We are generating a **test harness** for a **Tenstorrent** kernel implementing **{{ operation }}**. The harness runs the Tenstorrent kernel and compares its output to a PyTorch reference; it is **expected** to fail with an AssertionError (tensor mismatch) until the kernel is correct. Right now the harness is failing in a **different** way—a structural problem in the harness (e.g., wrong input tensor type or shape, missing parameters, missing imports, or misuse of the Tenstorrent API).

Your goal: use your understanding of the **{{ operation }}** op's semantics and of Tenstorrent kernel interfaces to identify what is wrong in the **test harness** so it runs and then fails with the expected AssertionError. Fix only the harness (tensor setup, types, shapes, parameters); do not suggest changing the kernel implementation.

## Failure Details

**Command run:** `{{ ran_command }}`

**Exit code:** {{ exit_code }}

**stdout:**
```
{{ stdout }}
```

**stderr:**
```
{{ stderr }}
```

{% if files %}
## Current Files
{% for file in files %}
### {{ file.filename }}
{% if file.usage %}*{{ file.usage }}*{% endif %}

```python
{{ file.contents }}
```
{% endfor %}
{% endif %}

## Common Structural Issues to Look For

- Wrong input tensor dtype or shape
- Wrong output tensor dtype or shape
- Missing or incorrect definition of parameter(s) used in golden reference calculation
- Python syntax errors

## Output Format

You must return your analysis as a JSON object with a `suggested_fixes` array. Each fix must be an object with:
- `path`: file path containing the error
- `line_number`: line number(s) with the error (can be a range like "117-118" or single line "117")
- `likely_cause`: brief explanation of the structural issue in the harness
- `suggested_fix`: specific actionable solution (change the harness only)

Example format:
```json
{
  "suggested_fixes": [
    {
      "path": "run_newop.py",
      "line_number": "42",
      "likely_cause": "Input tensor dtype does not match what the Tenstorrent kernel and reference expect for this op",
      "suggested_fix": "Use ttnn.bfloat16 for input_tensor to match the kernel and reference; ensure golden uses the same dtype"
    }
  ]
}
