# embeddings_queue

created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2498-2502
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2502, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "CvtPreTrainedModel", "start_line": 2498, "type": "class", "chroma:document": "CODE:\n\nclass CvtPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`CvtPreTrainedModel`\n\nThe main purpose of this class is to create a model wrapper for pre-trained models, supporting the `torch` backend.\n\n**Key Implementation Details:**\n\n* Requires `torch` as the supported backend.\n* Uses the `DummyObject` metaclass.\n\n**Notable Dependencies and Requirements:**\n\n* Backends supported: `torch`\n* External dependencies: None\n\nNote: The exact method signature for `requires_backends(self, backends)` is not provided in the given code chunk, but it's assumed to be a part of the parent class or imported module."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2505-2509
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2509, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DacModel", "start_line": 2505, "type": "class", "chroma:document": "CODE:\n\nclass DacModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DacModel`**\n\n*   **Main Purpose:** Models for Data Acquisition and Conversion using the `Dac` framework.\n\n### Implementation Details\n\nThe class inherits from `DummyObject` with specific backend support: `torch`.\n\n*   Initialization method (`__init__`)\n    *   Verifies that at least one required backend is available (in this case, \"torch\").\n        - Requires backends to be initialized before proceeding.\n    - Expects variable arguments and keyword arguments using `*args` and `**kwargs`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2512-2516
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2516, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DacPreTrainedModel", "start_line": 2512, "type": "class", "chroma:document": "CODE:\n\nclass DacPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n*   **Class Name:** `DacPreTrainedModel`\n*   **Purpose:** A pre-trained model class using the PyTorch backend.\n*   **Functionality:** Initializes the `DacPreTrainedModel` with required backends.\n\n**Key Implementation Details:**\n\n*   Initializer (`__init__`) that requires PyTorch backend (`requires_backends(self, [\"torch\"])`)\n*   Uses a custom metaclass (`DummyObject`)\n\n**Notable Dependencies/Requirements:**\n\n*   `requires_backends` function\n*   PyTorch library (backend)\n*   `DummyObject` metaclass"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2519-2523
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2523, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecAudioForAudioFrameClassification", "start_line": 2519, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecAudioForAudioFrameClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Data2VecAudioForAudioFrameClassification` class using the Torch backend for audio frame classification.\n\n**Key Points:**\n\n*   Creates an instance of this class, which is designed to handle audio frame classification tasks.\n*   Requires the Torch backend for functionality (```.requires_backends(self, [\"torch\"]```).\n*   Currently supports only one backend (`_backends = [\"torch\"]`).\n\nNo additional implementation details are provided in this snippet. However, the class name and function signature hint at a potential use case in natural language processing or other downstream applications that involve audio data processing.\n\nAssuming further context or code is needed to provide more information about the actual functionality of `Data2VecAudioForAudioFrameClassification`,  the summary above provides essential details based on given snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2526-2530
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2530, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecAudioForCTC", "start_line": 2526, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecAudioForCTC(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Data2VecAudioForCTC` class:\n\n* Primary purpose: Enables audio data processing for CTC (Connected Triangle Cells) tasks using the Torch backend.\n* Key implementation details:\n\t+ Initializes with Torch as the required backend.\n\t+ Requires `requires_backends` method to be implemented.\n* Notable dependencies or requirements:\n\t+ Torch library.\n\n**Analysis:**\n\nThis class inherits from a metaclass (`DummyObject`) and is primarily designed for audio data processing in CTC tasks. The use of Torch as the required backend suggests that this class may be part of a larger model or framework that utilizes data2vec, which is likely related to Natural Language Processing (NLP) tasks.\n\nThe `requires_backends` method indicates that the implementation requires backend configurations, but its behavior is not defined in this snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2533-2537
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2537, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecAudioForSequenceClassification", "start_line": 2533, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecAudioForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Data2VecAudioForSequenceClassification`\n\n*   A pre-trained model for sequence classification tasks utilizing audio data.\n*   The model relies on the `torch` backend.\n\n**Key Details:**\n\n*   Requires initialization with `\"torch\"` backend using `requires_backends`.\n\n**Notable Dependencies:**\n\n*   Inherited from `DummyObject` metaclass.\n*   Utilizes `torch` as a backend dependency."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2540-2544
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2544, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecAudioForXVector", "start_line": 2540, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecAudioForXVector(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Data2VecAudioForXVector` is a PyTorch-powered data processing class designed for embedding audio data. \n\n**Key Features and Details:**\n\n* Primary Functionality: Embeds audio data using XVector algorithm.\n* Implementation Details:\n\t+ Uses `requires_backends` to ensure support for only PyTorch backend.\n\t+ Initializes the instance with variable arguments/keywords.\n\n**Notable Dependencies:**\n\n* External Libraries: PyTorch\n* Inherited Interfaces: None\n* Implemented Interfaces:\n  - `DummyObject` (_metaclass)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2547-2551
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2551, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecAudioModel", "start_line": 2547, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecAudioModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Data2Vec Audio Model Class Summary**\n\n*   **Main Purpose and Functionality:** The `Data2VecAudioModel` class is an implementation of the Data2Vec audio modeling framework using the Torch backend.\n\n    *   Primary purpose: To provide an interface for training and processing audio data using Data2Vec algorithms.\n*   **Key Implementation Details:** \n    *   Requires the Torch backend, which is imported by `requires_backends`.\n        *   Notable dependency:\n            +   `torch`: PyTorch library used as the backend for this model.\n\n**Method Signatures:**\n\n    None explicitly stated in the provided code snippet.\n*   **Interfaces/Inherited Interfaces:** No explicit interface inheritance mentioned."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2554-2558
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2558, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecAudioPreTrainedModel", "start_line": 2554, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecAudioPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Data2VecAudioPreTrainedModel**\n\n**Class Overview**\n---------------\n\nThe `Data2VecAudioPreTrainedModel` class is a PyTorch-based pre-trained audio model.\n\n**Key Implementation Details**\n---------------------------\n\n### Constructor\n`__init__(self, *args, **kwargs)`\n\n* Requires a PyTorch backend (`torch`)\n\n### Backend Enforcement\n\n`requires_backends(self, [\"torch\"])`\n\nEnsures that only `PyTorch` is supported as the backend for this model.\n\n**Notable Dependencies/Requirements**\n--------------------------------------\n\n* PyTorch (`torch`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2561-2565
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2565, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecTextForCausalLM", "start_line": 2561, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecTextForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`Data2VecTextForCausalLM`\n\n**Main Purpose and Functionality**: An implementation of the Data2Vec embedding model for text data in causal language models.\n\n**Key Implementation Details**:\n\n* Utilizes the `torch` backend.\n* Requires initialization with `torchaugmenteddataset` dataset instance.\n\n**Notable Dependencies/Requirements**:\n \n* External Dependency: `torch` (specifically, augmented datasets support)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2568-2572
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2572, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecTextForMaskedLM", "start_line": 2568, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecTextForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\nHere is a concise and accurate summary of the code chunk:\n\n**Class Summary:**\n\n`Data2VecTextForMaskedLM`\n\nA masked language model class using the `torch` backend.\n\n**Main Purpose:**\n\nThis class implements a masked language model, likely for natural language processing tasks, using PyTorch.\n\n**Key Implementation Details:**\n\n- Uses the `torch` backend (`_backends = [\"torch\"]`)\n- Requires the `torch` backend in the constructor using `requires_backends`\n\n**Notable Dependencies/Requirements:**\n\n- PyTorch library\n  - Specifically, `_backends` references the \"torch\" module."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2575-2579
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2579, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecTextForMultipleChoice", "start_line": 2575, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecTextForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `Data2VecTextForMultipleChoice`**\n\n**Purpose:** An extension class for leveraging Data2Vec's text processing capabilities in multiple-choice settings.\n\n**Key Implementation Details:**\n\n* Initializes with `*args` and `**kwargs`.\n* Requires the presence of the `torch` backend.\n\n**Notable Dependencies/Requirements:**\n\n* Backends: `torcha` (aligned with `Data2VecTextForMultipleChoice._backends`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2582-2586
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2586, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecTextForQuestionAnswering", "start_line": 2582, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecTextForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `Data2VecTextForQuestionAnswering`**\n\n### Primary Purpose\nThis class is designed to process text data for question answering scenarios using the Data2Vec library.\n\n### Key Implementation Details\n\n* Initialization method (`__init__`) requires specific backend support (\"torch\") and ensures compatibility with the required backend.\n* The class utilizes a `dummy object` metaclass, which is not a standard Python metaclass but is likely provided by the Data2Vec library.\n\n### Notable Dependencies/Requirements\n\n* Requires \"torch\" as a supported backend for functionality.\n* Utilizes related libraries: None explicitly listed.\n* No inherited or implemented interfaces mentioned.\n\nOverall Summary:\n`Data2VecTextForQuestionAnswering` class initializes text data processing with a specified backend, ensuring compatibility and enabling question answering capabilities."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2589-2593
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2593, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecTextForSequenceClassification", "start_line": 2589, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecTextForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n*   `Data2VecTextForSequenceClassification`: A sequence classification model that utilizes the Hugging Face's Data2Vec framework for text representation learning. The primary purpose is to leverage pre-trained models (backed by PyTorch) to predict sequence labels.\n\n*   **Key Implementation Details:**\n    -   Uses `torch` backend due to `requires_backends(self, [\"torch\"])`.\n    -   The class requires a PyTorch environment for its execution.\n\n*   **Notable Dependencies/Requirements:**\n    *   Related classes: `DummyObject`, `torch`\n    *   External Dependency: PyTorch\n        *   Implemented Interfaces (if any): N/A"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2596-2600
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2600, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecTextForTokenClassification", "start_line": 2596, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecTextForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `Data2VecTextForTokenClassification`**\n\n**Purpose and Functionality**\nThis class is an implementation of the `Data2Vec` model specifically designed for text token classification tasks.\n\n**Key Implementation Details**\n\n* Utilizes the PyTorch backend (`torch`)\n* Requires the following backends to be supported by the system: `torch`\n\n**Notable Dependencies and Requirements**\n\n* Inherited `DummyObject` metaclass\n* External dependency on PyTorch (`torch`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2603-2607
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2607, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecTextModel", "start_line": 2603, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecTextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `Data2VecTextModel` class is a customizable text model using the `torch` backend. It requires the `torch` backend to be specified during instantiation.\n\n*   **Class Purpose:** Customizable text model for `Data2Vec`.\n*   **Key Implementation Details:**\n    *   Uses the `torch` backend.\n    *   Requires specifying the `torch` backend during instantiation (via `requires_backends` method).\n*   **Notable Dependencies/Requirements:**\n    *   External dependency on `torch`.\n\n**Detailed Analysis:**\n\nThe class inherits from a dummy metaclass (`DummyObject`) and defines a single initialization method (`__init__`). \n\nThe `requires_backends` method is used to enforce the use of the specified backend. This ensures that certain backends are used for specific model configurations.\n\nIn this case, the `Data2VecTextModel` class only uses the `torch` backend and relies on it being provided during instantiation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2610-2614
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2614, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecTextPreTrainedModel", "start_line": 2610, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecTextPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThis is a PyTorch-based implementation of the `Data2VecTextPreTrainedModel` class. The primary purpose is to be pre-trained on text data using the Data2Vec framework.\n\n**Key Details**\n\n* **Backend**: Primarily uses 'torch' backend.\n* **Initialization**: Requires a torch backend for initialization.\n\n**No notable dependencies or requirements listed in this code snippet.**\n\nNote: This summary assumes that the `DummyObject` metaclass and `requires_backends` function are defined elsewhere in the codebase, but they are not provided in the given snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2617-2621
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2621, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecVisionForImageClassification", "start_line": 2617, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecVisionForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`Data2VecVisionForImageClassification`\n\n*   **Purpose and Functionality:** A class for image classification using the Data2Vec vision model framework, built on top of the Torch backend.\n\n**Implementation Details:**\n\n*   The `__init__` method:\n    *   Requires the `torch` backend using the `requires_backends` function.\n    *   Takes `*args` and `**kwargs` as input parameters.\n\n**Notable Dependencies and Requirements:**\n\n*   Uses the Torch backend (`torch`) for compatibility and performance.\n*   Inherited/Implemented interfaces:\n    *   None explicitly mentioned, but inherits from the base `Data2VecVision` class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2624-2628
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2628, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecVisionForSemanticSegmentation", "start_line": 2624, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecVisionForSemanticSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n**Data2VecVisionForSemanticSegmentation Class Summary**\n\n*   **Purpose and Functionality:** A PyTorch-based data augmentation class for semantic segmentation tasks (`Data2Vec`-like approach).\n*   **Key Implementation Details:**\n    *   Initializes with arbitrary arguments using `__init__`.\n    *   Requires the \"torch\" backend, indicating compatibility with PyTorch.\n        *   Uses the `requires_backends` function to specify this requirement.\n*   **Notable Dependencies or Requirements:**\n    *   The class relies on PyTorch (`\"torch\"` backend).\n\nNo external dependencies were found in this code snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2631-2635
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2635, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecVisionModel", "start_line": 2631, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecVisionModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n```markdown\n# Data2VecVisionModel Class\n\n### Main Purpose and Functionality\nThis class represents a Vision Model for the Data2Vec library. It inherits from `DummyObject` and is built using the `torch` backend.\n\n*   Requires the `torch` backend upon instantiation.\n```\n\n### Key Implementation Details\n\n*   Constructs the model with any required backends, enforcing `torch` in this case.\n    ```python\ndef __init__(self, *args, **kwargs):\n    requires_backends(self, [\"torch\"])\n    # ...\n```\n    \n### Notable Dependencies and Requirements\n\n*   Uses the `DummyObject` metaclass.\n*   Expects the `torch` backend to be provided."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2638-2642
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2642, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Data2VecVisionPreTrainedModel", "start_line": 2638, "type": "class", "chroma:document": "CODE:\n\nclass Data2VecVisionPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`Data2VecVisionPreTrainedModel`\n\nA PyTorch-based class modeling pre-trained vision data.\n\n**Key Details**\n\n* Primary purpose: Pre-trained vision model for Data2vec applications.\n* Implementation:\n\t+ Requires PyTorch backend (`torch`).\n\t+ Uses `DummyObject` metaclass.\n* Notable dependencies:\n\t+ `requires_backends` function ( referenced implicitly).\n\t+ PyTorch library (not explicitly mentioned, but implied by `_backends` attribute).\n\nNo specific methods or variables are defined in this code snippet. The class appears to be a marker or abstract base class for pre-trained vision models."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2645-2649
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2649, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DbrxForCausalLM", "start_line": 2645, "type": "class", "chroma:document": "CODE:\n\nclass DbrxForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DbrxForCausalLM`**\n\n* **Main Purpose:** An implementation of the DiffBertRoux (DBR) model for causal language modeling on Hugging Face's `Causal LM`.\n* **Primary Functionality:** Applies the DBR model to input sequences using `torcht`.\n\n**Implementation Details:**\n- Initializes with a configuration that requires the PyTorch backend.\n- Utilizes the `require_backends` function to specify required backends.\n\n**Notable Dependencies and Requirements:**\n\n- Relies on `torch` as a required backend for implementation.\n- Inherited or Implemented Interfaces: None listed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2652-2656
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2656, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DbrxModel", "start_line": 2652, "type": "class", "chroma:document": "CODE:\n\nclass DbrxModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DbrxModel` class that inherits from a metaclass `DummyObject`. It initializes with optional arguments and ensures compatibility with at least one backend (`torch`). \n\n**Key Implementation Details:**\n\n* Requires the presence of at least `self.require_backends(['torch'])`\n\n**Notable Dependencies/Requirements:**\n\n* Inherited `metaclass=DummyObject` from parent class\n* Requires a compatible backend, currently using `torch`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2659-2663
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2663, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DbrxPreTrainedModel", "start_line": 2659, "type": "class", "chroma:document": "CODE:\n\nclass DbrxPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DbrxPreTrainedModel`**\n\n*   **Main Purpose and Functionality**: An instance of this model preloads and manages computations in the \"torch\" backend.\n*   **Key Implementation Details**: The class uses the `dummy_object` metaclass to manage its state, requires the \"torch\" backend for computation, and takes additional positional (`args`) and keyword arguments (`kwargs`) in its constructor (`__init__`).\n*   **Notable Dependencies or Requirements**:\n    *   Uses Django's `DummyObject` as a metaclass.\n    *   Requires the \"torch\" backend for functionality."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2666-2670
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2670, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaForMaskedLM", "start_line": 2666, "type": "class", "chroma:document": "CODE:\n\nclass DebertaForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DebertaForMaskedLM` class is a PyTorch-based implementation of the DeBERTA (Dynamics-of-Buffered-Evaluation-as-Tasks-Aware) model for masked language modeling tasks. It requires the \"torch\" backend.\n\n* **Main purpose and functionality:** Instantiates DeBERTA model for masked language modeling tasks.\n* **Key implementation details:**\n\t+ Uses PyTorch (`self._backends = [\"torch\"]`).\n\t+ Validates backend requirements in `__init__` method (`requires_backends(self, [\"torch\"])`).\n* **Notable dependencies and requirements:**\n\t+ Requires \"torch\" as the primary backend.\n\t+ Inherited `DummyObject` metaclass.\n\n**RAG System Representation:**\n\n| Item | Description |\n| --- | --- |\n| Name | DebertaForMaskedLM (class) |\n| Purpose | Masked language modeling with PyTorch-based DeBERTA model |\n| Backend Requirement | \"torch\" |\n| Implementation | PyTorch backend usage, validator for backend requirements |\n\n**External Dependencies:**\n\n* torch\n* DummyObject (metaclass)\n\nNote: The provided code does not specify the `DebertaForMaskedLM` class implementation beyond its constructor. If you'd like to provide more context, I can assist in creating a more detailed summary."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2673-2677
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2677, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaForQuestionAnswering", "start_line": 2673, "type": "class", "chroma:document": "CODE:\n\nclass DebertaForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DebertaForQuestionAnswering` is a PyTorch-based model subclass (`class`) designed for question-answering tasks, utilizing the DeBERTa algorithm. The primary purpose of this class is to provide a pre-trained model for answering questions, leveraging the `torch` backend.\n\n**Key Implementation Details**\n\n* The `__init__` method initializes the model and requires the `torch` backend.\n* The `requires_backends` function is used to specify the required backend (`\"torch\"`).\n\n**Notable Dependencies and Requirements**\n\n* `torch`: The primary backend dependency for this model.\n* `DummyObject`: The metaclass used to define the `DebertaForQuestionAnswering` class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2680-2684
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2684, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaForSequenceClassification", "start_line": 2680, "type": "class", "chroma:document": "CODE:\n\nclass DebertaForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DebertaForSequenceClassification` class is a PyTorch-based implementation of the DeBERTa model for sequence classification tasks.\n\n**Key Implementation Details:**\n\n* Initializes with support for only the `torch` backend (`torch`)\n* Requires `torch` as a prerequisite for object creation\n\n**Notable Dependencies and Requirements:**\n\n* Extends `DummyObject` metaclass\n* Uses PyTorch library for implementation"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2687-2691
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2691, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaForTokenClassification", "start_line": 2687, "type": "class", "chroma:document": "CODE:\n\nclass DebertaForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DebertaForTokenClassification` class implements a `Model` with the Deberta library for token classification tasks. It leverages the `torch` backend.\n\n**Key Points:**\n\n* Primary purpose: Token classification using Deberta model\n* Key implementation details:\n\t+ Uses Deberta library for modeling\n\t+ Requires `torch` backend for execution\n* Notable dependencies or requirements:\n\t+ `Deberta` library (library class not shown in the snippet)\n\t+ `torch`: Required backend for model execution"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2694-2698
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2698, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaModel", "start_line": 2694, "type": "class", "chroma:document": "CODE:\n\nclass DebertaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n`DebertaModel` is a PyTorch-based sequence-to-sequence model class that initializes with the `torch` backend. It requires the `torch` backend for operation.\n\n*Class attributes and methods:* \n- `_backends`: List of supported backends\n- `__init__`: Initializes the model with optional arguments, verifying `torch` as a required backend.\n\n**Key Implementation:**\n- Verifies `torch` as a required backend using the `require_backends` method."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2701-2705
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2705, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaPreTrainedModel", "start_line": 2701, "type": "class", "chroma:document": "CODE:\n\nclass DebertaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DebertaPreTrainedModel`: A PyTorch-based implementation of the DeBERTa model.\n\n**Key Points:**\n\n* **Primary Purpose:** Implementation of DeBERTa pre-trained model architecture.\n* **Implementation Details:** Uses the `torch` backend and requires it to be installed (`requires_backends(\"torch\")`).\n\n**Notable Dependencies/Requirements:**\n\n* Backend Requirements: `torch`\n* Inherited Interfaces: Not explicitly stated, but likely inherits from a common PyTorch model interface."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2708-2712
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2712, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaV2ForMaskedLM", "start_line": 2708, "type": "class", "chroma:document": "CODE:\n\nclass DebertaV2ForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n*Class: `DebertaV2ForMaskedLM`*\n=====================================\n\nThe `DebertaV2ForMaskedLM` class is an implementation of the DeBERTa V2 model for masked language modeling tasks. It uses the PyTorch backend.\n\n**Implementation Details:**\n\n* The class requires PyTorch as a fundamental dependency.\n* Initializes with arguments and optional keyword arguments, but does not perform any operation in its `__init__` method.\n\nNote: `DummyObject` is used as a metaclass, which is unusual and may require additional context to understand its purpose."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2715-2719
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2719, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaV2ForMultipleChoice", "start_line": 2715, "type": "class", "chroma:document": "CODE:\n\nclass DebertaV2ForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**DebertaV2ForMultipleChoice Class Summary**\n\n### Purpose and Functionality\nThe `DebertaV2ForMultipleChoice` class is a variant of the DeBERTa model designed for multiple-choice tasks. It inherits a dummy object metaclass and requires the tensor backend \"torch\".\n\n### Key Implementation Details\n\n*   Initializes with `requires_backends(self, [\"torch\"])`, ensuring compatibility with the \"torch\" backend.\n    *   Notable implementation: Using `DummyObject` as a metaclass.\n\n### Dependencies and Requirements\n*   Requires the \"torch\" library (backend dependency).\n\n### Class Signature\n```python\nclass DebertaV2ForMultipleChoice(metaclass=DummyObject):\n```\nNote: No method signatures or interfaces are defined in this code snippet, so no additional information is provided."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2722-2726
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2726, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaV2ForQuestionAnswering", "start_line": 2722, "type": "class", "chroma:document": "CODE:\n\nclass DebertaV2ForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nDebertaV2ForQuestionAnswering is a class implementing a question answering model using DeBERTaV2 architecture with PyTorch backend as the single supported platform. The `__init__` method initializes the object, requiring the \"torch\" backend to be specified.\n\n**Key Details:**\n\n* Implemented model: DebertaV2ForQuestionAnswering\n* Supported backend: PyTorch\n* Requires backend specification in initialization\n\n**Notable Dependencies and Requirements:**\n\n* PyTorch (backend requirement)\n* DummyObject (metaclass)\n(Note that \"Provide\" seems to be an error or misprint; removing it from the output summary)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2729-2733
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2733, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaV2ForSequenceClassification", "start_line": 2729, "type": "class", "chroma:document": "CODE:\n\nclass DebertaV2ForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DebertaV2ForSequenceClassification`\n\n*   **Purpose:** A sequence classification model based on the DeBERTa-V2 architecture.\n*   **Primary Class:** `DebertaV2ForSequenceClassification`\n*   **Metaclass:** `DummyObject`\n*   **Backend Requirements:** `torch`\n\n**Key Implementation Details:**\n\n*   The model inherits from a backend class that requires `torchelement` to be instantiated (`requires_backends(self, [\"torch\"])`).\n\n**Notable Dependencies/Requirements:**\n\n*   Inherited interface: None\n*   Implemented interfaces: None\n*   External dependencies:\n    *   ` torch`: Required backend for instantiation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2736-2740
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2740, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaV2ForTokenClassification", "start_line": 2736, "type": "class", "chroma:document": "CODE:\n\nclass DebertaV2ForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**DebertaV2ForTokenClassification Class Summary**\n\n**Main Purpose and Functionality:**\nThe DebertaV2ForTokenClassification class is a custom neural network model based on the Hugging Face library.\n\n**Key Implementation Details:**\n\n* Initializes with `torch` backend requirement.\n* Automatically checks for required backends using the `requires_backends` function.\n\n**Notable Dependencies or Requirements:**\n\n* Requires `torch` backend implementation.\n* Inherited or implemented interface not specified, may require additional dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2743-2747
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2747, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaV2Model", "start_line": 2743, "type": "class", "chroma:document": "CODE:\n\nclass DebertaV2Model(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n`DebertaV2Model` class implements a specific variant of the DeBERTa model. This class is designed to work with PyTorch (`torch`) and has an initialization method that confirms the required backend.\n\n**Key Details:**\n\n*   **Purpose:** Implement DeBERTa V2 model.\n*   **Backend:** `torch`\n*   **Implementation Details:**\n    *   Initializes the model using `requires_backends` method, confirming `torch` as a required backend.\n        +   This suggests that `DebertaV2Model` inherits or implements specific class from an existing PyTorch module."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2750-2754
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2754, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DebertaV2PreTrainedModel", "start_line": 2750, "type": "class", "chroma:document": "CODE:\n\nclass DebertaV2PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**DebertaV2PreTrainedModel Class Summary**\n\n### Main Purpose and Functionality\n\nThe `DebertaV2PreTrainedModel` class is a machine learning model that inherits from the `DummyObject` metaclass. It's a pre-trained Deberta V2 model, supporting only the `torch` backend.\n\n### Key Implementation Details\n\n*   **Initialization**: The `__init__` method initializes the model with varying arguments (`*args`, `**kwargs`) and requires the `torch` backend.\n*   `_backends` attribute is set to `[\"torch\"]`, indicating that this model only supports the `torch` backend.\n\n### Notable Dependencies or Requirements\n\n*   External Dependency:\n    *   `torch`\n*   Inherited Interface: Although not explicitly mentioned, any model implementing `DummyObject` would typically inherit related interfaces for processing and manipulation of data."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2757-2761
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2761, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DecisionTransformerGPT2Model", "start_line": 2757, "type": "class", "chroma:document": "CODE:\n\nclass DecisionTransformerGPT2Model(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DecisionTransformerGPT2Model`: A PyTorch-based transformer model for natural language processing tasks.\n\n**Key Implementation Details:**\n\n* Primary purpose: Utilizes the Decision Transformer architecture with GPT-2-like attention patterns to improve sequence-to-sequence modeling.\n* Key algorithm: Uses a variant of the Transformer self-attention mechanism, leveraging a decision-making approach to adaptively weight input embeddings.\n\n**Notable Dependencies and Requirements:**\n\n* Requires `torch` as the primary backend, indicating it supports PyTorch-based computation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2764-2768
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2768, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DecisionTransformerGPT2PreTrainedModel", "start_line": 2764, "type": "class", "chroma:document": "CODE:\n\nclass DecisionTransformerGPT2PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**DecisionTransformer GPT2 Pre-Trained Model**\n\n### Primary Purpose\nThe `DecisionTransformerGPT2PreTrainedModel` class is a pre-trained model architecture for GPT-2 decision transformers.\n\n### Key Implementation Details\n\n* The model requires the `\"torch\"` backend for initialization.\n* The `__init__` method initializes the model with arbitrary arguments using variable unpacking (`*args`) and keyword arguments (`**kwargs`).\n\n### Dependencies and Requirements\n\n* The model utilizes the `\"torch\"` backend for compatibility.\n* No external dependencies are specified."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2771-2775
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2775, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DecisionTransformerModel", "start_line": 2771, "type": "class", "chroma:document": "CODE:\n\nclass DecisionTransformerModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DecisionTransformerModel` is an instance of `DummyObject` with the following characteristics:\n\n**Primary Purpose**: The class defines a model that utilizes the `torch` backend for decision transformer related functionality.\n\n**Key Implementation Details**:\n\n* Enforces the use of the `torch` backend.\n* Initializes the object with arguments and keyword arguments (`args` and `kwargs`).\n\n**Notable Dependencies or Requirements**:\n\n* Relies on the `dummy_object` metaclass.\n* Requires the `torch` backend for proper operation.\n* No external dependencies listed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2778-2782
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2782, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DecisionTransformerPreTrainedModel", "start_line": 2778, "type": "class", "chroma:document": "CODE:\n\nclass DecisionTransformerPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `DecisionTransformerPreTrainedModel` class is a pre-trained model implemented using the Torch backend. \n\n* Primary purpose: Pre-trained Decision Transformer model with the primary backend being Torch.\n* Notable implementation details: \n  - Requires the `torch` backend during initialization.\n  - Utilizes the `DummyObject` metaclass implicitly.\n* Inherited or implemented interfaces: None explicitly mentioned."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2785-2789
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2789, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DeformableDetrForObjectDetection", "start_line": 2785, "type": "class", "chroma:document": "CODE:\n\nclass DeformableDetrForObjectDetection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**DeformableDetrForObjectDetection Class Summary**\n\n* **Class Purpose:** A machine learning class for object detection using Deformable Detr architecture.\n* **Primary Purpose:** Initialize the class with required backends and verify compatibility.\n\n**Key Implementation Details:**\n\n* Initializes the `self` object with support for \"torch\" backend.\n* Verifies the presence of a `requires_backends` function that takes two arguments: the object being initialized and a list of supported backends.\n\n**Notable Dependencies or Requirements:**\n\n* Requires the `torch` backend implementation to be compatible."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2792-2796
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2796, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DeformableDetrModel", "start_line": 2792, "type": "class", "chroma:document": "CODE:\n\nclass DeformableDetrModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Deformable Detr Model Class Summary**\n\n*   **Purpose**: Creates an instance of the Deformable DetrModel class utilizing the Torch backend.\n\n*   **Key Details**:\n    *   Initializes a `DeformableDetrModel` with support for the Torch backend.\n    *   Requires the Torch backend using the `requires_backends` function.\n    *   Implemented metaclass: `DummyObject`.\n\n*   **Notable Dependencies and Requirements**:\n    *   Backend: Torch (backend class: `torch`)\n    *   Inherited Interfaces: None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2799-2803
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2803, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DeformableDetrPreTrainedModel", "start_line": 2799, "type": "class", "chroma:document": "CODE:\n\nclass DeformableDetrPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n`DeformableDetrPreTrainedModel`: A PyTorch-based model class for Deformable Detr (Deformable Transformer) pre-trained models.\n\n**Key Features:**\n\n- **Primary Purpose**: Pre-trained deformability models for tasks involving transformations.\n- **Implementation Details**: The model's backend is fixed to `torch`.\n- **Notable Dependencies**:\n  - No explicit external dependencies listed, but it requires PyTorch (`\"torch\"`).\n  - Inherited interface from `DummyObject` metadata."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2806-2810
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2810, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DeiTForImageClassification", "start_line": 2806, "type": "class", "chroma:document": "CODE:\n\nclass DeiTForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**DeiTForImageClassification Class Summary**\n\n*   **Purpose and Functionality:** \n    A PyTorch implementation of the DeiT (DEIT) architecture for image classification.\n*   **Implementation Details:**\n    *   Utilizes the `torch` backend.\n    *   Requires `torch` as a dependency; specifically, it calls the `requires_backends` function with [\"torch\"] to ensure compatible setup.\n*   **Notable Dependencies and Requirements:**\n    *   Extends the `DummyObject` metaclass for class definition (implying other classes might benefit from this setup).\n    *   References Torch backend (`\"torch\"`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2813-2817
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2817, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DeiTForImageClassificationWithTeacher", "start_line": 2813, "type": "class", "chroma:document": "CODE:\n\nclass DeiTForImageClassificationWithTeacher(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**DeiTForImageClassificationWithTeacher Class Summary**\n\n*Main Purpose:*\nThe `DeiTForImageClassificationWithTeacher` class is an implementation of the DeiT (DEep Idea transformer) model for image classification tasks with teacher forcing.\n\n**Key Functionality:**\nProvides a forward pass interface for the DeiT model, requires the TorCH backend.\n\n**Implementation Details:**\n\n* Inherits from `DummyObject` metaclass\n* Requires the `torch` backend in the constructor\n\nNo notable dependencies or requirements are specified. \n\nNote: The exact implementation of the DeiT model is not provided in this snippet, it's essential to analyze the complete code to understand its behavior and performance."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2820-2824
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2824, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DeiTForMaskedImageModeling", "start_line": 2820, "type": "class", "chroma:document": "CODE:\n\nclass DeiTForMaskedImageModeling(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class DeiTForMaskedImageModeling Summary**\n\n*   **Main Purpose**: A DeiT (Deformed Attention-Shifted) model for masked image modeling tasks, utilizing the PyTorch backend.\n*   **Functionality**: Initializer that requires the PyTorch backend to be installed.\n\n**Key Implementation Details:**\n-   Uses `DummyObject` metaclass.\n-   Initializes with standard arguments (`*args`, `**kwargs`) while requiring PyTorch backend. \n\n**Notable Dependencies and Requirements:**\n-   References `requires_backends` function from the same module.\n-   Depends on the `torch` backend for its primary functionality.\n-   Inherited or implemented interfaces: None mentioned."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2827-2831
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2831, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DeiTModel", "start_line": 2827, "type": "class", "chroma:document": "CODE:\n\nclass DeiTModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DeiTModel` is a container class for PyTorch-based De-it (deberta) model instances. It enforces the use of only the \"torch\" backend.\n\n**Key Details:**\n\n* **Initialization**: The `__init__` method accepts arbitrary arguments (`args`) and keyword arguments (`kwargs`). It requires the `\"torch\"` backend using the `requires_backends` function.\n* **Backend Requirements**: Only the `\"torch\"` backend is supported by default. This suggests that other backends might be added or modified in the future.\n \n\nNote: Without further context, I couldn't determine more details about what methods or properties this class might have."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2834-2838
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2838, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DeiTPreTrainedModel", "start_line": 2834, "type": "class", "chroma:document": "CODE:\n\nclass DeiTPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nClass `DeiTPreTrainedModel` implements a PyTorch-trained model with a metaclass `DummyObject`. The class has an initialization method that checks for the presence of the \"torch\" backend.\n\n*   **Primary Purpose**: Pretrained DeiT model with Torch backend.\n*   **Key Implementation Details**:\n    *   Initializes the model, requiring the `torch` backend.\n*   **Dependencies and Requirements**:\n    *   Tries to validate `requires_backends` method which takes a list of [\"torch\"] as argument."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2841-2845
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2845, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetaForObjectDetection", "start_line": 2841, "type": "class", "chroma:document": "CODE:\n\nclass DetaForObjectDetection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DetaForObjectDetection`**\n\n*   **Purpose:** Object Detection implementation using the `torch` backend.\n\n**Implementation Details:**\n\n*   Requires detection of supported `torch` backend at initialization.\n\n**Dependencies and Requirements:**\n\n*   `torch`: Required for object detection functionality.\n*   `DummyObject`: Metaclass used for class definition."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2848-2852
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2852, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetaModel", "start_line": 2848, "type": "class", "chroma:document": "CODE:\n\nclass DetaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DetaModel`: A class utilizing the `torch` backend with automatic dependency check.\n\n**Key Details:**\n\n* Primary purpose: Utilize `torch` as a required backend.\n* Implementation detail: Checks for activation of `requires_backends` method with valid backend(s).\n\n**Methods and Interfaces:**\n\n* `__init__`: Initializes the instance, requires `torch` backend.\n* `requires_backends`: Verifies activation of the backend with specific dependencies (`\"torch\"`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2855-2859
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2859, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetaPreTrainedModel", "start_line": 2855, "type": "class", "chroma:document": "CODE:\n\nclass DetaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DetaPreTrainedModel` class is a PyTorch-based model that inherits from `DummyObject`. It uses the `torch` backend and enforces it through the `requires_backends` function.\n\n**In Focus:**\n\n* Primary purpose: To provide a PyTorch integration for a Deta pre-trained model.\n* Key implementation details:\n\t+ Enforces `torch` as the required backend using `requires_backends`.\n* Notable dependencies/requirements:\n\t+ `torch`: The specified backend, referenced by exact name."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2862-2866
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2866, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientFormerForImageClassification", "start_line": 2862, "type": "class", "chroma:document": "CODE:\n\nclass EfficientFormerForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `EfficientFormerForImageClassification`**\n\n**Purpose and Functionality:**\nThe `EfficientFormerForImageClassification` class is an image classification model that leverages the EfficientNet architecture with various optimization techniques.\n\n**Key Implementation Details:**\n\n* Initializes with supporting backends, primarily leveraging Torch.\n* Applies a backend check to ensure compatibility before proceeding.\n\n**Notable Dependencies and Requirements:**\n\n* Utilizes `torch` as its primary backend for computation.\n* Inherit(s) from `DummyObject`, indicating an abstract base class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2869-2873
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2873, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientFormerForImageClassificationWithTeacher", "start_line": 2869, "type": "class", "chroma:document": "CODE:\n\nclass EfficientFormerForImageClassificationWithTeacher(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `EfficientFormerForImageClassificationWithTeacher` class is an implementation of the EfficientNet model for image classification with teacher-student training. It utilizes the PyTorch backend.\n\n**Key Details:**\n\n* Primary purpose: Image classification using EfficientNet architecture\n* Key Implementation: Teacher-student training with PyTorch backend\n\n**Notable Dependencies/Requirements:**\n\n* External Dependency: PyTorch (backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2876-2880
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2880, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientFormerModel", "start_line": 2876, "type": "class", "chroma:document": "CODE:\n\nclass EfficientFormerModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `EfficientFormerModel` class is a PyTorch-based model that uses the `DummyObject` metaclass. It initializes with the specified backends (`\"torch\"`), enforcing this backend at runtime.\n\n**Key Implementation Details:**\n\n* Primary purpose: Initialize and enforce the use of the PyTorch backend.\n* Notable dependency:\n\t+ `requires_backends` function, which is called in `__init__`.\n\n**Design Choice:**\nThe use of the `DummyObject` metaclass allows for customized class creation while maintaining type checking and attributes."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2883-2887
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2887, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientFormerPreTrainedModel", "start_line": 2883, "type": "class", "chroma:document": "CODE:\n\nclass EfficientFormerPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\nHere is a concise summary of the provided class code chunk:\n\n**Summary:**\n\n`EfficientFormerPreTrainedModel` is an efficient, pytorch-based model pre-trained with a specific architecture. \n\n* **Primary purpose**: EfficientFormer Pre-Trained Model\n* **Key implementation details**:\n\t+ Implements the requires_backends function to ensure compatibility with torch backend.\n* **Notable dependencies/requirements**:\n\t+ `dummy_object` metaclass (not explicitly listed, but inferred)\n\t+ PyTorch backend (`torch`)\n \n\nNote: The `DummyObject` metaclass is not a standard Python metaclass. It may be defined elsewhere in the codebase and isn't included in this summary as it's not directly related to the model itself."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2890-2894
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2894, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieMForInformationExtraction", "start_line": 2890, "type": "class", "chroma:document": "CODE:\n\nclass ErnieMForInformationExtraction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**ErgoMFor Information Extraction Class Summary**\n\n### Purpose and Functionality\n\nThe `ErnieMForInformationExtraction` class is an instance of the `DummyObject` metaclass. Its primary purpose is to facilitate information extraction using the Ernie M model, leveraging the PyTorch backend.\n\n### Key Implementation Details\n\n*   The `__init__` method initializes the object and confirms that the required Backend (\"torch\") is available.\n*   The `requires_backends` function ensures the correct backend is specified for the object.\n\n### Notable Dependencies or Requirements\n\n*   **Backend:** PyTorch\n*   **Interfaces/Classes:** None explicitly mentioned within this snippet, but assuming interactions with other class or functions defined in the parent metaclass (`DummyObject`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2897-2901
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2901, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieMForMultipleChoice", "start_line": 2897, "type": "class", "chroma:document": "CODE:\n\nclass ErnieMForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ErnieMForMultipleChoice`\n\n* Primary Purpose: Custom multi-choice classification model wrapper for Ernie-M based on PyTorch backend.\n\n**Key Implementation Details:**\n\n* Utilizes `requires_backends` to enforce the use of PyTorch (`torch`) as a mandatory backend.\n* Employs PyTorch's backend architecture.\n \n**Distributed Dependencies/Requirements:**\n \n- Inherited or Implemented Interface(s): None\n- External Dependency: PyTorch (`torch`)\n- Related Class/Function: `requires_backends`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2904-2908
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2908, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieMForQuestionAnswering", "start_line": 2904, "type": "class", "chroma:document": "CODE:\n\nclass ErnieMForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ErnieMForQuestionAnswering` class is a PyTorch-based model designed for question-answering tasks. It inherits from the `DummyObject` metaclass and requires PyTorch as its backend.\n\n**Key Details:**\n\n* Primary purpose: Question-answering task with PyTorch backend.\n* Implementation details:\n\t+ Requires PyTorch 1.9+ using `requires_backends`.\n\t+ No specific algorithm or pattern mentioned.\n\n**Notable Dependencies:**\n\n* PyTorch (version 1.9+) as required backend.\n* `dummy_object' metaclass for class definition.\n \nNote: The `DummyObject` metaclass and the `requiries_backends` function are not part of the provided code snippet, so I couldn't include them in this summary."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2911-2915
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2915, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieMForSequenceClassification", "start_line": 2911, "type": "class", "chroma:document": "CODE:\n\nclass ErnieMForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ErnieMForSequenceClassification` class is an implementation of a sequence classification model using the PyTorch backend. Its primary purpose is to classify input sequences in a natural language processing (NLP) task.\n\n**Key Implementation Details:**\n\n*   Uses the `torch` backend.\n*   Initializes from a sequence of keyword arguments (`args` and `kwargs`) with a validation check for required backends using the `requires_backends` function.\n\n**Notable Dependencies and Requirements:**\n\n*   Relies on PyTorch functionality (`self requires_backends([\"torch\"])`).\n*   Inherited metaclass `DummyObject`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2918-2922
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2922, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieMForTokenClassification", "start_line": 2918, "type": "class", "chroma:document": "CODE:\n\nclass ErnieMForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ErnieMForTokenClassification` is a PyTorch-based token classification model using the Ernie-M architecture. Its primary purpose is **token level sentence classification**.\n\n**Key Implementation Details:**\n\n*   The model is initialized with the `torch` backend using `requires_backends`.\n    *   Key feature: Uses the Ernie-M neural network architecture.\n*   Method Signature:\n    *   `__init__(self, *, **kwargs):`\n\n**Notable Dependencies and Requirements:**\n\n*   Related Classes/Functions:\n    *   `DummyObject`\n    *   `requires_backends`\n*   External Dependencies:\n    *   Torch (`torch`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2925-2929
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2929, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieMModel", "start_line": 2925, "type": "class", "chroma:document": "CODE:\n\nclass ErnieMModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nErnieMModel is a class using the ` DummyObject` metaclass. It initializes with optional arguments (`*args, **kwargs`) and enforces that it uses the \"torch\" backend.\n\n**Key Details:**\n\n- Metaclass: `DummyObject`\n- Primary Purpose: ErnieMModel as a data model with backend support\n- Notable Implementation Detail:\n  - Validation: `requires_backends` function ensures only \"torch\" is used\n\n**Dependencies/Requirements:**\n- `PyTorch`: backend dependency"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2932-2936
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2936, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieMPreTrainedModel", "start_line": 2932, "type": "class", "chroma:document": "CODE:\n\nclass ErnieMPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: Ernie-M Pre-Trained Model**\n\n*   **Purpose and Functionality**: A wrapper class for the Ernie-M pre-trained model, using the `DummyObject` metaclass, with backend support for PyTorch (`torch`).\n*   **Key Implementation Details**:\n    *   Initializes the model instance.\n    *   Verifies that the `torch` backend is required.\n*   **Notable Dependencies and Requirements**:\n    *   `torch`: The PyTorch library is a dependency.\n\nInherited/Implemented Interfaces: None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2939-2943
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2943, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GPTSanJapaneseForConditionalGeneration", "start_line": 2939, "type": "class", "chroma:document": "CODE:\n\nclass GPTSanJapaneseForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`GPTSanJapaneseForConditionalGeneration`\n\n*   A PyTorch-based implementation for conditional generation of Japanese text using GPT-SA models.\n*   The primary purpose is to provide a framework for generating coherent Japanese sentences under specific conditions.\n\n**Key Details**\n\n*   `_backends=[\"torch\"]`: Indicates that the class relies on the PyTorch backend.\n*   `requires_backends(self, [\"torch\"])`: Validates that the required backend is installed before initialization.\n\n**Notable Dependencies/Requirements**\n\n*   None explicit dependencies mentioned in this snippet. However, it implicitly requires the PyTorch backend to be available.\n*   This class may inherit from or implement specific interfaces related to natural language processing or machine learning models, but no interfaces are explicitly referenced here."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2946-2950
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2950, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GPTSanJapaneseModel", "start_line": 2946, "type": "class", "chroma:document": "CODE:\n\nclass GPTSanJapaneseModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`GSPTSanJapaneseModel` is a class representing a GPT-based Japanese language model. The class's primary purpose is to initialize and configure the model.\n\n**Key Implementation Details:**\n\n* Initializes with a list of required backends (`torch`)\n* Verifies backend compliance before instantiation\n* Has `_backends` attribute set to `[\"torch\"]`\n\n**Notable Dependencies or Requirements:**\n\n* Relies on `torch` backend (requires verification prior to instantiation)\n \n\nNote: The exact purpose and functionality are described by using precise technical terms, preserving class names exactly, stating the primary purpose, narrowing down key implementation details, highlighting important method signatures, and noting notable dependencies or requirements. The summary is concise, clean, and focused on the essential aspects of the class code chunk."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2953-2957
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2957, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GPTSanJapanesePreTrainedModel", "start_line": 2953, "type": "class", "chroma:document": "CODE:\n\nclass GPTSanJapanesePreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`GPTSanJapanesePreTrainedModel`: A pre-trained model for Japanese language processing using the GPT architecture.\n\n**Key Functions and Variables:**\n\n* `__init__`: Initializes the model with the specified backends, requiring `torch`.\n* `_backends`: Specifies that only `torch` is supported as a backend.\n\n**Notable Dependencies and Requirements:**\n\n* `requires_backends(self, [\"torch\"])`\n* Depends on:\n\t+ `torch` (backend)\n\t+ `DummyObject` (metaclass)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2960-2964
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2964, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GraphormerForGraphClassification", "start_line": 2960, "type": "class", "chroma:document": "CODE:\n\nclass GraphormerForGraphClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n### GraphormerForGraphClassification Class Overview\n\nThe `GraphormerForGraphClassification` class is a custom graph classification model using PyTorch as its backend.\n\n**Key Features and Implementation Details:**\n\n* Primary purpose: Develops a graph classification model with PyTorch.\n* Key implementation detail: Utilizes the `RequiresBackends` decorator to enforce the use of the Torches backend for execution.\n\n### Class Metadata:\n\n* **Inheritance**: Implements the `DummyObject` metaclass.\n* **Dependent Backends**: Supports only the \"torch\" backend. \n* **Dependencies**:\n    * External Dependencies: PyTorch library (torch).\n    * Implementation Interfaces: None explicitly mentioned.\n\n### Note:\nThe class expects all backends to be specified on initialization, with support limited to PyTorch (`torch`) in this context"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2967-2971
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2971, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GraphormerModel", "start_line": 2967, "type": "class", "chroma:document": "CODE:\n\nclass GraphormerModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**GraphormerModel Class Summary**\n\n### Purpose and Functionality\n`GraphormerModel` is a meta-class-enabled subclass used to configure a graph model using the `torch` backend.\n\n### Key Implementation Details\n\n* Enforces backend configuration: `requires_backends(self, [\"torch\"])` ensures that only the `torch` backend is supported.\n* **Method Signatures:**\n\t+ `__init__`: Initializes the model instance with variable arguments (`args`, `kwargs`) and verifies required backends.\n\n### Notable Dependencies\n\n* `_backends = [\"torch\"]`: Restricts to using the `torch` backend.\n* External dependencies:\n\t+ `torch`: Required for graph operations.\n\n### Inherited or Implemented Interfaces\nNone mentioned, but inherits from `DummyObject` meta-class.\n\nNote: No additional class members or methods are provided in this code snippet. The focus is on understanding the purpose and configuration of the `GraphormerModel` class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2974-2978
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2978, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "GraphormerPreTrainedModel", "start_line": 2974, "type": "class", "chroma:document": "CODE:\n\nclass GraphormerPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`GraphormerPreTrainedModel`: A pre-trained model class that inherits from `DummyObject`, requiring the `torch` backend.\n\n**Key Details:**\n\n*   **Purpose**: Pre-trained model for `Graphormer`.\n*   **Backend Requirements**: `torch`.\n\n**Implementation Details:**\n\n*   `requires_backends(self, [\"torch\"])` ensures the `torch` backend is installed and available.\n*   The class uses a metaclass (`DummyObject`) to define its behavior.\n\n**Notable Dependencies/Requirements:**\n\n*   Requiring the `torch` backend for compatibility.\n*   No external dependencies listed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2981-2985
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2985, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "JukeboxModel", "start_line": 2981, "type": "class", "chroma:document": "CODE:\n\nclass JukeboxModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `JukeboxModel` class is a data model used for configuring Jukebox, utilizing the \"torch\" backend by default. It has an initializer that enforces the use of the \"torch\" backend, ensuring compatibility.\n\n**Implementation Details:**\n\n*   The primary purpose of this class is to act as a configuration model for Jukebox, with the \"torch\" backend being the supported option.\n*   The `_backends` variable uses a list of string values to store the supported backends.\n\n**Key Details:**\n\n*   `__init__(self, *args, **kwargs)`: The initializer method checks that the \"torch\" backend is used when creating a new instance of this model."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2988-2992
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2992, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "JukeboxPreTrainedModel", "start_line": 2988, "type": "class", "chroma:document": "CODE:\n\nclass JukeboxPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Jukebox Pre-Trained Model Class Summary**\n\n**Class Purpose:**\nThe `JukeboxPreTrainedModel` class is a pre-trained model representation for Jukebox, utilizing the PyTorch backend.\n\n**Primary Functionality:**\nIt initializes the model with support for the `torch` backend and enforces its usage through a static method called `requires_backends`.\n\n**Key Implementation Details:**\n\n*   Uses `_backends = [\"torch\"]` to specify the supported backend.\n*   Implements an instance initializer (`__init__`) that requires the `torch` backend using `requires_backends(self, [\"torch\"])`.\n*   Note: The `DummyObject` metaclass is used for this class.\n\n**Notable Dependencies or Requirements:**\n\n*   Related classes/functions: `requires_backends`, `dummy_object`\n*   External dependencies:\n    *   ` Torch` (backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:2995-2999
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 2999, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "JukeboxPrior", "start_line": 2995, "type": "class", "chroma:document": "CODE:\n\nclass JukeboxPrior(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**JukeboxPrior Class Summary**\n\n* **Purpose:** A JukeboxPrior class for inference with the specified backend (default: torch).\n\t+ Primary purpose: Utilize the PyTorch backend for JukeboxPrior operations.\n* **Implementation Details:**\n\t+ Requires PyTorch as a dependency for initialization (`requires_backends` function calls `torch` backend).\n* **Notable Requirements:**\n\t+ External Dependency:\n\t\t- PyTorch (backend)\n\t+ Inherited/Implemented Interfaces:\n\t\t- `_backends` module ( PyTorch configuration)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3002-3006
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3006, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "JukeboxVQVAE", "start_line": 3002, "type": "class", "chroma:document": "CODE:\n\nclass JukeboxVQVAE(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**JukeboxVQVAE Class Summary**\n\n### Purpose and Functionality\nThe JukeboxVQVAE class implements a VQ-VAE (Variational Quantized Variational Autoencoder) model using the PyTorch backend.\n\n### Key Implementation Details\n* Enforces `torch` as a required backend for instantiation.\n* Initiates VQ-VAE model with unspecified arguments (`*args`, `**kwargs`) and backends (`[\"torch\"]`).\n\n### Notable Dependencies or Requirements\n* Requires the `torch` library as a dependency.\n\n### Class Statistics\n\n* Inherited Metaclass: `DummyObject`\n* Backends Implemented: `torch`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3009-3013
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3013, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MCTCTForCTC", "start_line": 3009, "type": "class", "chroma:document": "CODE:\n\nclass MCTCTForCTC(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nMCTCTForCTC is a class that inherits from DummyObject and uses the \"torch\" backend. The class's `__init__` method initializes it with variable arguments and keyword arguments. Specifically, it checks if the specified backends (\"torch\") are available for use.\n\n* **Purpose:** Initialize MCTCTForCTC with backend requirements\n* **Key Features:**\n\t+ Backend: \"torch\"\n\t+ Initialization method with args/kwargs support\n* **Notable Dependencies:**\n\t+ DummyObject (metaclass)\n\t+ requires_backends function"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3016-3020
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3020, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MCTCTModel", "start_line": 3016, "type": "class", "chroma:document": "CODE:\n\nclass MCTCTModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary: MCTCTModel Class**\n\nThe `MCTCTModel` class is a PyTorch backend implementation. It initializes with a set of supported backends (`\"torch\"`), enforcing the use of only one backend at instantiation.\n\n**Key Details:**\n\n*   Primary purpose: PyTorch backend initialization\n*   Implementation: Requires a single backend to be specified (`torch`)\n*   Method Signatures:\n    ```\nrequires_backends(self, [\"torch\"]):\n```\n    *   Verifies that only `torch` backend is used\n\n**Notable Dependencies/Requirements:**\n\n*   InheritedInterface: None\n*   Implemented Interfaces:\n    ```\n metaclass=DummyObject\n```\n    *   Uses `DummyObject` as metaclass for serialization (not directly related to functionality, but worth noting)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3023-3027
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3027, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MCTCTPreTrainedModel", "start_line": 3023, "type": "class", "chroma:document": "CODE:\n\nclass MCTCTPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n*   `MCTCTPreTrainedModel` class with a dummy object-based implementation.\n*   Primary purpose: To provide a pre-trained model for MCTC (Maximum Consistency Thresholding with Context) processing.\n*   **Key Implementation Details:**\n    *   The model requires the `torch` backend to function.\n        +   Method signature: `requires_backends(self, [\"torch\"])`\n*   Notable Dependencies:\n    *   Expects a `torch` backend implementation.\n\n**Additional Notes:** This summary is based on the provided code chunk and may not be exhaustive of all code details."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3030-3034
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3034, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForCausalLM", "start_line": 3030, "type": "class", "chroma:document": "CODE:\n\nclass MegaForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`MegaForCausalLM`: A neural network architecture for Causality-aware Language Models using the `torch` backend.\n\n**Key Implementation Details**\n\n* Uses `torch` as its primary backend\n* Initializes with `requires_backends` function, ensuring compatibility with `[\"torch\"]`\n\n**Notable Dependencies/Requirements**\n\n* Relies on `torch` external library\n* Inherits from unspecified base classes (possibly absent due to absence of metaclass definition)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3037-3041
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3041, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForMaskedLM", "start_line": 3037, "type": "class", "chroma:document": "CODE:\n\nclass MegaForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `MegaForMaskedLM` class is a masked language model implementation using the PyTorch backend. It initializes with support for multiple backends (`requires_backends` function ensures `torch` is supported).\n\n**Key Details:**\n\n* **Main Purpose:** Masked Language Modeling (MLM) with multi-backend support.\n* **Implementation:**\n\t+ `__init__`: Initializes the model, ensuring PyTorch backend support.\n\t+ `_backends`: List of supported backends (`[\"torch\"]`).\n* **Notable Dependencies:**\n\n\t+ `torch`\n\t+ `DummyObject` (metaclass).\n\nNote that this summary does not include any external dependencies or interface implementations not explicitly mentioned in the code snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3044-3048
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3048, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForMultipleChoice", "start_line": 3044, "type": "class", "chroma:document": "CODE:\n\nclass MegaForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`MegaForMultipleChoice` is a class that leverages the `torch` backend with the `DummyObject` metaclass. It initializes with variable arguments and keyword arguments, requiring the presence of the `torch` backend.\n\n**Key Details**\n\n* Metaclass: `DummyObject`\n* Primary Purpose: Utilize `torch` for multiple-choice related tasks\n* Implementation:\n\t+ Requires the `torch` backend be present during instantiation\n\t+ Initializes with variable (`*args`) and keyword arguments (`**kwargs`)\n* Dependencies:\n\t+ `torch`\n\t+ `requires_backends` function"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3051-3055
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3055, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForQuestionAnswering", "start_line": 3051, "type": "class", "chroma:document": "CODE:\n\nclass MegaForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `MegaForQuestionAnswering` class is a PyTorch-based model (specifically, a transformer architecture) designed for question-answering applications. Its primary purpose is to provide contextualized representations of input questions.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires the `torch` backend at instantiation using the `requires_backends` function.\n\n**Notable Dependencies and Requirements:**\n\n* Related classes/function: `DummyObject`, `requires_backends`\n* External dependencies:\n\t+ `torch` library\n\n**Primary Purpose:** Contextualized question answering using transformer architecture."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3058-3062
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3062, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForSequenceClassification", "start_line": 3058, "type": "class", "chroma:document": "CODE:\n\nclass MegaForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**MegaForSequenceClassification Class Summary**\n\n**Main Purpose:** A sequence classification model implementation utilizing the MetaLearner framework with PyTorch backend.\n\n**Key Implementation Details:**\n\n*   Initializes the class with sequence classification capabilities using PyTorch backend.\n*   Verifies that the required backend \"torch\" is present during object creation.\n\n**Notable Dependencies and Requirements:**\n\n*   `MetaLearner` metaclass (built-in)\n*   PyTorch library (`torch`) [External Dependency]"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3065-3069
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3069, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaForTokenClassification", "start_line": 3065, "type": "class", "chroma:document": "CODE:\n\nclass MegaForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `MegaForTokenClassification` class is a custom PyTorch model for token classification tasks. It's defined using the `DummyObject` metaclass.\n\n* Primary purpose: **Implementation of Token Classification Model**\n* Key implementation details:\n\t+ Used backend: Torches (`torch`)\n\t+ Initialization method `__init__` requires the presence of the \"torch\" backend\n\t+ Class uses `requires_backends` method to enforce required backends\n* Notable dependencies and requirements:\n\t+ External dependency: `torch`\n\t+ Implemented interface: None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3072-3076
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3076, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaModel", "start_line": 3072, "type": "class", "chroma:document": "CODE:\n\nclass MegaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**MegaModel Class Summary**\n\n*   **Purpose and Functionality**: MegaModel is a class that utilizes the `torch` backend for various operations. It serves as a central model in the system.\n\n    ```\nMegaModel (class)\n  -- Class metaclass: DummyObject\n  -- Requires torch backend initialization\n```\n\n*   **Key Implementation Details**:\n\n    *   Initializes with arguments using the `__init__` method.\n    *   Calls `requires_backends` method to ensure correct backend is selected (`torch`).\n    \n    ```\nMethods:\n  def __init__(*args, **kwargs):  # Initialize MegaModel\n    requires_backends(self, [\"torch\"])  \n    ```\n\n*   **Notable Dependencies and Requirements**:\n\n    *   Utilizes `torch` as the backend.\n    \n    ```\nExternal Dependencies:\n - torch\n    \nRelated Classes/Functions:\n - DummyObject (metaclass)\n - requires_backends  # Function to ensure correct backend selection\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3079-3083
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3083, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MegaPreTrainedModel", "start_line": 3079, "type": "class", "chroma:document": "CODE:\n\nclass MegaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`MegaPreTrainedModel`\n=====================================\n\nA pre-trained model wrapper class using the torch backend.\n\n**Purpose and Functionality:**\nCreate an instance of a pre-trained model with specified requirements for the `torch` backend.\n\n**Key Implementation Details:**\n\n* Initializes the model with `requires_backends` check, ensuring `torch` is supported.\n* Takes variable arguments (`kwargs`) and keyword-only positional arguments (`args`).\n\n**Dependencies and Requirements:**\n- Requires the `torch` backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3086-3090
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3090, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MMBTForClassification", "start_line": 3086, "type": "class", "chroma:document": "CODE:\n\nclass MMBTForClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThis is a PyTorch-based machine learning model class `MMBTForClassification`. It inherits from `DummyObject` metaclass and supports only the \"torch\" backend.\n\n**Key Details:**\n\n- Primary purpose: Classification task using Meta-BERT Model (MMBT)\n- Supported backend: torch\n- Interface: `requires_backends(self, [\"torch\"])`\n\n**Additional Notes:**\n\n- The model class uses metaclass `DummyObject`.\n- It requires the \"torch\" backend for initialization.\n- No external dependencies are explicitly listed in this code snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3093-3097
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3097, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "MMBTModel", "start_line": 3093, "type": "class", "chroma:document": "CODE:\n\nclass MMBTModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**MMBTModel Class Summary**\n\n*   **Purpose**: Enables model configuration and initialization with specific backends (currently set to \"torch\").\n*   **Key Implementation Details**:\n    *   Requires the presence of the \"torch\" backend at initialization (`requires_backends` method).\n*   **Notable Dependencies/Requirements**:\n    *   Uses `DummyObject` as a metaclass.\n    *   Inherited interfaces and classes not specified in this snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3100-3104
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3104, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ModalEmbeddings", "start_line": 3100, "type": "class", "chroma:document": "CODE:\n\nclass ModalEmbeddings(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n```python\n**Class:** ModalEmbeddings\n**Primary Purpose:** A class for handling modal embeddings with Torch backend.\n\n**Implementation Details:**\n- Initializes with support for torch backend.\n  - `__init__`: Required backend check (\"torch\") performed using `requires_backends` method.\n\n**Notable Dependencies/Requirements:**\n- Torch library (`_backends = [\"torch\"]`)\n```\n\nThis summary captures the essential information about the class, its primary purpose, and key implementation details in a concise manner."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3107-3111
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3111, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NatBackbone", "start_line": 3107, "type": "class", "chroma:document": "CODE:\n\nclass NatBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**NatBackbone Class Summary**\n\n*   **Purpose and Functionality**: The `NatBackbone` class implements a Nat Backbone with the primary functionality of initializing and verifying required backends for data processing tasks.\n\n### Key Implementation Details\n\n*   The class uses the `DummyObject` metaclass, specifying that it supports only the `torch` backend.\n*   In the constructor (`__init__` method), it calls the `requires_backends` function to ensure that the `torch` backend is installed and compatible with the specified environment.\n\n### Notable Dependencies or Requirements\n\n*   External dependency: The class requires the `torch` backend to be installed and accessible for data processing tasks.\n*   Interfaces:\n    *   **requires\\_backends**: A function that checks if a list of backends are available, ensuring compatibility with the current environment."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3114-3118
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3118, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NatForImageClassification", "start_line": 3114, "type": "class", "chroma:document": "CODE:\n\nclass NatForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**NatForImageClassification Class Summary**\n\n*   **Purpose:** Provides the `NatForImageClassification` class, which implements an image classification model using the specified backend (`torch`).\n*   **Key Details:**\n    *   The class uses the `DummyObject` metaclass.\n    *   Requires the `torch` backend for initialization.\n*   **Implementations:**\n    *   Initializes with optional arguments and keyword arguments.\n*   **Dependencies/Requirements:**\n    *   Implies use of the `torch` library.\n    *   Inherently uses class methods or attributes not explicitly defined in this snippet.\n\nPlease note that without further context, some implementation details are left out. For a more extensive analysis, additional information about class attributes, method implementations or other necessary components would be needed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3121-3125
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3125, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NatModel", "start_line": 3121, "type": "class", "chroma:document": "CODE:\n\nclass NatModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`NatModel` class using `DummyObject` metaclass. It initializes with a list of required backends (`[\"torch\"]`) enforced at instantiation.\n\n**Key Details:**\n\n* **Method Signature:** `__init__(*args, **kwargs)`\n* **Implementation:** Initializes instance with backend requirement check.\n\n**Notable Dependencies/Requirements:**\n\n* `DummyObject` (metaclass)\n* `requires_backends` function\n* External dependency: `torch` (backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3128-3132
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3132, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NatPreTrainedModel", "start_line": 3128, "type": "class", "chroma:document": "CODE:\n\nclass NatPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`NatPreTrainedModel` class:\n--------------------\n\n*   **Purpose:** Provides a pre-trained model using the `torch` backend.\n*   **Initial Setup:** Requires the `torch` backend for initialization; it does not support other backends.\n\n**Key Implementation Details:**\n\n*   Uses `DummyObject` metaclass.\n*   Defines `_backends` attribute with `\"torch\"` as the only supported backend.\n\n**Notable Dependencies/Requirements:**\n\n*   Relies on `requires_backends` function for backend handling.\n*   Inherently requires the `torch` library."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3135-3139
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3139, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForMaskedLM", "start_line": 3135, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `NezhaForMaskedLM` class is a PyTorch-based masked language model (MLM) implementation. It establishes the necessity of using the \"torch\" backend and initializes the object.\n\n**Key Highlights:**\n\n*   Metaclass: `DummyObject`\n*   Required Backends: \"torch\"\n*   Method Signatures:\n    *   `requires_backends(self, [...])`: Verifies that at least one required backend (\"torch\") is available.\n*   Inherited Interfaces: None"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3142-3146
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3146, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForMultipleChoice", "start_line": 3142, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`NezhaForMultipleChoice` is a PyTorch-based multiple-choice classification model using the `DummyObject` metaclass.\n\n**Purpose:**\nCreate a model instance with specified backend support for torch.\n\n**Implementation Details:**\n- Uses `_backends` class attribute to store supported backends.\n- Initializes model with variable arguments (`*args`) and keyword arguments (`**kwargs`) in its constructor, ensuring proper backend validation via `requires_backends`.\n\n**Notable Dependencies/Requirements:**\n\n- Torcha (PyTorch) installed as a required backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3149-3153
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3153, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForNextSentencePrediction", "start_line": 3149, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForNextSentencePrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`NezhaForNextSentencePrediction`\n\n**Purpose and Functionality:** \nCustom class implementing a neural network model for next sentence prediction.\n\n**Key Implementation Details:**\n\n* Requires a specific backend (`\"torch\"`) for initialization.\n* Utilizes the `requires_backends` function to enforce this requirement.\n\n**Notable Dependencies or Requirements:**\n\n* Backend dependency on `\"torch\"`.\n* Inheritance and implementation of unspecified interfaces."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3156-3160
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3160, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForPreTraining", "start_line": 3156, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `NezhaForPreTraining` class is a PyTorch-based neural network model designed for pre-training tasks. Its primary purpose is to initialize and verify that the required backends are supported.\n\n**Breakdown**\n\n*   **Main Purpose**: Verifies required backends support.\n*   **Key Implementation Details**:\n    *   Uses `DummyObject` as its metaclass, implying a minimal setup or placeholder for more advanced initialization logic in future versions.\n    *   Initializes itself with various arguments using the `self` parameter.\n    *   Calls the `requires_backends` method to check if the necessary backends (\"torch\" in this case) are available before proceeding further.\n*   **Notable Dependencies/Requirements**:\n    *   Requires PyTorch as its backend, referred to by `self requires_backends[\"torch\"]`.\n    \nIn a RAG system, this summary would be used to provide context about the purpose and key implementation details of the `NezhaForPreTraining` class for developers working on or interacting with the codebase."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3163-3167
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3167, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForQuestionAnswering", "start_line": 3163, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`NezhaForQuestionAnswering`\n\n**Purpose:** \nImplement a question answering model using the `Nezha` library and PyTorch backend.\n\n**Key Implementation Details:**\n\n* Initializes the model with required `torch` backend.\n\n**Notable Dependencies/Requirements:**\n\n* `Nezha` library\n* `torch`\n* (`DummyObject` metaclass)\n\n**Additional Notes:** \nThis class inherits from `DummyObject` metaclass and uses the `requires_backends` function to verify if the system has a compatible backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3170-3174
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3174, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForSequenceClassification", "start_line": 3170, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**NezhaForSequenceClassification Class Summary**\n\n### Main Purpose and Functionality\nThe `NezhaForSequenceClassification` class is implemented using PyTorch as its backend. It's designed for sequence classification tasks.\n\n### Key Implementation Details\n\n*   Initializes with `requires_backends` method, which checks if the required `\"torch\"` backend is supported.\n*   Uses a `DummyObject` metaclass.\n\n### Notable Dependencies or Requirements\n\n*   Requires the PyTorch library (`_backends = [\"torch\"]`)\n*   Replaces the need for importing PyTorch libraries explicitly within the class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3177-3181
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3181, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaForTokenClassification", "start_line": 3177, "type": "class", "chroma:document": "CODE:\n\nclass NezhaForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`NezhaForTokenClassification` is a custom token classification model class using the `torch` backend.\n\n*Primary Functionality:* This class represents a PyTorch-based tokenize classification model.\n*Key Implementation Details:* The `__init__` method enforces the use of `torch` as a required backend for this model, ensuring compatibility with the chosen framework.\n**Notable Dependencies or Requirements:** \n- `torch`: A popular deep learning framework used within this model.\n\n(Note: DummyObject metaclass is used for demonstration purposes and not recommended for actual usage. In general, `metaclass` is typically set to `object`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3184-3188
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3188, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaModel", "start_line": 3184, "type": "class", "chroma:document": "CODE:\n\nclass NezhaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `NezhaModel` class initializes with `torch` backend support. It requires the presence of either `\"torch\"` in its `backends` parameter through the `requires_backends` function.\n\n**Key Details:**\n\n* **Function Signatures:**\n\t+ `__init__(self, *args, **kwargs)` initializes the model instance.\n* **Backing:** Uses `torch` as a backend.\n* **Dependencies/Requirements:**\n\t+ External dependency on `torch`.\n\nNote: `DummyObject` is not analyzed as it's used solely as a metaclass and does not contribute to the class functionality."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3191-3195
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3195, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "NezhaPreTrainedModel", "start_line": 3191, "type": "class", "chroma:document": "CODE:\n\nclass NezhaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `NezhaPreTrainedModel` class is a PyTorch-based pre-trained model, inheriting from `DummyObject`. It supports the \"torch\" backend. The constructor initializes the model with required dependencies for the 'torch' backend.\n\n**Key Details:**\n\n* Primary purpose: Pre-trained model for PyTorch\n* Implementation details:\n  + Requires the \"torch\" backend\n* Notable dependencies:\n    * `torch`\n    * `DummyObject` (inherited from)\n* Dependencies:\n  + `requires_backends(self, [\"torch\"])`\n\nNote: Without additional code context, it's difficult to determine specific implementation details. However, this summary should provide a clear understanding of the model's purpose and dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3198-3202
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3202, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "OpenLlamaForCausalLM", "start_line": 3198, "type": "class", "chroma:document": "CODE:\n\nclass OpenLlamaForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `OpenLlamaForCausalLM`**\n\n*Purpose:* Encapsulates the Open Llama model for Causal Language Modeling.\n\n**Main Purpose and Functionality:**\nThe class is designed to work with the torch backend, providing an interface for causal language modeling tasks. It initializes itself with various optional arguments.\n\n**Key Implementation Details:**\n\n* The `__init__` method ensures that the required torch backend is available.\n* The `requires_backends` function (not shown in this snippet) checks if the specified backends are installed and configured properly.\n\n**Notable Dependencies or Requirements:**\n\n* Torch backend (`torch`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3205-3209
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3209, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "OpenLlamaForSequenceClassification", "start_line": 3205, "type": "class", "chroma:document": "CODE:\n\nclass OpenLlamaForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`OpenLlamaForSequenceClassification` class.\n\n**Main Purpose and Functionality:**\nA PyTorch-based sequence classification model wrapper.\n\n**Key Implementation Details:**\n\n* Initializes the model with specific backend requirements (`torch`)\n* Calls `requires_backends` function to validate backend support\n\n**Notable Dependencies and Requirements:**\n\n* torch library (backend)\n\nNote: The metaclass used is `DummyObject`, but its purpose is not explicitly mentioned in the provided code snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3212-3216
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3216, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "OpenLlamaModel", "start_line": 3212, "type": "class", "chroma:document": "CODE:\n\nclass OpenLlamaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `OpenLlamaModel`**\n\n**Main Purpose and Functionality:** \nThe `OpenLlamaModel` class is an implementation of a machine learning model using the OpenLMMA framework. It initializes the model with support for the PyTorch backend.\n\n**Key Implementation Details:**\n\n* Initializes the model with optional arguments (`args`) and keyword arguments (`kwargs`).\n* Verifies that the PyTorch backend is required for this model instance using the `requires_backends` function.\n\n**Notable Dependencies or Requirements:**\n\n* Uses the OpenLMMA framework.\n* Requires PyTorch as a backend implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3219-3223
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3223, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "OpenLlamaPreTrainedModel", "start_line": 3219, "type": "class", "chroma:document": "CODE:\n\nclass OpenLlamaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `OpenLlamaPreTrainedModel` class is a meta-class-based model with a specific backend requirement. It initializes the instance with additional arguments (`*args`, `**kwargs`) and asserts that the \"torch\" backend is required (and implicitly provided within the class's metaclass implementation).\n\n**Key Implementation Details:**\n\n- **Metaclass**: Using the `DummyObject` metaclass to define the `_backends` attribute, implying implicit initialization of the backend.\n- **Backend Requirements**: Asserting the need for the \"torch\" backend during instance creation.\n\n**Notable Dependencies or Requirements:**\n\n- `requites_backends`: A function that enforces a specific backend requirement (in this case, `\"torch\"`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3226-3230
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3230, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForMaskedLM", "start_line": 3226, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: QDQBertForMaskedLM**\n\n### Purpose and Functionality\n\n* `QDQBertForMaskedLM` is a masked language model based on BERT, utilizing the PyTorch backend.\n\n### Key Implementation Details\n\n* Initializes the model with support for the \"torch\" backend.\n* Requires both positional and attention masks in the input data.\n\n### Notable Dependencies and Requirements\n\n* Uses `RequiresBackends` to enforce use of the \"torch\" backend.\n* Inherths from a parent class (`DummyObject`) without explicitly mentioning its exact nature."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3233-3237
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3237, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForMultipleChoice", "start_line": 3233, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\nThe `QDQBertForMultipleChoice` class is a custom BERT model for multiple-choice tasks, utilizing the PyTorch backend (`torch`). It inherits from a dummy object metaclass and provides an initializer that requires the 'torch' backend.\n\n**Key Details:**\n\n* **Primary Purpose:** Custom BERT model for multiple-choice tasks.\n* **Implementation Details:**\n\t+ Utilizes the 'torch' backend for inference.\n\t+ The `requires_backends` function is used to enforce the required backend.\n* **Notable Dependencies:**\n\t+ Related classes/functions: None specified in this snippet, but implied by the use of PyTorch and the dummy object metaclass.\n\t+ External dependencies:\n\t\t- 'torch'\n\t+ Implemented interfaces: None specified.\n\n**Context:**\n\nThis class is likely part of a larger NLP project for multiple-choice questions. The custom BERT model would be used to classify input texts as correct or incorrect based on the provided choices."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3240-3244
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3244, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForNextSentencePrediction", "start_line": 3240, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForNextSentencePrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`QDQBertForNextSentencePrediction` class:\n\n* Main purpose: Implementation of next sentence prediction model using BERT, leveraging PyTorch backend (`\"torch\"`).\n* Key implementation details:\n\t+ Uses `torch` as primary backend.\n\t+ Requires `torch` to be present for instantiation (`requires_backends` check).\n* Dependencies:\n\t+ `DummyObject` metaclass (not specified in class definition, assumed to be defined elsewhere)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3247-3251
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3251, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForQuestionAnswering", "start_line": 3247, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`QDQBertForQuestionAnswering` is a PyTorch-based class for Question Answering (QA) using BERT architecture. The primary purpose is to facilitate QA tasks by providing pre-trained language model weights.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires the `torch` backend to be available at instantiation (`requires_backends`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3254-3258
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3258, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForSequenceClassification", "start_line": 3254, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`QDQBertForSequenceClassification`: A PyTorch-based sequence classification model, leveraging BERT architecture.\n\n**Key Implementation Details**\n\n* Initializes with `torch` backend requirement\n* Utilizes the BERT architecture for sequence classification tasks\n\n**Notable Dependencies/Requirements**\n\n* `_backends = [\"torch\"]`: Relies on the Torcha library.\n* `requires_backends(self, [\"torch\"])`: Initializes model with Torch as a required dependency.\n\n**Note**: The class relies on BERT architecture but does not provide further details about its internal workings."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3261-3265
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3265, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertForTokenClassification", "start_line": 3261, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nQDQBertForTokenClassification Class:\n\n* Primary purpose: A PyTorch-based token classification model inspired by BERT architecture.\n* Implements the `DummyObject` metaclass and relies on the \"torch\" backend.\n\n**Implementation Details:**\n\n* Initializes with arguments `args` and keyword arguments `kwargs`.\n* Requires the \"torch\" backend for instantiation.\n\n**Notable Dependencies/Requirements:**\n\n* Relies on the \"torch\" backend.\n* Uses the ` DummyObject` metaclass, specific to PyTorch."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3268-3272
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3272, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertLMHeadModel", "start_line": 3268, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertLMHeadModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `QDQBertLMHeadModel` class is a model head component for BERT (Bidirectional Encoder Representations from Transformers) with a specific configuration. It utilizes the PyTorch backend.\n\n* Primary purpose: Implements a customized model head for BERT-based language models.\n* Key implementation details:\n\t+ Utilizes `torch` as the backend, specifically through the `DummyObject` metaclass.\n\t+ Initializes with variable arguments (`args`, `kwargs`) via the `__init__` method.\n* Notable dependencies and requirements:\n\t+ Requires the `torch` backend to function."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3275-3279
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3279, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertModel", "start_line": 3275, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `QDQBertModel` class is a PyTorch-based implementation of the BERT algorithm for Natural Language Processing tasks. The model uses the \"torch\" backend and relies on external libraries for compatibility.\n\n**Key Implementation Details**\n\n* Inherents from `DummyObject` metaclass.\n* Requires `\"torch\"` backend for initialization.\n* Uses `requires_backends` function to ensure compatible backends are available.\n\n**Notable Dependencies and Requirements**\n\n* External dependencies:\n\t+ \"torch\"\n* Implemented interfaces:\n\t+ No explicit interfaces mentioned."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3282-3286
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3286, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "QDQBertPreTrainedModel", "start_line": 3282, "type": "class", "chroma:document": "CODE:\n\nclass QDQBertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThis is a customized BERT pre-training model class `QDQBertPreTrainedModel`. It inherits from an unspecified parent class using the `DummyObject` metaclass.\n\n**Key Details**\n\n* The model requires the `torch` backend and ensures its availability using the `requires_backends` function.\n* The model's primary purpose is to provide a customized BERT pre-training framework.\n\n**Notable Dependencies/Requirements**\n\n* Backend: `torch`\n* Parent class: unspecified (`DummyObject`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3289-3290
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3290, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_qdqbert", "start_line": 3289, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_qdqbert(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`load_tf_weights_in_qdqbert` loads weight files into the Qdqbert model. It leverages `load_tf_weights_in_qdqbert` backend to handle Torch weights.\n\n**Detailed Analysis:**\n\n- **Purpose:** Loads pre-trained weight files for the Qdqbert model.\n- **Functionality:**\n  - Utilizes the `load_tf_weights_in_qdqbert` backend specifically designed for handling Torch-related data.\n- **Key Implementation Details:**\n  - The function relies on the `load_tf_weights_in_qdqbert` backend to manage Torch weight loading.\n  - Implementation details related to loading and processing weights are not explicitly defined in this code snippet, but can be inferred through its usage of the backend.\n  \n**Notable Dependencies or Requirements:**\n\n- `load_tf_weights_in_qdqbert` backend for handling Torch-related data.\n- The Qdqbert model architecture (`not directly shown in this code snippet`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3293-3297
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3297, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmEmbedder", "start_line": 3293, "type": "class", "chroma:document": "CODE:\n\nclass RealmEmbedder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThis `RealmEmbedder` class is designed to embed realm models using the Torch backend. The primary purpose is to handle initialization and backend requirements.\n\n**Key Details:**\n\n* Class Method Signature: `__init__(self, *args, **kwargs)`\n* Backend Requirement Enforcement: The `requires_backends` function checks if the required backend (\"torch\") is provided at instantiation.\n* Inherited Metaclass: `_backends = [\"torch\"]`\n\n**Notable Dependencies/Requirements:**\n\n* Torch frontend is required\n* `DummyObject` metaclass (imported in class definition)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3300-3304
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3304, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmForOpenQA", "start_line": 3300, "type": "class", "chroma:document": "CODE:\n\nclass RealmForOpenQA(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `RealmForOpenQA` class is a backend-specific implementation for the Realm ORM framework. It defines a single backend, \"torch\", and initializes all instances with this backend.\n\n**Key Details**\n\n* Metaclass: `DummyObject`\n* Backend: `\"torch\"`\n* Initialization:\n\t+ Requires all backends to be set (inherited requirement)\n\t+ Only specifies a required backend: `\"torch\"`\n\n**Notable Dependencies/Requirements**\n\n* Inherited requirements from `requires_backends`:\n\t+ All backends must be set (currently only `\"torch\"` is specified)\n* External dependency:\n\t+ None explicitly stated, but may require Realm ORM framework implementation details"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3307-3311
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3311, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmKnowledgeAugEncoder", "start_line": 3307, "type": "class", "chroma:document": "CODE:\n\nclass RealmKnowledgeAugEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `RealmKnowledgeAugEncoder` class is a custom encoder for augmented knowledge tasks. It utilizes the `torch` backend and requires it at initialization.\n\n**Key Points:**\n\n* **Class Structure**: Implements `DummyObject` metaclass\n* **Primary Purpose**: Augmented knowledge encoding with `torch` backend\n* **Dependencies**:\n\t+ External Dependency: `torch` (backend)\n* **Implementation Details**:\n\t+ Requires `torch` backend setup in constructor\n\nThis summary aims to capture the essential information about this code chunk, focusing on its purpose, structure, and key dependencies, while omitting generic or unnecessary details."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3314-3318
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3318, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmPreTrainedModel", "start_line": 3314, "type": "class", "chroma:document": "CODE:\n\nclass RealmPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`RealmPreTrainedModel`\n\n* Purpose: A custom PyTorch model class for `realm` framework using pre-trained models.\n\n**Key Implementation Details:**\n\n- Uses only the PyTorch backend.\n- Initializes with any available command-line arguments (`*args`, **kwargs`).\n\n**Dependencies/Requirements:**\n\n- Requires the `torch` backend to be present. \n\nNo notable external dependencies are mentioned, nor does it inherit or implement any known interfaces."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3321-3325
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3325, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmReader", "start_line": 3321, "type": "class", "chroma:document": "CODE:\n\nclass RealmReader(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\nThe `RealmReader` class serves as an abstraction layer for interacting with various data storage backends. It has a fixed backend of \"torch\" and enforces this requirement during initialization.\n\n**Key Details**\n\n*   Initializes with `requires_backends(self, [\"torch\"])`, ensuring only the specified backend is used.\n*   Uses `DummyObject` as its metaclass, which seems to be a placeholder or mock implementation.\n\n**Notable Dependencies/ requirements**\n\n*   Requires \"torch\" as its supported backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3328-3332
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3332, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmRetriever", "start_line": 3328, "type": "class", "chroma:document": "CODE:\n\nclass RealmRetriever(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`RealmRetriever` class:\n\n* Primary purpose: A retriever for retrieving data from various backends, in this case, only `torch`.\n \n* Key implementation details:\n  - Enforces backend requirements using the `requires_backends` function with specific requirements.\n \n\n* Dependencies and requirements:\n  - Utilizes `DummyObject` metaclass.\n  - Requires `torch` as a backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3335-3339
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3339, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RealmScorer", "start_line": 3335, "type": "class", "chroma:document": "CODE:\n\nclass RealmScorer(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`RealmScorer` class with a metaclass of `DummyObject`. The class initializes the backend support for PyTorch.\n\n*   **Purpose:** Provides scoring functionality using specified backends (currently supports PyTorch).\n*   **Key implementation details:**\n    *   Initializes backend support for 'torch' using the `requires_backends` function.\n*   **Notable dependencies and requirements:**\n    *   Uses `DummyObject` metaclass.\n    *   Depends on `requires_backends` function with the keyword argument: \"torch\"\n   "}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3342-3343
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3343, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_realm", "start_line": 3342, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_realm(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `load_tf_weights_in_real` function (corrected spelling) is used to load pre-trained TensorFlow weights specifically for the PyTorch backend. \n\nIt has dependencies on the `load_tf_weights` function and requires the `torch` backend.\n\n**Key Details:**\n\n* Function signature: `load_tf_weights_in_realm(*args, **kwargs)`\n* Backend dependency: `torch`\n* Primary purpose: Loads pre-trained TensorFlow weights for PyTorch."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3346-3350
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3350, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RetriBertModel", "start_line": 3346, "type": "class", "chroma:document": "CODE:\n\nclass RetriBertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`RetriBertModel` is a `dummy object metaclass` used to create BERT models, specifically supporting the `torch` backend. The class initializes with required backends, enforcing compatibility.\n\n**Implementation Details**\n\n- Uses `DummyObject` as its metaclass.\n- Requires `torch` backend for initialization.\n\n**Notable Dependencies/Requirements**\n\n* `requires_backends` function, imported but not shown in this code snippet.\n* `torch` backend implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3353-3357
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3357, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "RetriBertPreTrainedModel", "start_line": 3353, "type": "class", "chroma:document": "CODE:\n\nclass RetriBertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**RetriBertPreTrainedModel Class Summary**\n\n*   **Main Purpose:** A class providing a pre-trained model for the RetriBert architecture, built on top of the `torch` backend.\n*   **Key Implementation Details:**\n    *   Initializes the model with custom arguments (`__init__` method).\n    *   Verifies the availability of the required `torch` backend using `requirements_backends`.\n*   **Notable Dependencies or Requirements:**\n    +   External Dependency: torch\n*   **Additional Notes:** The class utilizes the PyTorch framework (torch backend) and is built using a custom metaclass (`DummyObject`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3360-3364
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3364, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Speech2Text2ForCausalLM", "start_line": 3360, "type": "class", "chroma:document": "CODE:\n\nclass Speech2Text2ForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Speech2Text2ForCausalLM` class:\n \n* A PyTorch-based model for speech-to-text tasks using causal Language Models (cLM).\n* Initializes with support for only \"torch\" backend.\n\nKey Implementation Details:\n\n* Requires PyTorch (`requires_backends(self, [\"torch\"])`) for native implementation.\n\nNotable Dependencies/Requirements:\n\n* `torchnet` (dummy metaclass): `DummyObject`\n               "}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3367-3371
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3371, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Speech2Text2PreTrainedModel", "start_line": 3367, "type": "class", "chroma:document": "CODE:\n\nclass Speech2Text2PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Speech2Text2PreTrainedModel Class Summary**\n\n*Main Purpose:* A pre-trained model for converting speech to text, utilizing the Torch backend.\n\n**Key Implementation Details:**\n\n* Requires the `torch` backend engine.\n* Initializes with variable arguments (`*args`) and keyword arguments (`**kwargs`).\n\n*Note:* The `requires_backends(self, [\"torch\"])` function is not shown in this code snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3374-3378
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3378, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TrajectoryTransformerModel", "start_line": 3374, "type": "class", "chroma:document": "CODE:\n\nclass TrajectoryTransformerModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**TrajectoryTransformerModel Summary**\n\n### Purpose and Functionality\nThe `TrajectoryTransformerModel` class is a machine learning model utilizing the `torch` backend for trajectory prediction tasks. It inherits from a metaclass (`DummyObject`) and initializes with optional arguments.\n\nPrimary purpose: Implementing a transformer-based model for trajectory prediction.\n\n### Key Implementation Details\n\n* Utilizes the `torch` backend.\n* Requires the `torch` backend for initialization.\n\n### Notable Dependencies/Requirements\n- Uses `torch` as the required backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3381-3385
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3385, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TrajectoryTransformerPreTrainedModel", "start_line": 3381, "type": "class", "chroma:document": "CODE:\n\nclass TrajectoryTransformerPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\nHere is a concise and focused summary of the provided class code chunk:\n\n**TrajectoryTransformerPreTrainedModel Class Summary**\n\nPurpose: A pre-trained transformer model optimized for trajectory processing.\n\nKey Implementation Details:\n\n* Uses the `torch` backend.\n* Requires initialization with compatible backends due to inherited dependencies from the parent `metaclass`.\n\nNotable Dependencies/Requirements:\n\n* `torch`\n* Inherited from `DummyObject`: \n    - `metaclass`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3388-3392
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3392, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "AdaptiveEmbedding", "start_line": 3388, "type": "class", "chroma:document": "CODE:\n\nclass AdaptiveEmbedding(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`AdaptiveEmbedding`\n\nA custom class extending `DummyObject`, used for adaptive embedding functionality. It requires the use of the \"torch\" backend.\n\n**Main Purpose and Functionality:**\n\nUtilizes the `requires_backends` function to enforce the use of the specified backend, in this case, \"torch\". The class facilitates adaptive embedding with the chosen backend.\n\n**Key Implementation Details:**\n\n- Extends `DummyObject`\n- Requires the use of the \"torch\" backend\n\n**Notable Dependencies and Requirements:**\n\n- Uses the `requires_backends` function\n- Relies on the \"torch\" backend"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3395-3399
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3399, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TransfoXLForSequenceClassification", "start_line": 3395, "type": "class", "chroma:document": "CODE:\n\nclass TransfoXLForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `TransfoXLForSequenceClassification` class is a custom transformer-based model. It inherits from a dummy metaclass (`DummyObject`) and is designed for sequence classification tasks.\n\n**Key Details:**\n\n* Primary Purpose: Sequence classification using transformer architecture (`TransfoXL`)\n* Backend Requirement: Supports Torch backend\n* Implementation Details:\n\t+ `requires_backends` function ensures the use of Torch as a required backend\n* Notable Dependencies:\n\t+ Inherits from `DummyObject` metaclass\n\t+ Exposes no external dependencies or interfaces"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3402-3406
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3406, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TransfoXLLMHeadModel", "start_line": 3402, "type": "class", "chroma:document": "CODE:\n\nclass TransfoXLLMHeadModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`TransfoXLLMHeadModel`\n\nThis class represents a transformer-based language model with the `torch` backend.\n\n**Key Details:**\n\n* Initializes with support for only the \"torch\" backend.\n* Requires backends to be set to \"torch\" during object creation.\n \n**Implementation:**\n\n\nNo additional implementation details are provided in this snippet. However, it is worth noting that a typical implementation would follow the constructor and define subsequent methods or attributes.\n\n \n\nNote: I have preserved the original class name structure, used precise technical terms, and highlighted key algorithm/implementation detail such as the backend requirement to 'torch'."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3409-3413
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3413, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TransfoXLModel", "start_line": 3409, "type": "class", "chroma:document": "CODE:\n\nclass TransfoXLModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`TransfoXLModel` class implements a machine learning model using the `torch` backend. \n\n**Key Details:**\n- Initializes with a list of required backends (`requires_backends` method)\n- Accepted arguments: variable positions and keywords\n- Primary function: setting up the model for execution\n\n**Implementation Insights:**\n- Leverages `DummyObject` as its metaclass.\n- Depends on the `torch` backend for implementation. \n\n**External Dependencies:**\n- `torch`\n- `dummy_object` (presumably a built-in Python module or custom library)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3416-3420
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3420, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TransfoXLPreTrainedModel", "start_line": 3416, "type": "class", "chroma:document": "CODE:\n\nclass TransfoXLPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `TransfoXLPreTrainedModel` is a class implementing a pre-trained transformer model (`TransfoXL`) using the `torch` backend.\n\n**Key Implementation Details:**\n\n* `__init__`: Initializes the model, ensuring it requires only the `torch` backend.\n\t+ Method signature: no explicit signature provided\n* Notable dependencies:\n\t+ `requires_backends(self, [\"torch\"])`\n\n**Notable Inheritance/Interfaces:**\nNone mentioned in this snippet, but likely inherits from a base model class.\n\n**External Dependencies:** \n`torch`, presumably as a required backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3423-3424
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3424, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_transfo_xl", "start_line": 3423, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_transfo_xl(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nFunction `load_tf_weights_in_transfo_xl` loads TF weights using TransFOXL library. It relies on the `load_tf_weights_in_transfo_xl` backend, which is required to be configured for use with PyTorch backend.\n\n* Primary purpose: Loads TF weights\n* Key implementation detail: Uses `TransFOXL` library behind the scenes.\n* Notable dependencies:\n\t+ `torch`: backend dependency\n\t+ `load_tf_weights_in_transfo_xl` (backend class): required configuration and utility function."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3427-3431
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3431, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TvltForAudioVisualClassification", "start_line": 3427, "type": "class", "chroma:document": "CODE:\n\nclass TvltForAudioVisualClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n* Class Name: `TvltForAudioVisualClassification`\n* Primary Purpose: Instantiation of the TVL (Tensor Virtual Lab) model for audio-visual classification using Torch backend.\n* Key Implementation Details:\n\t+ Requires Torch backend (`requires_backends` method)\n\t+ Uses Torch in the construction of the `__init__` method\n* Notable Dependencies/Requirements:\n\t+ Torch framework (`_backends = [\"torch\"]`)\n\t+ An external library or module for using the TVL model (not specified in this snippet)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3434-3438
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3438, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TvltForPreTraining", "start_line": 3434, "type": "class", "chroma:document": "CODE:\n\nclass TvltForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `TvltForPreTraining`**\n\n* **Main Purpose and Functionality**: A pre-training model class `TvltForPreTraining` that utilizes the Torch backend for its operations.\n\n* **Key Implementation Details**:\n\t+ Initializes with optional arguments (`args`) and keyword arguments (`kwargs`).\n\t+ Sets the required backends to [\"torch\"] using the `requires_backends` method.\n\n* **Notable Dependencies or Requirements**:\n\t+ Inheriting from `DummyObject`.\n\t+ Dependence on the Torch backend.\n\t+ Usage of `requires_backends` method for backend checking."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3441-3445
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3445, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TvltModel", "start_line": 3441, "type": "class", "chroma:document": "CODE:\n\nclass TvltModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `TvltModel` class utilizes the `torch` backend. The constructor (`__init__`) verifies that the required backend is present before initialization.\n\n**Key Details:**\n\n*   Backend: `torch`\n*   Constructor (Verify) Parameters:\n    *   `self`: reference to the instance\n        +   Used as implicit first argument in Python methods, equivalent to `args[0]` or `kwargs[\"self\"]`.\n     *   `*args` and `**kwargs` for additional arguments (not used)\n\n**Note:** The metaclass is set to `DummyObject`, but its implementation is not shown."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3448-3452
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3452, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "TvltPreTrainedModel", "start_line": 3448, "type": "class", "chroma:document": "CODE:\n\nclass TvltPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `TvltPreTrainedModel` class is a PyTorch-based pre-trained model class. It initializes with the following properties:\n\n*   **Backends:** Supports Python backend.\n*   **Initialization Requirements:**\n    *   Requires the support of at least two backends, specifically Python and Torch (verified by `requires_backends(self, [\"torch\"])` method).\n \n\n**Key Implementation Details:**\n\n*   `_backends = [\"torch\"]`: Specifies the supported backends for this model.\n*   `__init__(self, *args, **kwargs)`: Initializes the class with arguments.\n\n**Notable Dependencies or Requirements:**\n\n*   **pytorch**: The class specifically requires the torch backend to be supported. However, it does not explicitly require pytorch as a separate dependency in its summary; it assumes that 'torch' is included under the pytorch back-end of this problem context.)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3455-3459
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3459, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "VanForImageClassification", "start_line": 3455, "type": "class", "chroma:document": "CODE:\n\nclass VanForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`VanForImageClassification` is an image classification model class using the `torch` backend.\n\n**Key Implementation Details:**\n\n- Uses the `VAN For` algorithm for image classification.\n- Requires `torch` backend, explicitly specified in the constructor (`self.requires_backends([\"torch\"])`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3462-3466
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3466, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "VanModel", "start_line": 3462, "type": "class", "chroma:document": "CODE:\n\nclass VanModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`VanModel` is an implementation of a vanilla version of a model using the Torch backend, leveraging the `requires_backends` function to validate the required backend.\n\n**Key Implementation Details:**\n\n* Uses the Torch backend (`_backends = [\"torch\"]`)\n* Verifies compatibility with the specified backend in the `__init__` method\n\n**Notable Dependencies/Requirements:**\n\n* Requires the `DummyObject` metaclass\n* Assumes the existence of a `requires_backends` function for validation\n\nNo external dependencies are listed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3469-3473
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3473, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "VanPreTrainedModel", "start_line": 3469, "type": "class", "chroma:document": "CODE:\n\nclass VanPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nClass `VanPreTrainedModel` extends `DummyObject`. It inherits from the following back-end:\n\n*   torch\n\nThe `__init__` method ensures that the required back-end is present (`torch`) at instantiation.\n\nKey aspects:\n\n*   Class name: `VanPreTrainedModel`\n*   Back-end support: `torch`\n\n**Detailed Code:**\n\n```python\nclass VanPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```\n\n**Dependencies/Requirements:**\n\n*   torch (back-end)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3476-3480
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3480, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ViTHybridForImageClassification", "start_line": 3476, "type": "class", "chroma:document": "CODE:\n\nclass ViTHybridForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ViTHybridForImageClassification` class is an image classification model using the PyTorch backend. It enforces the use of the PyTorch backend during initialization.\n\n**Key Details:**\n\n* Class: `ViTHybridForImageClassification`\n* Backend: PyTorch (`self._backends = [\"torch\"]`)\n* Primary Purpose: Image Classification\n\n**Implementation Details:**\n\n* Initialization Method: `__init__` with variable arguments and keyword arguments, enforcing the presence of the PyTorch backend.\n\n**Notable Dependencies:**\n\n* Backend: PyTorch (`requires_backends(self, [\"torch\"])`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3483-3487
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3487, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ViTHybridModel", "start_line": 3483, "type": "class", "chroma:document": "CODE:\n\nclass ViTHybridModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `ViTHybridModel` class is a hybrid machine learning model implementation using the PyTorch backend. It inherits from a metaclass `DummyObject`.\n\n*   **Purpose**: The primary purpose of this class is to provide a framework for creating and training hybrid machine learning models.\n*   **Key Implementation Details**:\n    *   The model requires at least one backend, which is \"torch\" in this case.\n*   **Notable Dependencies or Requirements**:\n    *   `requires_backends` function with PyTorch as one of the required backends.\n\n(Note: No external dependencies are explicitly listed, assuming PyTorch and the `DummyObject` metaclass are already installed.)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3490-3494
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3494, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ViTHybridPreTrainedModel", "start_line": 3490, "type": "class", "chroma:document": "CODE:\n\nclass ViTHybridPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ViTHybridPreTrainedModel` class is a hybrid model based on the ViT architecture. It has Torch as its primary backend.\n\n**Key Details:**\n\n* Primary purpose: Implementing Hybrid Vision Transformer (ViT) model with Torch backend\n* Important method signature:\n  - `__init__`: Initializes the model, including dependency check on Torch backend\n* Notable dependencies:\n  - `torch` (backend)\n  - `DummyObject` metaclass\n\n**Implementation Insights:**\n\nThe class uses a metaclass (`DummyObject`) and includes a check for required Torch backend in its constructor."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3497-3501
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3501, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetDecoder", "start_line": 3497, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetDecoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`XLMProphetNetDecoder`\n\n*Purpose:* A decoder class for ProphetNet models in the XLM framework.\n \n**Key Implementation Details:**\n- Initializes with `requires_backends` method to ensure required backend is installed (`torch`).\n- Utilizes `DummyObject` metaclass, but its actual functionality remains undefined.\n\n**Notable Dependencies/Requirements:**\n\n* Backend: `torch`\n \n**Note:** This analysis only covers the provided code snippet and may not include all possible implementation details or usage contexts."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3504-3508
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3508, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetEncoder", "start_line": 3504, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\nXLMProphetNetEncoder Class\n\n*   A class that leverages the PyTorch backend for encoding tasks.\n*   Initializes with configuration arguments `*args` and `**kwargs`.\n*   Requires the PyTorch backend to be installed (`requires_backends(self, [\"torch\"])`). \n\n**Key Details:**\n\n*   The class uses a metaclass (`DummyObject`) but its purpose is unknown without more context or implementation details.\n*   The `requires_backends` function (not shown) implies some backend-specific requirements.\n\n**Dependencies and Requirements:**\n\n*   PyTorch library for encoding tasks (`\"torch\"`).\n*   `requires_backends` function, possibly implementing backend installation checks."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3511-3515
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3515, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetForCausalLM", "start_line": 3511, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\nHere is a concise summary of the given class code chunk:\n\n**Class XLMProphetNetForCausalLM**\n\n*   Main purpose: Provides an implementation of a neural network model using the ProphetNet architecture, specifically for causal language models (CaLMs), on top of the Transformers library with PyTorch backend.\n*   Key Implementation Details:\n    *   Uses the ProphetNet architecture.\n    *   Requires PyTorch as its backend.\n    *   Initializes the model instance with variable arguments and keyword arguments (`*args` and `**kwargs`).\n*   Notable Dependencies:\n    *   `torch`: Required backend library for this model.\n\nNote: As the provided code does not define any methods, attributes or interfaces, no additional information can be extracted from it."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3518-3522
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3522, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetForConditionalGeneration", "start_line": 3518, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`XLMProphetNetForConditionalGeneration`: A PyTorch-based model (backend: `torch`) for conditional generation.\n\n**Key Features**\n\n- Class uses `DummyObject` metaclass\n- Reqs backend support for torch (`requires_backends(self, [\"torch\"])`)\n\nNo implementation details are provided. The code initializes the class with optional arguments `*args` and `**kwargs`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3525-3529
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3529, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetModel", "start_line": 3525, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `XLMProphetNetModel` class is a custom neural network model using the PyTorch backend. It initializes with the specified backends (`\"torch\"`). The model requires the PyTorch library for its computation.\n\n**Detailed Analysis:**\n\n*   **Main Purpose and Functionality:** This class represents a custom Prophet Net model, which is likely used in Natural Language Processing (NLP) applications.\n*   **Key Implementation Details:**\n    *   `requires_backends(self, [\"torch\"])`: Verifies that the PyTorch backend is available before creating an instance of the model.\n    *   Backend: `torch` - Specifies that this model uses the PyTorch library for its computation.\n*   **Notable Dependencies or Requirements:**\n    *   `DummyObject` (metaclass): Used to define the metaclass for the `XLMProphetNetModel` class. This indicates a custom approach to creating instances of this model.\n    *   `Provides` (method call): Used to create an instance of this model, ensuring that the required backends are available before instantiation.\n\n**RAG System Summary:**\n\n*   Primary Purpose: Custom Prophet Net model using PyTorch backend\n*   Key Implementation Details: requires_backends with torch backend dependency.\n*   Notable Dependencies: DummyObject metaclass and provides method."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3532-3536
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3536, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "XLMProphetNetPreTrainedModel", "start_line": 3532, "type": "class", "chroma:document": "CODE:\n\nclass XLMProphetNetPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `XLMProphetNetPreTrainedModel` class is a PyTorch-based Prophet Net pre-trained model, utilizing the `DummyObject` metaclass. It has two key aspects:\n\n*   Requires PyTorch as its backend (`torch`) for execution.\n*   Initializes with generic input parameters (`args`, `kwargs`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3539-3543
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3543, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DepthAnythingForDepthEstimation", "start_line": 3539, "type": "class", "chroma:document": "CODE:\n\nclass DepthAnythingForDepthEstimation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DepthAnythingForDepthEstimation`**\n\n**Main Purpose and Functionality**: A class providing depth estimation functionality for various backends.\n\n**Key Implementation Details**:\n\n* Initializes instance with required backend support using the `requires_backends` method.\n* Enforces use of 'torch' backend.\n\n**Notable Dependencies or Requirements**:\n\n* Backend dependency: torch\n* Interface implemented: `requires_backends` (implied, but not explicitly defined in this code snippet)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3546-3550
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3550, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DepthAnythingPreTrainedModel", "start_line": 3546, "type": "class", "chroma:document": "CODE:\n\nclass DepthAnythingPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DepthAnythingPreTrainedModel` is a custom model class that inherits from `DummyObject`. It uses the PyTorch backend. The class has an `__init__` method that requires the `torch` backend, with no additional arguments.\n\n**Key aspects:**\n\n* Metaclass: ` DummyObject`\n* Backend: `PyTorch`\n* Primary purpose: Pre-trained model implementation\n* Important functions:\n\t+ `requires_backends(self, [\"torch\"])`: Verifies if the required `torch` backend is available.\n\nNo external dependencies are explicitly listed. However, it is implied that PyTorch is required to be installed and imported (`import torch`) for this class to function properly."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3553-3557
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3557, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetrForObjectDetection", "start_line": 3553, "type": "class", "chroma:document": "CODE:\n\nclass DetrForObjectDetection(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DetrForObjectDetection` is an object detection class using PyTorch as its backend.\n\n**Key Features:**\n\n* Initializes with optional arguments `args` and `kwargs`\n* Requires PyTorch backend (`\"torch\"`)\n\n**Implementation Details:**\n\n* The `requires_backends` method is used to specify the required backend.\n \n**Notable Interfaces and Dependencies:**\n\n* `DummyObject`: a metaclass used by the class\n* `require_backends` function: requires a specific backend for object detection"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3560-3564
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3564, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetrForSegmentation", "start_line": 3560, "type": "class", "chroma:document": "CODE:\n\nclass DetrForSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DetrForSegmentation` is a PyTorch-based segmentation model class. It leverages the `DummyObject` metaclass and relies on the `torch` backend.\n\n**Key Implementation Details:**\n\n* Enforces the use of the `torch` backend during initialization.\n* Utilizes the `requires_backends` function to validate backend requirements.\n\n**Notable Dependencies:**\n\n* ` DummyObject`: metaclass used for the class definition (not a dependency in terms of external libraries, but rather a custom class).\n* `torch`: required backend for this model."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3567-3571
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3571, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetrModel", "start_line": 3567, "type": "class", "chroma:document": "CODE:\n\nclass DetrModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: DetrModel**\n\n*   **Main Purpose**: The `DetrModel` class is an implementation of a detaction model using the `torch` backend.\n\n**Key Implementation Details**\n\n*   It uses the `requirements_backends` function to ensure that only `torch` is supported.\n*   Initializes the object with arguments (`args`) and keyword arguments (`kwargs`).\n\n**Notable Dependencies or Requirements**\n\n*   Requires the `torch` backend.\n*   Inherited from `DummyObject`, a metaclass."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3574-3578
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3578, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DetrPreTrainedModel", "start_line": 3574, "type": "class", "chroma:document": "CODE:\n\nclass DetrPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DetrPreTrainedModel` class is a custom model base class that inherits from the `DummyObject` metaclass. It has two notable features:\n\n* Required backend support for \"torch\" as indicated by `requires_backends`.\n* The class defines no specific implementation details, only setting up requirements.\n\n **Key Details:**\n\n- Inherited Metaclass: `DummyObject`\n- Primary Dependency: Supports \"torch\" backend\n\n**Notable Dependencies/Requirements:**\n\n- `DummyObject` metaclass\n- Torch backend (required)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3581-3585
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3585, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaForCausalLM", "start_line": 3581, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DiffLlamaForCausalLM`\n\n**Main Purpose and Functionality:**\nInstantiates a custom diffusion model (`DiffLlama`) designed for causal language models.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires the `torch` backend during initialization.\n\n**Notable Dependencies or Requirements:**\n\n* `DummyObject` metaclass\n* `requires_backends` function (imported but not shown in this snippet)\n* `torch` library (backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3588-3592
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3592, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaForQuestionAnswering", "start_line": 3588, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\nThe `DiffLlamaForQuestionAnswering` class is an implementation of the Hugging Face model using Torch backend (`torch`), specifically designed for question answering tasks. It inherits from `DummyObject`.\n\n**Key Implementation Details:**\n\n* Initializes the object with *args and **kwargs\n* Requires Torch backend to be present during initialization, checking if it's already available\n\n**Notable Dependencies/Requirements:**\n\n* Torch (`\"torch\"`); requires its presence for proper backend setup."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3595-3599
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3599, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaForSequenceClassification", "start_line": 3595, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`DiffLlamaForSequenceClassification` is a PyTorch-based sequence classification model variant of the DiffLaframodel. Its primary purpose is to leverage the strengths of both diffusion and language models.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires compilation at initialization, necessitating the use of the `requires_backends` function.\n\n**Notable Dependencies and Requirements:**\n\n* `DummyObject`: Metaclass used for instantiation.\n* `DiffLlamaForSequenceClassification` class itself serves as a required backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3602-3606
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3606, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaForTokenClassification", "start_line": 3602, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `DiffLlamaForTokenClassification` class is a tokenizer-specific model that leverages the `DiffLlama` architecture for token classification tasks. The primary purpose is to classify tokens into predefined categories.\n\n**Key Implementation Details**\n\n* Utilizes the `torch` backend.\n* Ensures compatibility with the `DiffLlama` framework by requiring the `torch` backend.\n\n**Notable Dependencies and Requirements**\n\n* Relies on the `DiffLlama` architecture from the `torch` backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3609-3613
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3613, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaModel", "start_line": 3609, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DiffLlamaModel` class is a model implementation specifically designed for the DiffLMLaSa (Dilated Llama for Long-distance Short-Attention) library, utilizing the PyTorch backend (`torch`). The class features an `__init__` method that requires the `torch` backend to be present before initialization.\n\n**Key Details:**\n\n* Primary Purpose: implements a model for DiffLMLaSa\n* Key Implementation Detail:\n\t+ Requires `torch` backend at initialization time (`requires_backends`)\n* Important Method Signature:\n\t+ `__init__`\n* Notable Dependencies and Requirements:\n\t+ `torch`\n\t+ `DummyObject` (class metaclass)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3616-3620
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3620, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DiffLlamaPreTrainedModel", "start_line": 3616, "type": "class", "chroma:document": "CODE:\n\nclass DiffLlamaPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n```markdown\nDiffLlamaPreTrainedModel (metaclass=DummyObject)\n\n  Primary Purpose:\n  - Pre-trained model for DiffLlama sequence-to-sequence task\n\n  Implementation Details:\n\n  - Initializes with required backend (torch)\n```\n\n**Key Algorithm/Pattern:** Use of `requires_backends` function to enforce specific backend support (in this case, torch)\n\n**Method Signatures:**\n\n- `__init__`: Initializes the model instance\n\n**Notable Dependencies:**\n\n- Related classes/functions: None\n- External dependencies:\n  - `torch`\n  - `DummyObject` (metaclass)\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3623-3627
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3627, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DinatBackbone", "start_line": 3623, "type": "class", "chroma:document": "CODE:\n\nclass DinatBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `DinatBackbone` class is a meta-class that inherits from `DummyObject`, specifying it as a backbone for Dinat models. It requires the use of only one backend: torch.\n\n**Key Details**\n\n* **Class Hierarchy**: `DinatBackline -> DummyObject`\n* **Backend Specification**: Requires the use of \"torch\" backend\n* **Initialization Method**: Calls `requires_backends` with a list of required backends, which is satisfied by the single requirement [\"torch\"].\n\n\n**External Dependencies**\n\n* Related method: `requires_backends`\n* Backend: torch"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3630-3634
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3634, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DinatForImageClassification", "start_line": 3630, "type": "class", "chroma:document": "CODE:\n\nclass DinatForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DinatForImageClassification` is a PyTorch-based class for image classification using Dinat (Diffusion-based Neural Architecture for Image and Text Generation) architecture. It initializes with required backend support (`torch`) and performs model setup.\n\n\n**Key Implementation Details**\n\n*   Initializes `self` with `args` and `kwargs`\n*   Verifies backup support is available for the \"torch\" backend\n*   Requires additional backend support is not explicitly handled in this code snippet\n\n\n**Notable Dependencies or Requirements**\n\n*   PyTorch library (`torch`)\n*   `requires_backends` function (backend agnostic model setup)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3637-3641
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3641, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DinatModel", "start_line": 3637, "type": "class", "chroma:document": "CODE:\n\nclass DinatModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**DinatModel Class Summary**\n\n*   **Main Purpose and Functionality:**\n    *   `DinatModel` is a class using the `DummyObject` metaclass, primarily designed for use with the `torch` backend.\n*   **Key Implementation Details:**\n    *   The `__init__` method initializes the model instance by requiring the presence of the `\"torc\"` backend (corrected from \"torch\") using the `requires_backends` function. This implies that this class is intended for use with PyTorch-based models.\n*   **Notable Dependencies or Requirements:**\n    *   The `DinatModel` class uses the `DummyObject` metaclass, which suggests a custom metaclass implementation.\n    *   Although not explicitly stated, it is assumed to rely on related classes/functions available in the environment where this code is executed.\n\nCorrected note:\nThe backend name used by DinatModel was mistakenly typed as \"torc\". The correct spelling is indeed \"torch\"."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3644-3648
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3648, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DinatPreTrainedModel", "start_line": 3644, "type": "class", "chroma:document": "CODE:\n\nclass DinatPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Dinat Pre-Trained Model**\n\n### Purpose and Functionality\nThis class implements a pre-trained Dinat model for use with the specified backend(s). The `DummyObject` metaclass is used. \n\n### Key Implementation Details\n\n- Requires PyTorch (torch) as the primary backend.\n- Initializes the model using variable arguments (`*args`) and keyword arguments (`**kwargs`). \n- Verifies the presence of the \"torch\" backend with the `requires_backends` function.\n\n ### Notable Dependencies/Requirements\n - Torchtensor backend (\"torchtensor\")"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3651-3655
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3655, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2Backbone", "start_line": 3651, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2Backbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`DinoV2Backbone`\n\n*   Class metaclass: `DummyObject`\n*   Primary purpose: A backbone class for integrating the DinoV2 model with various backends.\n*   Initializes the class by requiring at least one of the \"torch\" backend.\n\n**Key Implementation Details:**\n\n*   Checks if either of the 'backends' attributes are set to include \"torch\".\n\n**Notable Dependencies and Requirements:**\n\n*   `DummyObject`: used as a metaclass for the class.\n*   No external dependencies are listed."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3658-3662
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3662, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2ForImageClassification", "start_line": 3658, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2ForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`Dinov2ForImageClassification`\n\n**Purpose:** A PyTorch-based class for image classification using Dynet's Dinov 2 optimization framework.\n\n**Main Functionality:** Initializes the model and checks required backends (currently only supports \"torch\") before construction.\n\n**Notable Implementation Details:**\n\n* Requires backend support via `requires_backends` method to ensure exclusive use of PyTorch.\n* Utilizes `DummyObject` metaclass for class definition.\n\n**Key Dependencies:**\n\n* `torch`\n\n**External Requirements:** Specifically, requires Python implementation of PyTorch."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3665-3669
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3669, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2Model", "start_line": 3665, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2Model(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`Dinov2Model` is a class that initializes a model using the `torch` backend (`_backends = [\"torch\"]`) and enforces the use of this backend through the `requires_backends` decorator.\n\n**Key Details:**\n\n- Initializes the model using the `__init__` method.\n- Enforces the use of the `torch` backend with the `requires_backends` decorator.\n\n**Notable Dependencies/Requirements:**\n\n- `DummyObject` metaclass\n- `requires_backends` decorator\n- `torch` (backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3672-3676
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3676, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2PreTrainedModel", "start_line": 3672, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: Dinov2PreTrainedModel**\n\n**Purpose:** A pre-trained model class for Dinov2.\n\n**Primary Functionality:** Instantiates the `Dinov2PreTrainedModel` class with required backend setup.\n\n**Key Implementation Details:**\n\n- **`__init__` Method:** Initializes the model by checking and setting up the `torch` backend using `requires_backends`.\n\n**Notable Dependencies/Requirements:**\n\n- Requires TensorFlow version 3.x for compatibility with `torch`\n- Inherently requires backend configuration (`_backends = [\"torch\"]`)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3679-3683
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3683, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2WithRegistersBackbone", "start_line": 3679, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2WithRegistersBackbone(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `Dinov2WithRegistersBackbone` class is a PyTorch-based neural network architecture with registers. It requires the \"torch\" backend and initializes with any additional keyword arguments.\n\n**Key Features:**\n\n* **Backend:** PyTorch\n* **Class:** `Dinov2WithRegistersBackbone`\n* **Purpose:** Neural Network Architecture\n* **Primary Method Signatures:** `__init__`\n\nNote:\n\n* `DummyObject` is used as the metaclass.\n* `requires_backends` function is imported but not defined in this chunk."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3686-3690
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3690, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2WithRegistersForImageClassification", "start_line": 3686, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2WithRegistersForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `Dinov2WithRegistersForImageClassification` class is a custom neural network model implemented using the PyTorch backend. It defines an initializer function that checks for the required 'torch' backend and performs some initialization steps.\n\n**Key Highlights:**\n\n* Primary purpose: Implementing a neural network model for image classification.\n* Backend:torch\n* Initialization Requirements:\n\t+ `requires_backends` method with argument \"torch\"\n\nNo notable dependencies or features are provided outside of this snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3693-3697
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3697, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2WithRegistersModel", "start_line": 3693, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2WithRegistersModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Dinov2WithRegistersModel` is a class model that:\n\n* **Primary Purpose:** Implements a PyTorch backend.\n\n* **Key Implementation Details:**\n\t+ Requires the `requires_backends` method to ensure it uses only the `torch` backend.\n\t+ Has an empty interface (due to `_backends` assignment) that defines its backend dependency.\n\n* **Dependencies:**\n\t+ Referred Class/Function: `requires_backends`. \n\t+ External Dependency: PyTorch."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3700-3704
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3704, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Dinov2WithRegistersPreTrainedModel", "start_line": 3700, "type": "class", "chroma:document": "CODE:\n\nclass Dinov2WithRegistersPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nDinoV2 Pre-Trained Model Class\n-----------------------------\n\n*   **Main Purpose and Functionality:** Dinov2WithRegisters is a pre-trained model class built using the `DummyObject` metaclass. It inherits requirements for the `torch` backend.\n\n### Key Implementation Details:\n\n*   The `__init__` method:\n    *   Requires the `torch` backend.\n    *   Initializes the instance with arbitrary arguments (`args`) and keyword arguments (`kwargs`).\n\n### Notable Dependencies or Requirements:\n\n*   The model requires the `torch` backend for execution."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3707-3711
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3711, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForMaskedLM", "start_line": 3707, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DistilBertForMaskedLM`**\n\n* **Purpose:** A PyTorch-based implementation of DistilBert model for masked language modeling tasks, utilizing the TPU backend.\n\n**Key Implementation Details:**\n\n* Initializes with `torch` backend using `requires_backends` method.\n* Utilizes `DummyObject` metaclass for customization.\n\n**Notable Dependencies and Requirements:**\n\n* Relies on `torch` tensor computation library.\n* Inheriting from a hypothetical `metaclass=DummyObject` class, but no further details provided."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3714-3718
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3718, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForMultipleChoice", "start_line": 3714, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Code Summary**\n\n`DistilBertForMultipleChoice` is a machine learning model class utilizing the PyTorch backend. It inherits from `DummyObject`.\n\n**Attributes:**\n- `_backends`: set to [\"torch\"]\n\n**Initialization:**\nThe model requires the PyTorch backend.\n\n**Implementation Details:**\nThe class does not specify any unique implementation details, but it builds upon the provided backends and initializes itself with generic data structure `*args` and keyword arguments (`**kwargs`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3721-3725
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3725, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForQuestionAnswering", "start_line": 3721, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DistilBertForQuestionAnswering`**\n\n**Purpose and Functionality:**\nA PyTorch-based implementation of DistilBERT for question answering tasks.\n\n**Key Implementation Details:**\n\n* Initialized with support for the \"torch\" backend using `requires_backends`.\n* Note: The specific requirements and dependencies are not explicitly stated in this code snippet.\n\n**Notable Dependencies or Requirements:**\n\n* Related classes/functions not specified due to limited information.\n* Inherited/implemented interfaces:\n\t+ torch (backend)\n* External dependencies: \n\t- DistilBERT model (not shown in this snippet)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3728-3732
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3732, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForSequenceClassification", "start_line": 3728, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DistilBertForSequenceClassification` is a sequence classification model based on the DistilBERT architecture. It uses the PyTorch backend.\n\n**Key Details:**\n\n- Models `distilbert-base-uncased` for tokenization\n- Uses `torch.nn.Module` as base class\n- Requires `torch` and `tokenizerclasses.Tokenizer` backends\n\n**Implementation Focus:**\n\nThe model's initialization (`__init__`) method checks if the required PyTorch backend is present, ensuring compatibility before proceeding.\n\n*   **Requires Backends:** Verifies the presence of the 'torch' backend to ensure compatibility.\n*   **Tokenization:** Utilizes `distilbert-base-uncased` for efficient tokenization of input data.\n\nThis class inherits from `DummyObject` which is likely used for metaclass purposes in Django."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3735-3739
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3739, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertForTokenClassification", "start_line": 3735, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DistilBertForTokenClassification`**\n\n* **Main Purpose and Functionality:** \nThe `DistilBertForTokenClassification` class is an implementation of a Token Classification model built on top of DistilBERT, using the PyTorch backend.\n\n* **Key Implementation Details:**\n\t+ Requires the \"torch\" backend.\n\t+ Utilizes the `DummyObject` metaclass for class definition customization.\n\n* **Dependencies and Requirements:**\n\t+ Associated with the PyTorch backend (\"torch\").\n\t+ Inherently uses the `requires_backends` function.\n \n* **Notable Interfaces/Methods:** \nNone specified within this provided code snippet."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3742-3746
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3746, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertModel", "start_line": 3742, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DistilBertModel` is a PyTorch-based implementation of the DistilBERT model using a `DummyObject` metaclass. The class initializes with support for the \"torch\" backend.\n \n**Key Details**\n- Primary Functionality: Implementing the DistilBERT model\n- Key Algorithms/Patterns: Uses the PyTorch framework\n\n**Notable Dependencies/Requirements**\n\n- Related Class: `requires_backends`\n- External Dependency: Python's `torch` library"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3749-3753
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3753, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DistilBertPreTrainedModel", "start_line": 3749, "type": "class", "chroma:document": "CODE:\n\nclass DistilBertPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DistilBertPreTrainedModel` class is a Python implementation of a pre-trained DistilBERT model using the PyTorch backend. It initializes itself with a set of required backends (`torch`) and allows for general-purpose arguments (`*args` and `**kwargs`). \n\n**Details:**\n\n* **Main purpose:** Implements a pre-trained DistilBERT model\n* **Purpose statement:** Primary function: [model initialization]\n* **Implementation details:**\n\t+ Utilizes the PyTorch backend (`torch`)\n\t+ Verifies that the required backend is available (`requires_backends` method)\n* **Notable dependencies:**\n\t+ Related class/functions: `DummyObject`, `requires_backends`\n\t+ External dependencies:\n\t\t- PyTorch (backend)\n\nNote: The `metaclass=DummyObject` and the `_backends = [\"torch\"]` lines are used for internal implementation purposes, which do not directly affect the external users of the class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3756-3760
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3760, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DonutSwinModel", "start_line": 3756, "type": "class", "chroma:document": "CODE:\n\nclass DonutSwinModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n### DonutSwin Model Class\n\n*   **Principal Functionality**: Represents a DonutSwin model with support for the \"torch\" backend.\n*   **Implementation Details**:\n    *   Initializes the model using `requires_backends` method, which ensures the \"torch\" backend is supported.\n*   **Notable Dependencies**:\n    *   Extensively relies on the `DummyObject` metaclass.\n    *   Requires the `torch` backend implementation to be provided."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3763-3767
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3767, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DonutSwinPreTrainedModel", "start_line": 3763, "type": "class", "chroma:document": "CODE:\n\nclass DonutSwinPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DonutSwinPreTrainedModel` class is a PyTorch-based pre-trained model, utilizing the Swin transformer architecture. Its main functionality involves providing an implementation of the pre-trained Donut-Swin model using Meta-Transformers framework.\n\n**Key Implementation Details:**\n\n* Utilizes the `torch` backend.\n* Requires the `\"torch\"` backend in its initialization (`requires_backends(self, [\"torch\"])`).\n\n**Notable Dependencies or Requirements:**\n\n* The class relies on the `DummyObject` metaclass and `meta_transformer` library for execution.\n\nPlease note that without further context, specifics about the Donut-Swin model and Meta-Transformers framework cannot be precisely determined."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3770-3774
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3774, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRContextEncoder", "start_line": 3770, "type": "class", "chroma:document": "CODE:\n\nclass DPRContextEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `DPRContextEncoder` class is a Python subclass of `DummyObject`. It initializes with optional positional and keyword arguments.\n\n**Key Takeaways**\n\n* Primary purpose: Encode context for DPR (Deep Perturbation Reweighting) model\n* Implementation details:\n\t+ Requires the 'torch' backend to be present during initialization\n\t+ Uses the `requires_backends` function from a related class (not shown in this snippet)\n* Notable dependencies:\n\t+ `DummyObject` (metadata class)\n\t+ External dependency: `torch`\n* Interfaces and classes:\n\t+ None explicitly mentioned, but possibly using interfaces defined by `DummyObject`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3777-3781
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3781, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRPretrainedContextEncoder", "start_line": 3777, "type": "class", "chroma:document": "CODE:\n\nclass DPRPretrainedContextEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `DPRPretrainedContextEncoder`**\n\n**Purpose:** A pre-trained context encoder class for DPR (Deep Purchasing Recommendation) model using the `torch` backend.\n\n**Main Functionality:** Initializes and configures theencoder with required dependencies and backends.\n\n### Key Implementation Details:\n\n*   Requires the `torch` backend.\n*   Uses a metaclass (`DummyObject`) to manage internal configuration.\n\n### External Dependencies:\n- **Backend Module:** torch\n\n### Other Notes:\n\nThis class is likely intended for deep learning model training or inference applications, utilizing the `DPR` (Deep Purchasing Recommendation) framework."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3784-3788
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3788, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRPreTrainedModel", "start_line": 3784, "type": "class", "chroma:document": "CODE:\n\nclass DPRPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n* **Class:** `DPRPreTrainedModel`\n* **Purpose:** A pre-trained model for DPR applications\n* **Functionality:** Instantiates the model with required backends (currently only \"torch\").\n\nThe key aspects of this code chunk are:\n\n* Inheriting from `DummyObject` metaclass with a backend constraint set to [\"torch\"]\n* Initializing the model with variable arguments and keyword arguments, enforcing a specific backend requirement"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3791-3795
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3795, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRPretrainedQuestionEncoder", "start_line": 3791, "type": "class", "chroma:document": "CODE:\n\nclass DPRPretrainedQuestionEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DPRPretrainedQuestionEncoder` class is a wrapper around the Torch backend for the Pretrained Question Encoder model. The main purpose of this class is to provide a convenient interface for using pre-trained question encoding models.\n\n**Key Implementation Details:**\n\n* The class requires the Torch backend, which is specified in the `_backends` attribute.\n* The `__init__` method enforces this requirement at initialization time, ensuring the correct backend is used.\n\n**Notable Dependencies/Requirements:**\n\n* Related classes/interfaces:\n\t+ `DummyObject`: assumed metaclass for this class\n\t+ `requires_backends`: a function that verifies the necessary backends are present\n* External dependencies:\n\t+ Torch (backend)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3798-3802
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3802, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRPretrainedReader", "start_line": 3798, "type": "class", "chroma:document": "CODE:\n\nclass DPRPretrainedReader(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `DPRPretrainedReader` class is a trainable reader for pre-trained models in the BERT framework. It uses the \"torch\" backend.\n\n* Purpose: Trains and evaluates a model on pre-trained data.\n* Implementation Details:\n\t+ Initializes with various arguments (not shown).\n\t+ Requires the \"torch\" backend to be installed.\n* Dependencies:\n\t+ `requires_backends(self, [\"torch\"])`: Uses the \"torch\" library for computations."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3805-3809
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3809, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRQuestionEncoder", "start_line": 3805, "type": "class", "chroma:document": "CODE:\n\nclass DPRQuestionEncoder(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DPRQuestionEncoder` class:\n=====================\n\n*   **Purpose and Functionality:** A class for encoding DPR (Dialog Rewriting Assistant) questions using the Torch backend.\n*   **Key Implementation Details:**\n    *   Initializes with optional arguments (*) and keyword arguments (**) using `__init__` method.\n    *   Requires Torch backend installation using `requires_backends` function.\n*   **Notable Dependencies or Requirements:**\n    +   Utilizes the Torch backend for implementation.\n    +   No specified external dependencies, but requires Torch library at runtime.\n\n**Detailed Version (for RAG system):**\n\nThe provided code defines a class `DPRQuestionEncoder`, which employs the Torch backend. It incorporates an `__init__` method with parameters (*) and (**), followed by the call to `requires_backends` function to confirm availability of the required Torch backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3812-3816
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3816, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPRReader", "start_line": 3812, "type": "class", "chroma:document": "CODE:\n\nclass DPRReader(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DPRReader` class: A data reader for DPR (Deep Prefix Tuning Reader) with support for loading data using **PyTorch** backend.\n\n**Key Details**\n\n* Uses PyTorch backend (`torch`)\n* Enforces backend requirement at initialization\n* Method signature:\n```\ndef __init__(self, *args, **kwargs):\n    requires_backends(self, [\"torch\"])\n```"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3819-3823
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3823, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPTForDepthEstimation", "start_line": 3819, "type": "class", "chroma:document": "CODE:\n\nclass DPTForDepthEstimation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\n`DPTForDepthEstimation` is a PyTorch implementation of the DPT (deep multi-path) algorithm for depth estimation. The class uses the \"torch\" backend.\n\n**Key Details**\n\n* Initializes with input arguments using `requires_backends` method, requiring `torch` as the primary backend.\n* Uses `metaclass=DummyObject` to define the class metaparameters.\n\n**Notable Dependencies/Requirements**\n\n* `torch` (as a required dependency)\n* `DummyObject` (used for class metaparameters)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3826-3830
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3830, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPTForSemanticSegmentation", "start_line": 3826, "type": "class", "chroma:document": "CODE:\n\nclass DPTForSemanticSegmentation(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DPTForSemanticSegmentation` is a PyTorch-based class for semantic segmentation. Its primary purpose is to initialize and configure the model with the `torch` backend.\n\n**Key Implementation Details:**\n\n* The model requires the `torch` backend, which is explicitly checked during initialization.\n* Supports variable input arguments (`*args`, `**kwargs`) in the constructor.\n\n**Notable Dependencies and Requirements:**\n\n* Requires `torch` as a backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3833-3837
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3837, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPTModel", "start_line": 3833, "type": "class", "chroma:document": "CODE:\n\nclass DPTModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`DPTModel` is a class that represents a deep learning model with TensorBoardBackend (not Torch as mentioned in comments). \n\n*   The primary purpose of the class is to initialize and manage the deep learning backend.\n*   Key implementation detail: It uses `requires_backends` function which asserts that at least \"torch\" backend was passed.\n\n**Key Details:**\n\n- `_backends = [\"torch\"]`: Specifies the required backend (TensorBoard).\n- `Def __init__(self, *args, **kwargs)`: Initializes the model with variable arguments.\n \n\n**Notable Dependencies and Requirements:**\n\n- None explicitly mentioned, however it depends on `DummyObject` metaclass."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3840-3844
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3844, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "DPTPreTrainedModel", "start_line": 3840, "type": "class", "chroma:document": "CODE:\n\nclass DPTPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n*Class: `DPTPreTrainedModel`*\n \nThe `DPTPreTrainedModel` class is a PyTorch-based pre-trained model that inherits from the DummyObject metaclass. It requires the `torch` backend for initialization using the `requires_backends` function. The primary purpose of this model is to handle pre-trained data in a specific domain.\n\n**Implementation Details:**\n\n* Requires PyTorch as the primary backend.\n* Initializes with any additional arguments (`*args`) and keyword arguments (`**kwargs`).\n\n**Notable Dependencies/Requirements:**\n\n* `torch`: Required backend for initialization."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3847-3851
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3851, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientNetForImageClassification", "start_line": 3847, "type": "class", "chroma:document": "CODE:\n\nclass EfficientNetForImageClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n```python\nEfficentNetForImageClassification Class Summary:\n\n - Purpose: Image classification model using the EfficientNet architecture.\n \n - Key Details:\n   - Initialize with support for only 'torch' backend.\n   \n   - Implementation Note:\n     - `requires_backends` function ensured that only 'torch' is required.\n     \n - Notable Dependencies:\n   - `DummyObject` metaclass (implicitly inherited)\n   - External Dependency: 'torch'\n```\n\n**Full Context:** This class seems to be part of a larger package handling neural network architectures. The main purpose is image classification, leveraging the EfficientNet model. The initialization process emphasizes support for only the \"torch\" backend. The context in which this code might appear could involve frameworks that integrate with various deep learning backends."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3854-3858
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3858, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientNetModel", "start_line": 3854, "type": "class", "chroma:document": "CODE:\n\nclass EfficientNetModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `EfficientNetModel` class is a metaclass-based implementation of an EfficientNet model. It requires the `torch` backend for runtime.\n\n**Key Details:**\n\n* **Primary Purpose:** Implements an EfficientNet model using a metaclass (`DummyObject`) with `torch` as the required backend.\n* **Implementation:** Initializes the model instance, requiring the presence of at least the `torch` backend.\n\n**Notable Dependencies/Requirements:**\n\n* Related Class/Function: `requires_backends`\n* Backend: `torch`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3861-3865
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3865, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EfficientNetPreTrainedModel", "start_line": 3861, "type": "class", "chroma:document": "CODE:\n\nclass EfficientNetPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`EfficientNetPreTrainedModel`\n\n*   A PyTorch-based pre-trained model for EfficientNeet architectures, utilizing the `DummyObject` metaclass.\n\n**Key Features:**\n\n*   Initializes with `args` and `kwargs`, which are validated using the `requires_backends` function ensuring support for only the `\"torch\"` backend.\n*   Utilizes a specific backend configuration.\n\n**Implementation Details:**\n\nNone mentioned in this code chunk.\n\n**Notable Dependencies/Requirements:**\n\n*   `DummyObject`: An abstract base class used as the metaclass for this class.\n*   `requires_backends` function: A function used to validate supported backends."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3868-3872
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3872, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForCausalLM", "start_line": 3868, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `ElectraForCausalLM`**\n\n*   **Main Purpose and Functionality:** This class implements the Electra model for causal language modeling.\n\n*   **Key Implementation Details:**\n    *   The `requires_backends` method is called in the constructor, ensuring that only the required backend (`torch`) can be used to initialize the object.\n    *   The model is expected to work on top of a specific setup or library not fully disclosed within this snippet.\n\n*   **Notable Dependencies or Requirements:**\n    +   The Electra model class inherits from `DummyObject`.\n    +   A minimal set of dependencies is specified through the `_backends` attribute, with only `torch` being required."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3875-3879
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3879, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForMaskedLM", "start_line": 3875, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ElectraForMaskedLM**\n\n**Purpose and Functionality:** \nThis class implements the Electra architecture for masked language modeling.\n\n**Key Implementation Details:**\n- `__init__` method initializes the instance with support for only the \"torch\" backend.\n- The `requires_backends` function ensures compatibility of the instance's arguments with the required backends.\n\n**Notable Dependencies or Requirements:**\n- **Backend Compatibility:** Only supports \"torch\" backend (`requires_backends(self, [\"torch\"])`).\n- No external dependencies listed. \n\n**Class Hierarchy Information:** \nThis class implements a subclass of `DummyObject`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3882-3886
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3886, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForMultipleChoice", "start_line": 3882, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ElectraForMultipleChoice` is a PyTorch-based class implementing the ELECTRA model for multiple-choice tasks. The main purpose is to provide a customized Electra model backend.\n\n**Key Details:**\n\n- **Implementation:** Utilizes the `DummyObject` metaclass.\n- **Backends:** Only supports the \"torch\" backend.\n- **Initialization:** Requires a valid \"torch\" backend support using the `requires_backends` function with specific arguments.\n\n**Notable Dependencies/Requirements:**\n\n- `torch` (required), `dummy_object.py` (metaclass)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3889-3893
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3893, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForPreTraining", "start_line": 3889, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`ElectraForPreTraining` is a PyTorch-based class for pre-training language models. The primary purpose of this class is to prepare the model for downstream NLP tasks.\n\n**Key Implementation Details**\n\n- Initializes ` ElectraForPreTraining` with support for only `torch` backend using `requires_backends`.\n\n**Notable Dependencies and Requirements**\n\n- Uses PyTorch for native functionality (`torch`).\n- Requires the `DummyObject` metaclass to instantiate the class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3896-3900
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3900, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForQuestionAnswering", "start_line": 3896, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `ElectraForQuestionAnswering` class is a PyTorch-based implementation of the Electra model for question answering. The primary purpose of this class is to provide an interface for loading and using pre-trained Electra models.\n\n**Key Implementation Details:**\n\n* Initializes with `torch` as the required backend.\n* Implies use of `requires_backends` function, likely a utility function to ensure compatible backends are installed.\n\n**Notable Dependencies/Requirements:**\n\n* Torch library (`\"torch\"`).\n* Assumed usage of `requirements.backends` (not shown in this snippet) for compatibility verification.\n\nNote: The exact implementation details of the class, such as model architecture and training requirements, are not provided within this chunk. Further inspection of the surrounding codebase would be necessary to understand its complete functionality."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3903-3907
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3907, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForSequenceClassification", "start_line": 3903, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**ElectraForSequenceClassification Class Summary**\n\n*   **Class Purpose:** Implementation of the Electra model for sequence classification tasks using the PyTorch backend.\n*   **Primary Functionality:**\n    *   Instantiation of the Electra model via the `__init__` method.\n    *   Backend requirement checking, ensuring that the \"torch\" backend is available.\n\n**Key Details:**\n\n*   Utilizes the `DummyObject` metaclass for class definition.\n*   Requires the PyTorch backend (`requires_backends`) during initialization to ensure compatibility.\n\n**Notable Dependencies and Requirements:**\n\n*   The `'torch'` backend is explicitly set as a dependency.\n*   No additional external libraries or frameworks are mentioned."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3910-3914
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3914, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraForTokenClassification", "start_line": 3910, "type": "class", "chroma:document": "CODE:\n\nclass ElectraForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ElectraForTokenClassification**\n\n**Purpose and Functionality:**\nThe `ElectraForTokenClassification` class is a PyTorch-based implementation of the Electra model for token classification tasks.\n\n**Key Implementation Details:**\n\n*   Initializes with the required `torch` backend.\n*   Uses the `requires_backends` function to ensure compatibility with the specified backends.\n\n**Notable Dependencies and Requirements:**\n\n*   The `_backends` class variable specifies that the \"torch\" backbone is supported.\n*   No external dependencies are listed, but it relies on PyTorch for its functionality.\n\n**Additional Notes:**\nThe `DummyObject` metaclass is used, implying some underlying configuration or customization mechanism. However, without further context, its purpose remains unclear."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3917-3921
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3921, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraModel", "start_line": 3917, "type": "class", "chroma:document": "CODE:\n\nclass ElectraModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Electric Model Class Summary**\n\n### Purpose and Functionality\n\nThe `ElectraModel` class is an implementation of the Electra model architecture using the PyTorch backend. It initializes the model with support for only the \"torch\" backend.\n\n### Key Implementation Details\n\n* The model requires the `torch` backend to be loaded during initialization.\n* The `requires_backends` method checks if the necessary backends are available and raises an error if they are not.\n\n### Dependencies and Requirements\n\n* PyTorch (default backend)\n \n\n### Class/Function Members\n `ElectraModel`: Electra model class implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3924-3928
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3928, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ElectraPreTrainedModel", "start_line": 3924, "type": "class", "chroma:document": "CODE:\n\nclass ElectraPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\n`ElectraPreTrainedModel`\n\nA PyTorch-based class implementing an Electra pre-trained model. \n\n**Key Implementation Details:**\n\n*   Initializes with `torch` backend.\n*   Calls `requires_backends` to ensure compatibility with the specified backend.\n\n**Notable Dependencies/Requirements:**\n\n*   [Requires Backend](https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L53) - Ensure presence of `torch` backend.\n\n**Class Signature:**\n```python\nclass ElectraPreTrainedModel(metaclass=DummyObject):\n```\nThis class uses a custom metaclass (`DummyObject`) and is designed to work exclusively with the `torch` backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3931-3932
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3932, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "load_tf_weights_in_electra", "start_line": 3931, "type": "function", "chroma:document": "CODE:\n\ndef load_tf_weights_in_electra(*args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`load_tf_weights_in_electra` loads pre-trained TF weights for the Electra model using the specified `torch` backend.\n\n**Key Details:**\n\n* Primary purpose: Load pre-trained TF weights\n* Backends: Supports `torch`\n* Method signature: `requires_backends.load_tf_weights_in_.*`\n\n**Notable Dependencies:**\n\n* Related class/interface: `load_tf_weights_in_electra` (built-in function)\n* External dependencies:\n\t+ torch and related libraries are required to function."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3935-3939
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3939, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3ForCausalLM", "start_line": 3935, "type": "class", "chroma:document": "CODE:\n\nclass Emu3ForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`Emu3ForCausalLM`\n\n*   **Primary Purpose**: Implementation of a causal language modeling model using the `torch` backend.\n\n**Key Implementation Details**\n\n*   Initializes with input arguments `*args` and `**kwargs`.\n*   Ensures required dependency: `torch`.\n\n**Notable Dependencies/Requirements**\n\n*   `torch`: Required backend for execution.\n*   `DummyObject`: Metaclass used for this class."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3942-3946
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3946, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3ForConditionalGeneration", "start_line": 3942, "type": "class", "chroma:document": "CODE:\n\nclass Emu3ForConditionalGeneration(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `Emu3ForConditionalGeneration`**\n\n**Main Purpose and Functionality:**\nThe `Emu3ForConditionalGeneration` class is an implementation of the Hugging Face Transformers library's conditional generation model, Emu-3. It uses the `torch` backend.\n\n**Implementation Details:**\n\n* Initializes the instance with required dependencies.\n\t+ Requires the `torch` backend to be installed and available.\n* Verifies that the required backend (`torch`) is present.\n\n**Notable Dependencies or Requirements:**\n\n* External dependency: `torch`\n* Inherited interface(s): None\n* Implemented interface(s): None\n\nNote: I've extracted the key points from the original code chunk, focusing on the main purpose, implementation details, and dependencies."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3949-3953
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3953, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3PreTrainedModel", "start_line": 3949, "type": "class", "chroma:document": "CODE:\n\nclass Emu3PreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Code Summary**\n\n`Emu3PreTrainedModel` is a [ torch backend ](https://pytorch.org/) model class that assumes `torch` as the primary backend. It initializes with args and kwargs using an undocumented `requirements_backends` function.\n\nNotable Details:\n\n*   Implements `dummy object` metaclass (`DummyObject`) to ensure class definition.\n*   Requires `torch` backend for initialization (`requires_backends(self, [\"torch\"])`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3956-3960
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3960, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3TextModel", "start_line": 3956, "type": "class", "chroma:document": "CODE:\n\nclass Emu3TextModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\nThe `Emu3TextModel` class is an interface for text models using the `torch` backend. It has a single constructor (`__init__`) that ensures at least the `\"torch\"` backend is required.\n\n**Key Details:**\n\n*   Backend support: `\"torch\"`\n*   Constructor signature: `self, *args, **kwargs`\n*   Requirements check: `requires_backends(self, [\"torch\"])`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3963-3967
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3967, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "Emu3VQVAE", "start_line": 3963, "type": "class", "chroma:document": "CODE:\n\nclass Emu3VQVAE(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`Emu3VQVAE` is a class implementing VQVAE (Variational Quantum Approximate Representation) using the `torch` backend. It initializes with required backend `torch`.\n\n**Key Details:**\n\n- Primary purpose: Implement VQVAE algorithm using `torch`.\n- Notable Design Choice: Initializes with `requires_backends` to ensure valid backend support.\n\n**Implementation Details:**\n\n- Method Signature:\n  - `__init__(self, *args, **kwargs)`\n\n- Dependencies:\n  - Related class: ` DummyObject`\n  - External Dependency: `torch`"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3970-3974
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3974, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EncodecModel", "start_line": 3970, "type": "class", "chroma:document": "CODE:\n\nclass EncodecModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`EncodecModel` class:\n-----------------------------------\n\n*   **Purpose**: An encoding model that requires the `torch` backend.\n\n*   **Key Implementation Details**:\n\n    *   Verifies that the required backend (`torch`) is present using the `requires_backends` method.\n    *   Initializes the model with variable arguments (`args`, `kwargs`).\n\n*   **Notable Dependencies**:\n\n    *   Requires the `torch` backend.\n    *   Inherited or Implemented interfaces: None.\n\nNote: The `DummyObject` metaclass is not explicitly used in the code snippet provided. For the sake of understanding, it's assumed to be a mock implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3977-3981
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3981, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EncodecPreTrainedModel", "start_line": 3977, "type": "class", "chroma:document": "CODE:\n\nclass EncodecPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `EncodecPreTrainedModel`**\n\n*   **Purpose and Functionality**: This class represents a pre-trained model for encoding tasks, utilizing the PyTorch backend.\n\n**Key Implementation Details**:\n\n*   The model is initialized with specific backend requirements during instantiation (`__init__` method).\n*   It relies on the `DummyObject` metaclass and has a fixed set of supported backends (`_backends = [\"torch\"]`).\n\n**Notable Dependencies or Requirements**:\n\n*   PyTorch: Required for this pre-trained model.\n*   `requires_backends` function (unimplemented): Expected to enforce specific backend requirements during model initialization.\n\n**Contextual Notes**: This class appears to be designed for encoding tasks and integrates with the specified backend, implying a deeper connection to natural language processing or computer vision applications."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3984-3988
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3988, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EncoderDecoderModel", "start_line": 3984, "type": "class", "chroma:document": "CODE:\n\nclass EncoderDecoderModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `EncoderDecoderModel` class is a metadata model for an encoder-decoder architecture, utilizing the \"torch\" backend. It initializes with no specific requirements.\n\n**Key Details**\n\n* Primary purpose: Metdata model for encoder-decoder architecture\n* Use case: Utilizes \"torch\" backend\n* Key implementation: Requires \"torch\" as a backend\n\n**Notable Dependencies/Requirements**\n\n* `requires_backends` function for specifying required backends\n* \"torch\" backend dependency"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3991-3995
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 3995, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForCausalLM", "start_line": 3991, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ErnieForCausalLM**\n\n*   **Purpose:** A PyTorch-based implementation of the Ernie model for causal language modeling (CLM).\n*   **Functionality:** The class inherits from `DummyObject` and provides an initializer that depends on the \"torch\" backend.\n\n**Key Implementation Details:**\n\n*   Initializes with variable arguments (`*args`) and keyword arguments (`**kwargs`).\n*   Verifies that the `\"torch\"` backend is required using `requires_backends`.\n\n**Notable Dependencies or Requirements:**\n\n*   Requires the `\"torch\"` PyTorch backend.\n*   Inherited from `DummyObject`."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:3998-4002
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4002, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForMaskedLM", "start_line": 3998, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `ErnieForMaskedLM` class is a PyTorch-based model implemented using the Metaclass `DummyObject`. It is designed for Masked Language Modeling tasks. \n\n**Key Implementation Details**\n\n- Initializes with a require check to ensure the 'torch' backend is provided.\n- Utilizes the `requires_backends` function, which enforces the specified backend (`['torch']`) when instantiating the class.\n\n**Notable Dependencies/Requirements**\n\n- Reqs the 'torch' PyTorch backend."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4005-4009
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4009, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForMultipleChoice", "start_line": 4005, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForMultipleChoice(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`ErnieForMultipleChoice`: A PyTorch-based model for multiple choice classification.\n\n**Key Implementation:**\n- Initializes with `*args` and `**kwargs`, requiring the `\"torch\"` backend via `requires_backends`.\n- Uses the PyTorch framework (`torch`) backend.\n\n**Dependencies/Requirements:**\n\n* PyTorch library\n* DummyObject metaclass (not explicitly listed, but assumed)\n* `requires_backends` function (not shown in this code snippet)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4012-4016
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4016, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForNextSentencePrediction", "start_line": 4012, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForNextSentencePrediction(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ErnieForNextSentencePrediction` is a PyTorch-based model for next sentence prediction. It utilizes the `ErnieTokenizer` and relies on the `torch` backend.\n\n**Key Details:**\n\n* Initializes with `requires_backends` function, enforcing `torch` as a required backend.\n* Tokenizer dependency implicitly references `ErnieTokenizer`.\n* No specific model architecture details are provided.\n\nNote that this summary focuses on the critical aspects of the class, omitting unnecessary implementation details and generic descriptions."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4019-4023
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4023, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForPreTraining", "start_line": 4019, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForPreTraining(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ErnieForPreTraining**\n\n*   **Main Purpose:** A pre-trained language model class `ErnieForPreTraining` that leverages the `torch` backend.\n\n**Key Implementation Details:**\n\n*   Initializes with input arguments `args` and `kwargs`.\n*   Verifies that the required `torch` backend is installed using the `requires_backends` function.\n\n**Notable Dependencies or Requirements:**\n\n*   Requires `torch`, a popular deep learning library.\n*   Utilizes the `DummyObject` metaclass due to its undefined implications on class initialization behavior."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4026-4030
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4030, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForQuestionAnswering", "start_line": 4026, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForQuestionAnswering(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: ErnieForQuestionAnswering**\n\n* **Main Purpose and Functionality:** ErnieForQuestionAnswering is a pre-trained model for question answering tasks.\n* **Key Implementation Details:**\n\t+ Initializes with `torch` backend using the `requires_backends` method.\n\t+ Uses `DummyObject` metaclass.\n* **Notable Dependencies or Requirements:**\n\t+ Relies on `torch` backend for implementation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4033-4037
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4037, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForSequenceClassification", "start_line": 4033, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary**\n\n`ErnieForSequenceClassification` Class:\n------------------------\n\n### Main Purpose and Functionality\n\n`ErnieForSequenceClassification` is a PyTorch-based sequence classification model that leverages the Ernie Large Language Model (`DummyObject`) as its backbone. The primary purpose is to classify input sequences.\n\n### Key Implementation Details\n\n*   Requires the `torch` backend for execution.\n*   Initializes with variable-length arguments and keyword-only arguments.\n\n**Notable Dependencies and Requirements**\n\n*   `[ErnieClassifier]`: Serves as a classification model interface, implying the need for a related implementation or library."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4040-4044
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4044, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieForTokenClassification", "start_line": 4040, "type": "class", "chroma:document": "CODE:\n\nclass ErnieForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:**\n\n`ErnieForTokenClassification` is a PyTorch-based model for token classification. Its primary purpose is to classify tokens in a sequence of text using the Ernie tokenizer backend.\n\n**Key Implementation Details:**\n\n* Models are initialized with the \"torch\" backend, which enables automatic backend switching.\n* The `requires_backends` function ensures that the required \"torch\" backend is available before model initialization.\n\n**Notable Dependencies/Requirements:**\n\n* PyTorch\n* DummyObject (metaclass)\n* ErnieTokenizer (presumably used by this class)"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4047-4051
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4051, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErnieModel", "start_line": 4047, "type": "class", "chroma:document": "CODE:\n\nclass ErnieModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nThe `ErnieModel` class is a PyTorch-based model class with the following characteristics:\n\n*   Primary purpose: Implements a machine learning model utilizing the PyTorch backend.\n*   Key implementation details:\n    *   Verifies if the required backend (`torch`) is present using the `requires_backends` function.\n    \n        **Code Snippet**\n        \n```python\nclass ErnieModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```\n\n**Additional Notes**\n\n*   Notable dependencies: This code depends on the `PyTorch` library and the `DummyObject` metaclass.\n*   Inherited/Implemented interfaces: None explicitly mentioned in this snippet. However, it's likely that the class implements the `torch._C._Module` interface."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4054-4058
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4058, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "ErniePreTrainedModel", "start_line": 4054, "type": "class", "chroma:document": "CODE:\n\nclass ErniePreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Ernie Pre-Trained Model Class Summary**\n\n**Main Purpose and Functionality:**\nThe `ErniePreTrainedModel` class is a pre-trained model implemented using the `DummyObject` metaclass. It serves as a base class for Ernie models.\n\n**Key Implementation Details:**\n\n* The model requires the `torch` backend, ensuring compatibility.\n* The `__init__` method initializes the model instance with variable arguments (`args`) and keyword arguments (`kwargs`).\n\n**Notable Dependencies or Requirements:**\n\n* External dependency: `torch`\n\nNote that there are no external interfaces implemented."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4061-4065
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4065, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmFoldPreTrainedModel", "start_line": 4061, "type": "class", "chroma:document": "CODE:\n\nclass EsmFoldPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary**\n\nErmia Fold Pre-trained Model (ESM): A language model built using EsmFold algorithm.\n\n**Key Details:**\n\n* Initializes with support for only `torch` backend (`requires_backends`).\n* Primary purpose is to leverage pre-trained weights obtained from the `EsmFold` algorithm.\n\n**Implementation:**\n```python\nclass EsmFoldPreTrainedModel:\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n```\n**Dependencies:**\n\n* External dependency on `_DummyObject` for metaclass definition.\n* Requires `requires_backends` framework function for backend validation."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4068-4072
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4072, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmForMaskedLM", "start_line": 4068, "type": "class", "chroma:document": "CODE:\n\nclass EsmForMaskedLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary**\n\n`EsmForMaskedLM` is a PyTorch-based masked language model.\n\n**Key Implementations**\n\n- Applies T5-style Masked Language Modeling algorithm.\n- Uses `requires_backends` method to ensure compatible backend (torch) installed.\n\n**Dependencies/Requirements**\n\n- Requires backend installation (`torch`)."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4075-4079
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4079, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmForProteinFolding", "start_line": 4075, "type": "class", "chroma:document": "CODE:\n\nclass EsmForProteinFolding(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Summary:** \n\n`EsmForProteinFolding` class: A PyTorch-based implementation of the ESM (Evolutionary Information from Structure and Motion) model for protein folding prediction.\n\n**Key Functionality:**\n\n* Initializes with supported backends (`torch`) and requires compatibility check\n* Uses `provide` method to ensure required backend (`torch`) is available\n\n**Notable Implementation Details:**\n\n* `__init__` method initializes the class with optional arguments\n* Requires compatible backend check using `requires_backends` function\n\n**Dependencies:**\n\n* PyTorch library (`torch`)\n* `DummyObject` metaclass"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4082-4086
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4086, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmForSequenceClassification", "start_line": 4082, "type": "class", "chroma:document": "CODE:\n\nclass EsmForSequenceClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`EsmForSequenceClassification`\n\n*   **Purpose:** Implements a PyTorch-based sequence classification model using the ESM embeddings.\n\n**Implementation Details:**\n\n*   Initializes the model using a custom `__init__` method, ensuring that `torch` is supported as a required backend.\n*   Calls the `requires_backends` function to enforce this requirement.\n\n**Dependencies and Requirements:**\n\n*   Supported backends: `torch`\n*   Inherited interface not specified."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4089-4093
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4093, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmForTokenClassification", "start_line": 4089, "type": "class", "chroma:document": "CODE:\n\nclass EsmForTokenClassification(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Concise Summary:**\n\nThe `EsmForTokenClassification` class is a PyTorch-based implementation of the ESMTokenClassifier model using the DummyObject metaclass. \n\nIt has a single requirement that the backend must be \"torch\". The constructor initializes the model with any required parameters.\n\nKey Implementation Details:\n\n*   Requires Torchaes to be present and initialized correctly.\n*   Uses the DummyObject metaclass, likely for testing or experimental purposes."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4096-4100
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4100, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmModel", "start_line": 4096, "type": "class", "chroma:document": "CODE:\n\nclass EsmModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary:**\n\n`EsmModel`\n================\n\n### Purpose and Functionality\n\nThe `EsmModel` class represents an ESM (Efficient Spatial Model) model. It initializes the backend to be used by default, which is set to `\"torch\"`.\n\n### Key Implementation Details\n\n- Initializes with a `requires_backends` method that checks if the required_backend(s) for each arg (args is of type variable arguments)\n    \n### Notable Dependencies or Requirements\n\n* Backend: `torch`\n \n\nNote:\n- `_backends` attribute can be accessed from outside the class \n-  requires_backends() is not built-in python function"}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4103-4107
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4107, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "EsmPreTrainedModel", "start_line": 4103, "type": "class", "chroma:document": "CODE:\n\nclass EsmPreTrainedModel(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: EsmPreTrainedModel**\n\n*   **Main Purpose:** An implementation of pre-trained models using the ESM (Emergence-based Sequence Modeling) architecture.\n\n*   **Key Implementation Details:**\n    *   The model uses a specific backend, requiring `torch` environment.\n    *   The class utilizes the `requires_backends` function to enforce backend requirements in the initialization process.\n\n*   **Notable Dependencies and Requirements:**\n\n    *   External dependency on `PyTorch`.\n        *   Related class/function name: `torch`\n    *   Implemented interface: `DummyObject` (as a metaclass)\n    *   Additional dependencies/inherited interfaces not explicitly mentioned."}


created_at: 2025-02-06 01:30:03
topic: persistent://default/default/36aaa84d-eaec-4336-b3cf-6fd9b8a1dd00
id: /home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py:4110-4114
encoding: FLOAT32
metadata:
{"docstring": "", "end_line": 4114, "file_path": "/home/gaby/projects/2025/transformers-4.48.1/src/transformers/utils/dummy_pt_objects.py", "name": "FalconForCausalLM", "start_line": 4110, "type": "class", "chroma:document": "CODE:\n\nclass FalconForCausalLM(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n\n\nSUMMARY:\n**Class Summary: `FalconForCausalLM`**\n\n*   **Purpose:** A custom implementation of FALCON (Flexible Application for Large Language Models) for causal LLM (Large Language Model) training using the Torch backend.\n\n**Key Implementation Details:**\n\n*   Requires support for both positional and keyword arguments (`self, *args, **kwargs`) as input in the initializer method.\n*   `requires_backends` function check ensures the FALCON model uses only the required torch backend during initialization.\n\n**Notable Dependencies/Requirements:**\n\n*   Torch backend implementation (`torch`)\n*   Inherited interface: Not explicitly listed; however, all dependencies are assumed to be managed by the internal metaclass or parent classes.\n*   Parent metaclass: `DummyObject`"}


