code: def model_index_to_eval_results(
      model_index: List[Dict[str, Any]]
  ) -> Tuple[str, List[EvalResult]]:
      """Takes in a model index and returns the model name and a list of `huggingface_hub.EvalResult` objects.
  
      A detailed spec of the model index can be found here:
      https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
  
      Args:
          model_index (`List[Dict[str, Any]]`):
              A model index data structure, likely coming from a README.md file on the
              Hugging Face Hub.
  
      Returns:
          model_name (`str`):
              The name of the model as found in the model index. This is used as the
              identifier for the model on leaderboards like PapersWithCode.
          eval_results (`List[EvalResult]`):
              A list of `huggingface_hub.EvalResult` objects containing the metrics
              reported in the provided model_index.
  
      Example:
          ```python
          >>> from huggingface_hub.repocard_data import model_index_to_eval_results
          >>> # Define a minimal model index
          >>> model_index = [
          ...     {
          ...         "name": "my-cool-model",
          ...         "results": [
          ...             {
          ...                 "task": {
          ...                     "type": "image-classification"
          ...                 },
          ...                 "dataset": {
          ...                     "type": "beans",
          ...                     "name": "Beans"
          ...                 },
          ...                 "metrics": [
          ...                     {
          ...                         "type": "accuracy",
          ...                         "value": 0.9
          ...                     }
          ...                 ]
          ...             }
          ...         ]
          ...     }
          ... ]
          >>> model_name, eval_results = model_index_to_eval_results(model_index)
          >>> model_name
          'my-cool-model'
          >>> eval_results[0].task_type
          'image-classification'
          >>> eval_results[0].metric_type
          'accuracy'
  
          ```
      """
  
      eval_results = []
      for elem in model_index:
          name = elem["name"]
          results = elem["results"]
          for result in results:
              task_type = result["task"]["type"]
              task_name = result["task"].get("name")
              dataset_type = result["dataset"]["type"]
              dataset_name = result["dataset"]["name"]
              dataset_config = result["dataset"].get("config")
              dataset_split = result["dataset"].get("split")
              dataset_revision = result["dataset"].get("revision")
              dataset_args = result["dataset"].get("args")
              source_name = result.get("source", {}).get("name")
              source_url = result.get("source", {}).get("url")
  
              for metric in result["metrics"]:
                  metric_type = metric["type"]
                  metric_value = metric["value"]
                  metric_name = metric.get("name")
                  metric_args = metric.get("args")
                  metric_config = metric.get("config")
                  verified = metric.get("verified")
                  verify_token = metric.get("verifyToken")
  
                  eval_result = EvalResult(
                      task_type=task_type,  # Required
                      dataset_type=dataset_type,  # Required
                      dataset_name=dataset_name,  # Required
                      metric_type=metric_type,  # Required
                      metric_value=metric_value,  # Required
                      task_name=task_name,
                      dataset_config=dataset_config,
                      dataset_split=dataset_split,
                      dataset_revision=dataset_revision,
                      dataset_args=dataset_args,
                      metric_name=metric_name,
                      metric_args=metric_args,
                      metric_config=metric_config,
                      verified=verified,
                      verify_token=verify_token,
                      source_name=source_name,
                      source_url=source_url,
                  )
                  eval_results.append(eval_result)
      return name, eval_results
  
imports: import copy
  from collections import defaultdict
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  from huggingface_hub.utils import logging, yaml_dump
label_desc: Use image node_id 1 for calling a global function with example usage: model_index_to_eval_results(model_index) and returns: name,  eval_results