kz930 opened a new pull request, #7533:
URL: https://github.com/apache/texera/pull/7533

   ### What changes were proposed in this PR?
   
   Word Cloud and Network Graph both lay their output out from an unseeded 
generator, so running the same workflow twice draws two different pictures of 
the same data. Word Cloud built its image without `random_state`, and Network 
Graph called `nx.spring_layout` without `seed`. This passes a fixed seed in 
both.
   
   Only the arrangement was moving. Which words appear and how large they are 
is decided by frequency alone, and the graph's nodes and edges are likewise 
unaffected, so no analysis changes. What changes is that a saved screenshot 
keeps matching what the operator produces, and two people opening the same 
workflow see the same picture. Every other visualization in Texera already 
behaves this way.
   
   The issue offered a second option, exposing the seed as a user setting. This 
takes the smaller one: a fixed default with no new setting. Existing workflows 
render one last different picture and are stable from then on.
   
   ### Any related issues, documentation, discussions?
   
   Closes #7326
   
   ### How was this PR tested?
   
   `WordCloudOpDescSpec` and `NetworkGraphOpDescSpec` each gain a case 
asserting the generated Python carries the seed. The Network Graph one reads 
the `nx.spring_layout` line rather than matching the whole call, so it survives 
a change to `k` or `iterations`. Both suites pass, 11 tests.
   
   The seed was also checked against the libraries themselves, outside the 
operators: rendering the same text twice through `WordCloud` gives two 
different PNGs unseeded and byte-identical ones with `random_state=0`, and 
`nx.spring_layout` on the same graph gives different node coordinates unseeded 
and identical ones with `seed=0`.
   
   ### Was this PR authored or co-authored using generative AI tooling?
   
   Generated-by: Claude Code (Claude Opus 5)
   


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