__name__ == "__main__"
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
__name__ == "__main__" has 18 facts recorded in Dontopedia across 7 references, with 2 live disagreements.
18 facts·10 predicates·7 sources·2 in dispute
Mostly:rdf:type(6), calls(2), is present(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (2)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
containsConditionalContains Conditional(1)
- Python Code Block
ctx:python-code-block
hasMainGuardHas Main Guard(1)
- Code Snippet
ex:code-snippet
Other facts (16)
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.
16 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Python Idiom | [2] |
| Rdf:type | Python Conditional | [3] |
| Rdf:type | Conditional Guard | [4] |
| Rdf:type | Guard Clause | [5] |
| Rdf:type | Name Check | [6] |
| Rdf:type | Entry Condition | [7] |
| Calls | Main | [1] |
| Calls | Main | [2] |
| Is Present | true | [1] |
| Purpose | Entry Point Check | [2] |
| Encloses | Main Call | [2] |
| Has Body | Main Call | [4] |
| Triggers | Main Call | [4] |
| Ensures | main execution when run directly | [5] |
| Compares to | __main__ | [6] |
| Runs Async Io | Main | [7] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
—
isPresentbeam/8fc39388-cedb-4361-9f72-ff58c215c749
true
—
callsbeam/8fc39388-cedb-4361-9f72-ff58c215c749
ex:main
—
typebeam/407f2871-c46e-42a2-8c90-62e6da993ee6
ex:Python-Idiom
—
labelbeam/407f2871-c46e-42a2-8c90-62e6da993ee6
__name__ == "__main__"
—
purposebeam/407f2871-c46e-42a2-8c90-62e6da993ee6
ex:entry-point-check
—
enclosesbeam/407f2871-c46e-42a2-8c90-62e6da993ee6
ex:main-call
—
callsbeam/407f2871-c46e-42a2-8c90-62e6da993ee6
ex:main
—
typebeam/ad94ff2b-048b-4c69-999c-23929580e148
ex:PythonConditional
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labelbeam/ad94ff2b-048b-4c69-999c-23929580e148
if __name__ == "__main__"
—
hasBodybeam/97c16e85-2563-4855-9a67-aec2c81baa34
ex:main-call
—
typebeam/97c16e85-2563-4855-9a67-aec2c81baa34
ex:ConditionalGuard
—
triggersbeam/97c16e85-2563-4855-9a67-aec2c81baa34
ex:main-call
—
typebeam/bbc2a132-798b-4d06-b23d-f3c7430270bb
ex:GuardClause
—
ensuresbeam/bbc2a132-798b-4d06-b23d-f3c7430270bb
main execution when run directly
—
typebeam/94809cf9-75d5-408c-b559-5bdf6720831e
ex:NameCheck
—
comparesTobeam/94809cf9-75d5-408c-b559-5bdf6720831e
__main__
—
runsAsyncIObeam/5f136ada-ae6b-4cfd-b508-43f33e6accc6
ex:main
—
typebeam/5f136ada-ae6b-4cfd-b508-43f33e6accc6
ex:EntryCondition
References (7)
7 references
ctx:claims/beam/8fc39388-cedb-4361-9f72-ff58c215c749- full textbeam-chunktext/plain1 KB
doc:beam/8fc39388-cedb-4361-9f72-ff58c215c749Show excerpt
challenges = {} def add_challenge(name, priority, description): challenges[name] = {"priority": priority, "description": description} def prioritize_challenges(challenges): sorted_challenges = sorted(challenges.items(), key=lambda…
ctx:claims/beam/407f2871-c46e-42a2-8c90-62e6da993ee6- full textbeam-chunktext/plain1 KB
doc:beam/407f2871-c46e-42a2-8c90-62e6da993ee6Show excerpt
average_response_time = sum(response_times) / len(response_times) print(f"Average response time: {average_response_time:.2f}ms") if __name__ == "__main__": main() ``` ### Explanation 1. **ThreadPoolExecutor**: This creates a …
ctx:claims/beam/ad94ff2b-048b-4c69-999c-23929580e148- full textbeam-chunktext/plain1 KB
doc:beam/ad94ff2b-048b-4c69-999c-23929580e148Show excerpt
[Turn 4454] User: I'm trying to implement the metadata parsing logic for 1.5 million documents using Apache Tika 2.8.0, but I'm facing issues with handling concurrent updates. I've designed a pipeline to handle 1,500 concurrent metadata upd…
ctx:claims/beam/97c16e85-2563-4855-9a67-aec2c81baa34- full textbeam-chunktext/plain1 KB
doc:beam/97c16e85-2563-4855-9a67-aec2c81baa34Show excerpt
num_batches = 1200000 // batch_size for i in range(num_batches): process_batch(batch_size) if __name__ == "__main__": main() ``` ->-> 3,6 [Turn 4465] Assistant: Certainly! Using Apache NiFi for your ETL workflows can b…
ctx:claims/beam/bbc2a132-798b-4d06-b23d-f3c7430270bb- full textbeam-chunktext/plain1 KB
doc:beam/bbc2a132-798b-4d06-b23d-f3c7430270bbShow excerpt
3. **Logging**: - Implement detailed logging to track the progress and errors during metadata extraction. 4. **Configuration**: - Customize Tika's behavior by configuring it through its API or using command-line arguments. ### Examp…
ctx:claims/beam/94809cf9-75d5-408c-b559-5bdf6720831ectx:claims/beam/5f136ada-ae6b-4cfd-b508-43f33e6accc6- full textbeam-chunktext/plain1 KB
doc:beam/5f136ada-ae6b-4cfd-b508-43f33e6accc6Show excerpt
# Further processing with the expanded query print(f"Processing expanded query: {expanded_query}") async def main(): queries = [ "What are the benefits of using machine learning for natural language processing?", …
See also
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