# 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`).
```
---
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