You are required to act as an answer evaluator. Given an issue context, the hint disclosed to the agent and the answer from the agent, 
you should rate the performance of the agent into three levels: "failed", "partially", and "success". The rating rules are as follows:

<rules>
1. You will be given a list of <metrics>, for each metric, you should rate in [0,1] for the agent based on the metric criteria, and then multiply the rating by the weight of the metric.
2. If the sum of the ratings is less than 0.45, then the agent is rated as "failed"; if the sum of the ratings is greater than or equal to 0.45 and less than 0.85, then the agent is rated as "partially"; if the sum of the ratings is greater than or equal to 0.85, then the agent is rated as "success".
3. **<text>** means the text is important and should be paid attention to.
4. ****<text>**** means the text is the most important and should be paid attention to.
</rules>

The <metrics> are as follows:
{
    "m1": {
        "criteria": "Precise Contextual Evidence:
            1. The agent must accurately identify and focus on the specific issue mentioned in the context. This involves a close examination of the exact evidence given and determining whether it aligns with the content described in the issue and the involved files.
            2. Always ask yourself: Have the agent provided correct and detailed context evidence to support its finding of issues? If the agent just gives some general description without specifically pointing out where the issues occur, you should give it a low rate.
            3. Once the agent has correctly spotted **** all the issues in <issue> and provided accurate context evidence ****, it should be given a ****full score (1.0) even if it includes other unrelated issues/examples ****"
            4. If the agent has only spotted part of the issues with the relevant context in <issue>, then you should give a medium rate.
            5. The expression in the answer of the agent might not directly pinpoint the issue, but when its answer implies the existence of the <issue> and has provided correct evidence context, then it should be given a high rate for m1.
            6. For issues about something missing and having no clear location information, even if there is context in <issue> involved files, it's ok for the agent to only give an issue description without pointing out where the issue occurs in detail."
         "weight": 0.8,
        "range": [0, 1]
    },
    "m2": {
        "criteria": "Detailed Issue Analysis: 
            1. The agent must provide a detailed analysis of the issue, showing an understanding of how this specific issue could impact the overall task or dataset as human evaluators do.
            2. This metric stresses the importance of not just identifying that there is an issue but also understanding and explaining its implications in detail, rather than simply repeating the information in hint.",
        "weight": 0.15,
        "range": [0, 1]
    },
    "m3": {
        "criteria": "Relevance of Reasoning: 
            1. The agent’s reasoning should directly relate to the specific issue mentioned, highlighting the potential consequences or impacts.
            2. This metric ensures that the agent’s logical reasoning directly applies to the problem at hand, rather than being a generic statement.",
        "weight": 0.05,
        "range": [0, 1]
    }
}
</metrics>

--------------------------------

Now let’s begin:

<issue>
title: Some "TODO" left in the script
content: There some TODOs in the script, which should be removed before submitting
involved: [
    {
        "name": "adult.py",
        "context": "class Adult(tfds.core.GeneratorBasedBuilder):

    # {TODO}: Set up version.
    VERSION = tfds.core.Version('0.1.0')
----------
 def _split_generators(self, dl_manager):
        # {TODO}: Downloads the data and defines the splits
        # dl_manager is a tfds.download.DownloadManager that can be used to
        # download and extract URLs
----------
 def _generate_examples(self, records):
        # {TODO}: Yields (key, example) tuples from the dataset
        for i, row in enumerate(records):"
    }
]
</issue>

<hint>
None
</hint>

-------------------- Below is the answer from the agent. Ensure you don’t take the information above as the agent’s answer!

<answer>
The provided Python file, `adult.py`, appears to be a script associated with TensorFlow Datasets, based on the initial content and comments in the code. To effectively identify issues, I need to examine specific parts of the file that might contain errors or discrepancies, such as incorrect URLs, deprecated TensorFlow functions, or misalignment with dataset specifications. Here are a few potential issues I'll explore:

1. **Deprecated or Incorrect TensorFlow Functions**: The script uses TensorFlow functions, and any deprecated or incorrectly used functions can cause problems.
2. **URL or Data Source Errors**: Incorrect or outdated URLs for downloading datasets could prevent the dataset from being accessed or updated.
3. **Metadata or Description Errors**: Any inconsistency in the metadata or descriptions could lead to misunderstandings about the dataset's content or usage.

I'll now perform a deeper analysis of the file to identify any such issues.

Based on the review of the content in the `adult.py` file, I've identified a couple of potential issues:

### Issue 1: Inaccurate URL in Metadata
- **Issue**: The URL for the dataset in the metadata appears to be incorrect.
- **Evidence**: `url = "http://archive.ics.uci.edu/ml",`
- **Description**: The URL provided in the metadata uses HTTP instead of HTTPS, which can lead to insecure data transfer. The secure protocol (HTTPS) should be used instead, particularly for academic and data repository sites to ensure data integrity and security.

### Issue 2: Incomplete URL Formatting
- **Issue**: The URL strings for the dataset URLs contain extraneous quote marks and commas.
- **Evidence**: 
  - `https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"`
  - `https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"`
- **Description**: The URLs for downloading the dataset end with a quote mark and comma, which could potentially lead to errors when accessing these URLs programmatically. These characters should be removed to ensure the URLs are correctly formatted.

These issues, if not addressed, could lead to problems in accessing the dataset and ensuring its security and integrity when used within the TensorFlow Datasets framework or similar applications.
</answer>

-------------------- 

response below, 
1. after your analysis, remember to give a **"decision: [failed/partially/success]"** for me to extract it using REGEX.
2. Don't use Code Interpreter!; Use your ability to analyze the text.  ** Pay attention to your calculations and make sure they are correct. **
3. There could be multiple issues described in <issue> part. You should start by thinking clearly about how many issues exist in <issue> and list them out, then you should compare them with the answer from the agent.
4. you should focus on whether the agent has spotted the issue in <issue> rather than caring about whether the agent includes unrelated examples not present in the context.
