Dontopedia

nn.Module.__init__ call

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

nn.Module.__init__ call has 6 facts recorded in Dontopedia across 4 references, with 2 live disagreements.

6 facts·1 predicates·4 sources·2 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (8)

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callsSuperCalls Super(5)

invokesInvokes(2)

callsCalls(1)

Other facts (4)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

Timeline

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typebeam/c6ee25c2-5292-4256-95f3-8b4c1563623a
ex:SuperConstructorCall
typebeam/e544e68c-76b5-4e41-95e3-2d1c8d6c4836
ex:SuperConstructorCall
labelbeam/e544e68c-76b5-4e41-95e3-2d1c8d6c4836
nn.Module.__init__ call
typebeam/fa097ab4-7c54-4d7c-bce6-50883cbc7667
ex:ParentConstructor
typebeam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
ex:ParentConstructorCall
labelbeam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
parent class initialization

References (4)

4 references
  1. ctx:claims/beam/c6ee25c2-5292-4256-95f3-8b4c1563623a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/c6ee25c2-5292-4256-95f3-8b4c1563623a
      Show excerpt
      class ResizingModule(nn.Module): def __init__(self): super(ResizingModule, self).__init__() self.fc1 = nn.Linear(512, 128) self.fc2 = nn.Linear(128, 128) def forward(self, x): x = torch.relu(self.fc1
  2. ctx:claims/beam/e544e68c-76b5-4e41-95e3-2d1c8d6c4836
    • full textbeam-chunk
      text/plain1 KBdoc:beam/e544e68c-76b5-4e41-95e3-2d1c8d6c4836
      Show excerpt
      - The `model` is created with a dynamic context size. - The `model.summary()` prints the model structure, and `model.predict` tests the model with the padded `input_ids`. By following these steps and using the provided example code, you sh
  3. ctx:claims/beam/fa097ab4-7c54-4d7c-bce6-50883cbc7667
  4. ctx:claims/beam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
      Show excerpt
      - Use tools like `torch.utils.benchmark` to measure and compare the performance of different configurations. ### Example with Error Handling Here's an example with error handling: ```python import torch import torch.nn as nn class Sc

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