# ds1000 / 976

- 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

```
# 976: DS-1000 Task

## Prompt
Problem:

I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.

I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1 or 2).

However, I must return a n x 1 tensor, and I want to somehow pick the lowest probability for each input and create a tensor indicating which class had the lowest probability. How can I achieve this using Pytorch?

To illustrate, my Softmax outputs this:

[[0.2, 0.1, 0.7],
 [0.6, 0.3, 0.1],
 [0.15, 0.8, 0.05]]
And I must return this:

[[1],
 [2],
 [2]]


A:

<code>
import numpy as np
import pandas as pd
import torch
softmax_output = load_data()
</code>
y = ... # 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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