# ds1000 / 943 - 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 ``` # 943: DS-1000 Task ## Prompt Problem: I'm trying to slice a PyTorch tensor using a logical index on the columns. I want the columns that correspond to a 1 value in the index vector. Both slicing and logical indexing are possible, but are they possible together? If so, how? My attempt keeps throwing the unhelpful error TypeError: indexing a tensor with an object of type ByteTensor. The only supported types are integers, slices, numpy scalars and torch.LongTensor or torch.ByteTensor as the only argument. MCVE Desired Output import torch C = torch.LongTensor([[999, 777], [9999, 7777]]) Logical indexing on the columns only: A_log = torch.ByteTensor([1, 1, 0]) # the logical index B = torch.LongTensor([[999, 777, 114514], [9999, 7777, 1919810]]) C = B[:, A_log] # Throws error If the vectors are the same size, logical indexing works: B_truncated = torch.LongTensor([114514, 1919, 810]) C = B_truncated[A_log] A: <code> import numpy as np import pandas as pd import torch A_log, B = load_data() </code> C = ... # 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