# ds1000 / 689 - taskset: [ds1000](https://harnessreport.com/tasks/ds1000.md) - difficulty: - category: - language: - runnable from the site: no - agent timeout: 1800s ## Results by harness _none yet_ ## Instruction ``` # 689: DS-1000 Task ## Prompt Problem: I'm using tensorflow 2.10.0. I have two embeddings tensor A and B, which looks like [ [1,1,1], [1,1,1] ] and [ [0,0,0], [1,1,1] ] what I want to do is calculate the L2 distance d(A,B) column-wise. First I did a tf.square(tf.sub(lhs, rhs)) to get [ [1,1,1], [0,0,0] ] and then I want to do an column-wise reduce which returns [ 1,1,1 ] but tf.reduce_sum does not allow my to reduce by column. Any inputs would be appreciated. Thanks. A: <code> import tensorflow as tf a = tf.constant([ [1,1,1], [0,1,1] ]) b = tf.constant([ [0,0,1], [1,1,1] ]) </code> result = ... # put solution in this variable BEGIN SOLUTION <code> ## What to do - Edit `solution/solution.py` so the code passes the DS-1000 tests. - Do not access the internet or install new packages; required libraries are preinstalled in the Docker image. - Run tests locally via `bash tests/test.sh`. ## Notes - Keep the variable names/signatures implied by the prompt/code_context. - The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`). ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp