho/nextcloud-mcp-server
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cbcoutinho_nextcloud-mcp-server/nextcloud_mcp_server/models/webdav.py
nextcloud_mcp_server.models.webdav.FileInfo
from pydantic import BaseModel, Field from datetime import datetime from typing import List, Optional class FileInfo(BaseModel): """Model for file/directory information.""" name: str = Field(description='File/directory name') path: str = Field(description='Full path') is_directory: bool = Field(descrip...
class FileInfo(BaseModel): '''Model for file/directory information.''' @property def last_modified_datetime(self) -> Optional[datetime]: '''Convert last modified string to datetime.''' pass
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cbcoutinho/nextcloud-mcp-server
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cbcoutinho_nextcloud-mcp-server/nextcloud_mcp_server/models/webdav.py
nextcloud_mcp_server.models.webdav.ReadFileResponse
from .base import BaseResponse, StatusResponse from typing import List, Optional from pydantic import BaseModel, Field class ReadFileResponse(BaseResponse): """Response model for reading file contents.""" path: str = Field(description='File path') content: str = Field(description='File content (text or bas...
class ReadFileResponse(BaseResponse): '''Response model for reading file contents.''' pass
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cbcoutinho/nextcloud-mcp-server
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/cbcoutinho_nextcloud-mcp-server/nextcloud_mcp_server/models/webdav.py
nextcloud_mcp_server.models.webdav.WriteFileResponse
from .base import BaseResponse, StatusResponse from pydantic import BaseModel, Field from typing import List, Optional class WriteFileResponse(StatusResponse): """Response model for writing files.""" path: str = Field(description='File path that was written') size: Optional[int] = Field(None, description='...
class WriteFileResponse(StatusResponse): '''Response model for writing files.''' pass
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.Attr
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting import onnx_ir from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from typing import Any, Callable, Generic, NamedTuple, SupportsInt, Union i...
class Attr(_protocols.AttributeProtocol, _protocols.ReferenceAttributeProtocol, _display.PrettyPrintable): '''Base class for ONNX attributes or references.''' def __init__(self, name: str, type: _enums.AttributeType, value: Any, ref_attr_name: str | None=None, *, doc_string: str | None=None) -> None: ...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.ExternalTensor
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting import os import onnx_ir import mmap from typing import Any, Callable, Generic, NamedTuple, SupportsInt, Union import numpy as np class ExternalTensor(TensorBase, _protocols.TensorProtocol): ...
class ExternalTensor(TensorBase, _protocols.TensorProtocol): '''An immutable concrete tensor with its data store on disk. This class uses memory mapping to avoid loading the tensor into memory, when the data type is supported by numpy. Otherwise, the tensor is loaded into memory lazily when accessed. ...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.Function
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting import onnx_ir from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence import typing import textwrap class Function(_protocols.FunctionProtocol, ...
class Function(_protocols.FunctionProtocol, Sequence[Node], _display.PrettyPrintable): '''IR functions. Like a graph, a function can have nodes that are not topologically sorted. It is the responsibility of the user to maintain a topological order of the nodes. Note that there is not a ``node`` attribu...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.Graph
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting import onnx_ir from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence import typing from collections.abc import Set as AbstractSet import heapq ...
class Graph(_protocols.GraphProtocol, Sequence[Node], _display.PrettyPrintable): '''IR Graph. Graph represents a computation graph. In addition to the ONNX specification specified fields, it also contains a mapping of :attr:`opset_imports`. This allows different subgraphs to import different opsets. It...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.GraphView
import typing from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting class GraphView(Sequence[Node], _display.PrettyPrintable): """A read-on...
class GraphView(Sequence[Node], _display.PrettyPrintable): '''A read-only view on a graph. The GraphView is useful for analysis of a subgraph. It can be initialized with a subset of nodes from a :class:`Graph`. Creating GraphView does not change the ownership of the nodes, and so it is possible to crea...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.LazyTensor
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting from typing import Any, Callable, Generic, NamedTuple, SupportsInt, Union import numpy as np class LazyTensor(TensorBase, _protocols.TensorProtocol): """A tensor that lazily evaluates a func...
class LazyTensor(TensorBase, _protocols.TensorProtocol): '''A tensor that lazily evaluates a function to get the actual tensor. This class takes a function returning an `ir.TensorProtocol`, a dtype, and a shape argument. The function is lazily evaluated to get the actual tensor when `tobytes()` or `numpy()...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.Model
from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting import textwrap class Model(_protocols.ModelProtocol, _display.PrettyPrintable): __sl...
class Model(_protocols.ModelProtocol, _display.PrettyPrintable): def __init__(self, graph: Graph, *, ir_version: int, producer_name: str | None=None, producer_version: str | None=None, domain: str | None=None, model_version: int | None=None, doc_string: str | None=None, functions: Sequence[Function]=(), metadata_...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.Node
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from typing import Any, Callable, Generic, NamedTuple, SupportsInt, Union class Node(_pro...
class Node(_protocols.NodeProtocol, _display.PrettyPrintable): '''IR Node. .. tip:: For a more convenient way (that supports Python objects as attributes) to create a node, use the :func:`onnx_ir.node` constructor. If ``graph`` is provided, the node will be added to the graph. Otherwise, ...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.OptionalType
class OptionalType(_RecursiveTypeBase): """A type that represents an optional element."""
class OptionalType(_RecursiveTypeBase): '''A type that represents an optional element.''' pass
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.PackedTensor
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from typing import Any, Callable, Generic, NamedTuple, SupportsInt, Union import numpy as ...
class PackedTensor(TensorBase, _protocols.TensorProtocol, Generic[TArrayCompatible]): '''A tensor that stores 4bit datatypes in packed format. .. versionadded:: 0.1.2 ''' def __init__(self, value: TArrayCompatible, dtype: _enums.DataType, *, shape: Shape | Sequence[int], name: str | None=None, doc_str...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.SequenceType
class SequenceType(_RecursiveTypeBase): """A type that represents a sequence of elements."""
class SequenceType(_RecursiveTypeBase): '''A type that represents a sequence of elements.''' pass
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.Shape
import typing from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from typing import Any, Callable, Generic, NamedTuple, SupportsInt, Union from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting c...
class Shape(_protocols.ShapeProtocol, _display.PrettyPrintable): '''Represents the shape of a tensor, including its dimensions and optional denotations. The :class:`Shape` class stores the dimensions of a tensor, which can be integers, None (unknown), or symbolic dimensions. It provides methods for queryin...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.SparseTensorType
class SparseTensorType(_TensorTypeBase): """A type that represents a sparse tensor."""
class SparseTensorType(_TensorTypeBase): '''A type that represents a sparse tensor.''' pass
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.StringTensor
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from typing import Any, Callable, Generic, NamedTuple, SupportsInt, Union import numpy as ...
class StringTensor(TensorBase, _protocols.TensorProtocol): '''Multidimensional array of strings (as binary data to match the string_data field in TensorProto).''' def __init__(self, value: Sequence[bytes] | npt.NDArray[np.bytes_], *, shape: Shape | None=None, name: str | None=None, doc_string: str | None=None...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.SymbolicDim
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting class SymbolicDim(_protocols.SymbolicDimProtocol, _display.PrettyPrintable): """Immutable symbolic dimension that can be shared across multiple shapes. SymbolicDim is used to represent ...
class SymbolicDim(_protocols.SymbolicDimProtocol, _display.PrettyPrintable): '''Immutable symbolic dimension that can be shared across multiple shapes. SymbolicDim is used to represent a symbolic (non-integer) dimension in a tensor shape. It is immutable and can be compared or hashed. ''' def __in...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.Tensor
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting from typing import Any, Callable, Generic, NamedTuple, SupportsInt, Union import numpy as np class Tensor(TensorBase, _protocols.TensorProtocol, Generic[TArrayCompatible]): """An immutable c...
class Tensor(TensorBase, _protocols.TensorProtocol, Generic[TArrayCompatible]): '''An immutable concrete tensor. This class is a wrapper around the raw tensor data. The raw tensor data can be a numpy array compatible object (e.g. ``np.ndarray``, ``torch.Tensor``) or a ``DLPack`` compatible object. The ...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.TensorBase
from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting import contextlib import abc import numpy as np import math class TensorBase(abc.ABC, _protocols.TensorProtocol, _display.PrettyPrintable): """Convenience Shared methods for classes implemen...
class TensorBase(abc.ABC, _protocols.TensorProtocol, _display.PrettyPrintable): '''Convenience Shared methods for classes implementing TensorProtocol.''' def __init__(self, name: str | None=None, doc_string: str | None=None, metadata_props: dict[str, str] | None=None) -> None: pass def _printable...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.TensorType
class TensorType(_TensorTypeBase): """A type that represents a tensor.""" def __str__(self) -> str: return f'{self.dtype}'
class TensorType(_TensorTypeBase): '''A type that represents a tensor.''' def __str__(self) -> str: pass
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.TypeAndShape
import dataclasses from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting @dataclasses.dataclass class TypeAndShape: """Type and shape. Useful for constructing a type proto. """ type: _protocols.TypeProtocol | None shape: Shape ...
@dataclasses.dataclass class TypeAndShape: '''Type and shape. Useful for constructing a type proto. ''' pass
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core.Usage
from typing import Any, Callable, Generic, NamedTuple, SupportsInt, Union class Usage(NamedTuple): """A usage of a value in a node. Attributes: node: The node that uses the value. idx: The input index of the value in the node. """ node: Node idx: int
class Usage(NamedTuple): '''A usage of a value in a node. Attributes: node: The node that uses the value. idx: The input index of the value in the node. ''' pass
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onnx/ir-py
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onnx_ir._core.Value
from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting import onnx_ir class Value(_protocols.ValueProtocol, _display.PrettyPrintable): """IR...
class Value(_protocols.ValueProtocol, _display.PrettyPrintable): '''IR Value. A value is a named entity that can be used to represent an input or output of a graph, a function, or a node. The information it stores generalizes over ``ValueInfoProto`` in the ONNX specification. A :class:`Value` is al...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core._RecursiveTypeBase
from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting class _RecursiveTypeBase(_protocols.TypeProtocol, _display.PrettyPrintable, Hashable): ...
class _RecursiveTypeBase(_protocols.TypeProtocol, _display.PrettyPrintable, Hashable): '''Base for recursive types like Optional and Sequence.''' def __init__(self, elem_type: _protocols.TypeProtocol, *, denotation: str | None=None) -> None: pass @property def dtype(self) -> _enums.DataType: ...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_core.py
onnx_ir._core._TensorTypeBase
from collections.abc import Collection, Hashable, Iterable, Iterator, Mapping, MutableSequence, Sequence from onnx_ir import _display, _enums, _graph_containers, _linked_list, _metadata, _name_authority, _protocols, _type_casting class _TensorTypeBase(_protocols.TypeProtocol, _display.PrettyPrintable, Hashable): "...
class _TensorTypeBase(_protocols.TypeProtocol, _display.PrettyPrintable, Hashable): '''Tensor types that are non recursive types.''' def __init__(self, dtype: _enums.DataType, *, denotation: str | None=None) -> None: pass @property def dtype(self) -> _enums.DataType: pass @dtype.se...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_display.py
onnx_ir._display.PrettyPrintable
class PrettyPrintable: def display(self, *, page: bool=False) -> None: """Pretty print the object. Args: page: Whether to page the output. """ rich = require_rich() text = str(self) if rich is None: print(text) print(f"\n\n\x1b[36...
class PrettyPrintable: def display(self, *, page: bool=False) -> None: '''Pretty print the object. Args: page: Whether to page the output. ''' pass
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_enums.py
onnx_ir._enums.AttributeType
import enum class AttributeType(enum.IntEnum): """Enum for the types of ONNX attributes.""" UNDEFINED = 0 FLOAT = 1 INT = 2 STRING = 3 TENSOR = 4 GRAPH = 5 FLOATS = 6 INTS = 7 STRINGS = 8 TENSORS = 9 GRAPHS = 10 SPARSE_TENSOR = 11 SPARSE_TENSORS = 12 TYPE_PRO...
class AttributeType(enum.IntEnum): '''Enum for the types of ONNX attributes.''' def __repr__(self) -> str: pass def __str__(self) -> str: pass
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_enums.py
onnx_ir._enums.DataType
import enum import ml_dtypes from typing import Any import numpy as np class DataType(enum.IntEnum): """Enum for the data types of ONNX tensors, defined in ``onnx.TensorProto``.""" UNDEFINED = 0 FLOAT = 1 UINT8 = 2 INT8 = 3 UINT16 = 4 INT16 = 5 INT32 = 6 INT64 = 7 STRING = 8 ...
class DataType(enum.IntEnum): '''Enum for the data types of ONNX tensors, defined in ``onnx.TensorProto``.''' @classmethod def from_numpy(cls, dtype: np.dtype) -> DataType: '''Returns the ONNX data type for the numpy dtype. Raises: TypeError: If the data type is not supported by...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_graph_containers.py
onnx_ir._graph_containers.Attributes
import collections from onnx_ir import _core, _protocols from collections.abc import Iterable, Sequence class Attributes(collections.UserDict[str, '_core.Attr']): """The attributes of a Node as ``dict[str, Attr]`` with additional access methods.""" def __init__(self, attrs: Iterable[_core.Attr]): supe...
class Attributes(collections.UserDict[str, '_core.Attr']): '''The attributes of a Node as ``dict[str, Attr]`` with additional access methods.''' def __init__(self, attrs: Iterable[_core.Attr]): pass def __setitem__(self, key: str, value: _core.Attr) -> None: '''Set an attribute for the no...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_graph_containers.py
onnx_ir._graph_containers.GraphInitializers
import collections from onnx_ir import _core, _protocols class GraphInitializers(collections.UserDict[str, '_core.Value']): """The initializers of a Graph as ``dict[str, Value]`` with additional mutation methods.""" def __init__(self, graph: _core.Graph, dict=None, /, **kwargs): data = {} if d...
class GraphInitializers(collections.UserDict[str, '_core.Value']): '''The initializers of a Graph as ``dict[str, Value]`` with additional mutation methods.''' def __init__(self, graph: _core.Graph, dict=None, /, **kwargs): pass def _set_graph(self, value: _core.Value) -> None: '''Set the ...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_graph_containers.py
onnx_ir._graph_containers.GraphInputs
from onnx_ir import _core, _protocols import onnx_ir class GraphInputs(_GraphIO): """The inputs of a Graph.""" def _check_invariance(self) -> None: """Check the invariance of the graph.""" if not onnx_ir.DEBUG: return for value in self.data: if value._graph is s...
class GraphInputs(_GraphIO): '''The inputs of a Graph.''' def _check_invariance(self) -> None: '''Check the invariance of the graph.''' pass def _set_graph(self, value: _core.Value) -> None: '''Set the graph for the value.''' pass def _maybe_unset_graph(self, value: _...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_graph_containers.py
onnx_ir._graph_containers.GraphOutputs
from onnx_ir import _core, _protocols import onnx_ir class GraphOutputs(_GraphIO): """The outputs of a Graph.""" def _check_invariance(self) -> None: """Check the invariance of the graph.""" if not onnx_ir.DEBUG: return for value in self.data: if value._graph is...
class GraphOutputs(_GraphIO): '''The outputs of a Graph.''' def _check_invariance(self) -> None: '''Check the invariance of the graph.''' pass def _set_graph(self, value: _core.Value) -> None: '''Set the graph for the value.''' pass def _maybe_unset_graph(self, value:...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_graph_containers.py
onnx_ir._graph_containers._GraphIO
from onnx_ir import _core, _protocols from typing import SupportsIndex, TypeVar from collections.abc import Iterable, Sequence import collections class _GraphIO(collections.UserList['_core.Value']): """The inputs and outputs of a Graph.""" def __init__(self, graph: _core.Graph, initlist=None): self._g...
class _GraphIO(collections.UserList['_core.Value']): '''The inputs and outputs of a Graph.''' def __init__(self, graph: _core.Graph, initlist=None): pass def _check_invariance(self) -> None: '''Check the invariance of the graph.''' pass def _set_graph(self, value: _core.Value...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_linked_list.py
onnx_ir._linked_list.DoublyLinkedSet
from collections.abc import Iterable, Iterator, Sequence from typing import Generic, TypeVar, overload class DoublyLinkedSet(Sequence[T], Generic[T]): """A doubly linked ordered set of nodes. The container can be viewed as a set as it does not allow duplicate values. The order of the elements is maintaine...
class DoublyLinkedSet(Sequence[T], Generic[T]): '''A doubly linked ordered set of nodes. The container can be viewed as a set as it does not allow duplicate values. The order of the elements is maintained. One can typically treat it as a doubly linked list with list-like methods implemented. Adding...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_linked_list.py
onnx_ir._linked_list._LinkBox
from typing import Generic, TypeVar, overload class _LinkBox(Generic[T]): """A link in a doubly linked list that has a reference to the actual object in the link. The :class:`_LinkBox` is a container for the actual object in the list. It is used to maintain the links between the elements in the linked lis...
class _LinkBox(Generic[T]): '''A link in a doubly linked list that has a reference to the actual object in the link. The :class:`_LinkBox` is a container for the actual object in the list. It is used to maintain the links between the elements in the linked list. The actual object is stored in the :attr...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_metadata.py
onnx_ir._metadata.MetadataStore
from collections.abc import Mapping import collections from typing import Any class MetadataStore(collections.UserDict): """Class for storing metadata about the IR objects. Metadata is stored as key-value pairs. The keys are strings and the values can be any Python object. The metadata store also sup...
class MetadataStore(collections.UserDict): '''Class for storing metadata about the IR objects. Metadata is stored as key-value pairs. The keys are strings and the values can be any Python object. The metadata store also supports marking keys as invalid. This is useful when a pass wants to mark a ke...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_name_authority.py
onnx_ir._name_authority.NameAuthority
from onnx_ir import _core class NameAuthority: """Class for giving names to values and nodes in the IR. The names are generated in the format ``val_{value_counter}`` for values and ``node_{op_type}_{node_counter}`` for nodes. The counter is incremented each time a new value or node is named. This...
class NameAuthority: '''Class for giving names to values and nodes in the IR. The names are generated in the format ``val_{value_counter}`` for values and ``node_{op_type}_{node_counter}`` for nodes. The counter is incremented each time a new value or node is named. This class keeps tracks of the n...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_protocols.py
onnx_ir._protocols.ArrayCompatible
from typing import Any, Literal, Protocol import typing @typing.runtime_checkable class ArrayCompatible(Protocol): """Protocol for array-like objects. An example of an array-like object is a numpy ndarray or a PyTorch Tensor. Read more at https://numpy.org/devdocs/user/basics.interoperability.html """...
@typing.runtime_checkable class ArrayCompatible(Protocol): '''Protocol for array-like objects. An example of an array-like object is a numpy ndarray or a PyTorch Tensor. Read more at https://numpy.org/devdocs/user/basics.interoperability.html ''' def __array__(self, dtype: Any) -> np.ndarray: ...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_protocols.py
onnx_ir._protocols.AttributeProtocol
import typing from onnx_ir import _enums from typing import Any, Literal, Protocol @typing.runtime_checkable class AttributeProtocol(Protocol): """Protocol for ONNX attributes. Attributes: name: The name of the attribute. type: The type of the attribute. value: The value of the attribu...
@typing.runtime_checkable class AttributeProtocol(Protocol): '''Protocol for ONNX attributes. Attributes: name: The name of the attribute. type: The type of the attribute. value: The value of the attribute. doc_string: Documentation string. ''' def is_ref(self) -> Litera...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_protocols.py
onnx_ir._protocols.DLPackCompatible
import typing from typing import Any, Literal, Protocol @typing.runtime_checkable class DLPackCompatible(Protocol): """Protocol for objects that can support dlpack. Computation backends can call __dlpack__ to obtain the underlying data in a tensor without copying the data. This allows use to use tensorflo...
@typing.runtime_checkable class DLPackCompatible(Protocol): '''Protocol for objects that can support dlpack. Computation backends can call __dlpack__ to obtain the underlying data in a tensor without copying the data. This allows use to use tensorflow tensors etc. without copying the data. ''' ...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_protocols.py
onnx_ir._protocols.FunctionProtocol
from collections.abc import Collection, Iterable, Iterator, Mapping, MutableMapping, MutableSequence, Sequence import typing from typing import Any, Literal, Protocol from collections import OrderedDict @typing.runtime_checkable class FunctionProtocol(Protocol): """Protocol for ONNX functions. Like a graph, a...
@typing.runtime_checkable class FunctionProtocol(Protocol): '''Protocol for ONNX functions. Like a graph, a function can have nodes that are not topologically sorted. It is the responsibility of the user to maintain a topological order of the nodes. Note that there is not a ``node`` attribute in the Fun...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_protocols.py
onnx_ir._protocols.GraphProtocol
import typing from collections.abc import Collection, Iterable, Iterator, Mapping, MutableMapping, MutableSequence, Sequence from typing import Any, Literal, Protocol @typing.runtime_checkable class GraphProtocol(Protocol): """Protocol for graphs. Graph represents a computation graph. In addition to the ONNX ...
@typing.runtime_checkable class GraphProtocol(Protocol): '''Protocol for graphs. Graph represents a computation graph. In addition to the ONNX specification specified fields, it also contains a mapping of :attr:`opset_imports`. This allows different subgraphs to import different opsets. It is the respon...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_protocols.py
onnx_ir._protocols.GraphViewProtocol
from typing import Any, Literal, Protocol import typing from collections.abc import Collection, Iterable, Iterator, Mapping, MutableMapping, MutableSequence, Sequence @typing.runtime_checkable class GraphViewProtocol(Protocol): """Protocol for a read-only view on a graph. The GraphView is useful for analysis ...
@typing.runtime_checkable class GraphViewProtocol(Protocol): '''Protocol for a read-only view on a graph. The GraphView is useful for analysis of a subgraph. It can be initialized with a subset of nodes from a :class:`Graph`. Creating GraphView does not change the ownership of the nodes, and so it is po...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_protocols.py
onnx_ir._protocols.MapTypeProtocol
import typing from typing import Any, Literal, Protocol from onnx_ir import _enums @typing.runtime_checkable class MapTypeProtocol(Protocol): """Protocol for ONNX map types. TODO: This protocol is not yet implemented in the ONNX IR. """ key_type: typing.Literal[_enums.DataType.STRING, _enums.DataType....
@typing.runtime_checkable class MapTypeProtocol(Protocol): '''Protocol for ONNX map types. TODO: This protocol is not yet implemented in the ONNX IR. ''' pass
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_protocols.py
onnx_ir._protocols.ModelProtocol
import typing from collections.abc import Collection, Iterable, Iterator, Mapping, MutableMapping, MutableSequence, Sequence from typing import Any, Literal, Protocol @typing.runtime_checkable class ModelProtocol(Protocol): """Protocol for models. A model is a container for a graph and metadata. It is the top...
@typing.runtime_checkable class ModelProtocol(Protocol): '''Protocol for models. A model is a container for a graph and metadata. It is the top-level object that represents an ONNX model. Attributes: graph: The graph of the model. ir_version: The version of the IR. producer_name:...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/_protocols.py
onnx_ir._protocols.NodeProtocol
from typing import Any, Literal, Protocol import typing from collections.abc import Collection, Iterable, Iterator, Mapping, MutableMapping, MutableSequence, Sequence from collections import OrderedDict @typing.runtime_checkable class NodeProtocol(Protocol): """Protocol for nodes. A node represents an invocat...
@typing.runtime_checkable class NodeProtocol(Protocol): '''Protocol for nodes. A node represents an invocation of an operation on the :class:`Value` s in the computational graph. A node can be optionally named. A name should typically be assigned when the node is added to a graph. :attr:`domain`...
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onnx_ir._protocols.ReferenceAttributeProtocol
from onnx_ir import _enums import typing from typing import Any, Literal, Protocol @typing.runtime_checkable class ReferenceAttributeProtocol(Protocol): """Protocol for a reference attribute. A reference attribute can only appear inside the definition body of a function. Attributes: name: The nam...
@typing.runtime_checkable class ReferenceAttributeProtocol(Protocol): '''Protocol for a reference attribute. A reference attribute can only appear inside the definition body of a function. Attributes: name: The name of the attribute. ref_attr_name: The name of the attribute definition this a...
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onnx/ir-py
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onnx_ir._protocols.ShapeProtocol
from collections.abc import Collection, Iterable, Iterator, Mapping, MutableMapping, MutableSequence, Sequence import typing from typing import Any, Literal, Protocol @typing.runtime_checkable class ShapeProtocol(Protocol): """Protocol for ONNX shapes. A shape is a sequence of dimensions. Attributes: ...
@typing.runtime_checkable class ShapeProtocol(Protocol): '''Protocol for ONNX shapes. A shape is a sequence of dimensions. Attributes: dims: The dimensions of the shape. ''' def __len__(self) -> int: pass def __iter__(self) -> Iterator[int | SymbolicDimProtocol]: pass ...
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onnx/ir-py
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onnx_ir._protocols.SparseTensorProtocol
from collections.abc import Collection, Iterable, Iterator, Mapping, MutableMapping, MutableSequence, Sequence import typing from typing import Any, Literal, Protocol @typing.runtime_checkable class SparseTensorProtocol(Protocol): values: TensorProtocol indices: TensorProtocol dims: Sequence[int]
@typing.runtime_checkable class SparseTensorProtocol(Protocol): pass
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onnx/ir-py
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onnx_ir._protocols.SymbolicDimProtocol
from typing import Any, Literal, Protocol import typing @typing.runtime_checkable class SymbolicDimProtocol(Protocol): """Value of a single symbolic/dynamic dimension in a shape. Attributes: value: The value of the dimension. """ value: str | None
@typing.runtime_checkable class SymbolicDimProtocol(Protocol): '''Value of a single symbolic/dynamic dimension in a shape. Attributes: value: The value of the dimension. ''' pass
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onnx/ir-py
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onnx_ir._protocols.TensorProtocol
from typing import Any, Literal, Protocol from onnx_ir import _enums import typing from collections.abc import Collection, Iterable, Iterator, Mapping, MutableMapping, MutableSequence, Sequence @typing.runtime_checkable class TensorProtocol(ArrayCompatible, DLPackCompatible, Protocol): """Concrete tensor backed by...
@typing.runtime_checkable class TensorProtocol(ArrayCompatible, DLPackCompatible, Protocol): '''Concrete tensor backed by data. The protocol does not specify how the data is stored. That data is exposed through the :attr:`raw` attribute for examination, but accessing :attr:`raw` is typically not needed....
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onnx/ir-py
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onnx_ir._protocols.TypeProtocol
from onnx_ir import _enums from typing import Any, Literal, Protocol import typing @typing.runtime_checkable class TypeProtocol(Protocol): """Protocol for ONNX tensors, Sequence tensors, Optional tensors and Sparse tensors. These three types of tensors share the same attribute "elem_type" so they are merg...
@typing.runtime_checkable class TypeProtocol(Protocol): '''Protocol for ONNX tensors, Sequence tensors, Optional tensors and Sparse tensors. These three types of tensors share the same attribute "elem_type" so they are merged in the same interface. Unlike the ONNX TensorProto, shapes are not included in...
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onnx/ir-py
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onnx_ir._protocols.ValueProtocol
from typing import Any, Literal, Protocol import typing from collections.abc import Collection, Iterable, Iterator, Mapping, MutableMapping, MutableSequence, Sequence @typing.runtime_checkable class ValueProtocol(Protocol): """Protocol for values. A value is a named entity that can be used to represent an inp...
@typing.runtime_checkable class ValueProtocol(Protocol): '''Protocol for values. A value is a named entity that can be used to represent an input or output of a graph, a function, or a node. The information it stores generalizes over ``ValueInfoProto`` in the ONNX specification. A :class:`Value` is ...
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onnx/ir-py
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onnx_ir._tape.Builder
from typing import Any, Optional import onnx_ir as ir from collections.abc import Mapping, Sequence class Builder(Tape): """An extension of the tape that provides a more convenient API for constructing the IR.""" def __getattr__(self, op_type: str) -> Any: return lambda *args, **kwargs: self._make_nod...
class Builder(Tape): '''An extension of the tape that provides a more convenient API for constructing the IR.''' def __getattr__(self, op_type: str) -> Any: pass def _make_node(self, op_type: str, inputs: Sequence[ir.Value], kwargs: dict[str, Any]): pass
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onnx/ir-py
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onnx_ir._tape.Tape
from typing import Any, Optional from onnx_ir import _convenience from collections.abc import Mapping, Sequence import onnx_ir as ir class Tape: """Tape class. A tape is a recorder that collects nodes and initializers that are created so that they can be used for creating a graph. Example:: ...
class Tape: '''Tape class. A tape is a recorder that collects nodes and initializers that are created so that they can be used for creating a graph. Example:: import onnx_ir as ir tape = ir.tape.Tape() a = tape.initializer(ir.tensor([1, 2, 3], name="a")) b: ir.Value = .....
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/external_data.py
onnx_ir.external_data.CallbackInfo
import dataclasses @dataclasses.dataclass class CallbackInfo: """A class that shares information about a tensor that is to be saved as external data for callback functions. Attributes: total: The total number of tensors to save. index: The index of the tensor being saved. offset: The o...
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onnx/ir-py
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/onnx_ir-py/src/onnx_ir/external_data.py
onnx_ir.external_data._ExternalDataInfo
import dataclasses @dataclasses.dataclass class _ExternalDataInfo: """A class that stores information about a tensor that is to be stored as external data. Attributes: name: The name of the tensor that is to be stored as external data. offset: The offset is used to determine where exactly in t...
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onnx/ir-py
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onnx_ir.passes._pass_infra.FunctionalPass
from typing import Literal, final class FunctionalPass(PassBase): """A pass that returns a new model but does not modify the input model.""" @property @final def in_place(self) -> Literal[False]: """A functional pass is not in place.""" return False @property @final def ch...
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onnx/ir-py
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onnx_ir.passes._pass_infra.InPlacePass
from typing import Literal, final class InPlacePass(PassBase): """A pass that modifies the input model in place and returns it.""" @property @final def in_place(self) -> Literal[True]: """An in-place pass is in place.""" return True @property @final def changes_input(self)...
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onnx/ir-py
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onnx_ir.passes._pass_infra.InvariantError
class InvariantError(Exception): """Raised when an invariant is violated."""
class InvariantError(Exception): '''Raised when an invariant is violated.''' pass
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onnx/ir-py
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onnx_ir.passes._pass_infra.PassBase
import abc import onnx_ir as ir class PassBase(abc.ABC): """Base class for all passes. ``in_place`` and ``changes_input`` properties and what they mean: +------------+------------------+----------------------------+ | | changes_inputs | not changes_inputs | +------------+----...
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onnx/ir-py
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onnx_ir.passes._pass_infra.PassError
class PassError(RuntimeError): """Raised when an error occurs during a pass."""
class PassError(RuntimeError): '''Raised when an error occurs during a pass.''' pass
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onnx/ir-py
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onnx_ir.passes._pass_infra.PassManager
import onnx_ir as ir from collections.abc import Sequence class PassManager(Sequential): """Pass manager for the IR. The PassManager is a Pass that runs a sequence of passes on a model. Attributes: passes: The passes to run. steps: The number of times to run the passes. early_stop...
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onnx/ir-py
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onnx_ir.passes._pass_infra.PassResult
import dataclasses import onnx_ir as ir @dataclasses.dataclass class PassResult: """Result of a pass. Attributes: model: The transformed model. modified: Whether the resulting model is different from the input model. """ model: ir.Model modified: bool
@dataclasses.dataclass class PassResult: '''Result of a pass. Attributes: model: The transformed model. modified: Whether the resulting model is different from the input model. ''' pass
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onnx/ir-py
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onnx_ir.passes._pass_infra.PostconditionError
class PostconditionError(InvariantError): """Raised when a postcondition is violated."""
class PostconditionError(InvariantError): '''Raised when a postcondition is violated.''' pass
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onnx/ir-py
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onnx_ir.passes._pass_infra.PreconditionError
class PreconditionError(InvariantError): """Raised when a precondition is violated."""
class PreconditionError(InvariantError): '''Raised when a precondition is violated.''' pass
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onnx/ir-py
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onnx_ir.passes._pass_infra.Sequential
import onnx_ir as ir class Sequential(PassBase): """Run a sequence of passes in order.""" def __init__(self, *passes: PassBase): if not passes: raise ValueError('Sequential must take at least one pass') self.passes = passes self._in_place = all((pass_.in_place for pass_ in ...
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onnx/ir-py
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onnx_ir.passes.common.clear_metadata_and_docstring.ClearMetadataAndDocStringPass
import onnx_ir as ir class ClearMetadataAndDocStringPass(ir.passes.InPlacePass): """Clear all metadata and docstring from the model, graphs, nodes, and functions.""" def call(self, model: ir.Model) -> ir.passes.PassResult: modified = self._clear_graph_or_function_metadata_and_docstring(model.graph) ...
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