class_docstr: An alternative FEDOT API version with optional attributes being documented
  and separated into groups by meaning.
  Each of the groups has corresponding setter method, named starting with :obj:`setup_*`.
  Use these methods to set corresponding API attributes:
  
  * :meth:`~FedotBuilder.setup_composition` -> general AutoML parameters
  
  * :meth:`~FedotBuilder.setup_parallelization` -> parameters of computational parallelization by CPU jobs
  
  * :meth:`~FedotBuilder.setup_output` -> parameters of outputs: logging, cache directories, etc.
  
  * :meth:`~FedotBuilder.setup_evolution` -> parameters of ML pipelines evolutionary optimization
  
  * :meth:`~FedotBuilder.setup_pipeline_structure` -> constrains on ML pipeline structure
  
  * :meth:`~FedotBuilder.setup_pipeline_evaluation` -> parameters of ML pipelines quality evaluation
  
  * :meth:`~FedotBuilder.setup_data_preprocessing` -> parameters of input data preprocessing
  
  After all demanded attributes are set, use :meth:`~FedotBuilder.build` to get a parametrized instance of
  :class:`~fedot.api.main.Fedot`.
  
  Examples:
      Example 1:
  
      .. literalinclude:: ../../../examples/simple/api_builder/classification_with_api_builder.py
  
      Example 2:
  
      .. literalinclude:: ../../../examples/simple/api_builder/multiple_ts_forecasting_tasks.py
  
  Args:
      problem: name of a modelling problem to solve.
  
          .. details:: Possible options:
  
              - ``classification`` -> for classification task
              - ``regression`` -> for regression task
              - ``ts_forecasting`` -> for time series forecasting task
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docstr: Sets parameters of ML pipelines quality evaluation.
  
  Args:
      metric:
          metric for quality calculation during composing, also is used for tuning if ``with_tuning=True``.
  
          .. details:: Default value depends on a given task:
  
              - ``roc_auc_pen`` -> for classification
              - ``rmse`` -> for regression & time series forecasting
  
          .. details:: Available metrics are listed in the following enumerations:
  
              - classification ->                         :class:`~fedot.core.repository.metrics_repository.ClassificationMetricsEnum`
              - regression ->                         :class:`~fedot.core.repository.metrics_repository.RegressionMetricsEnum`
              - time series forcasting ->                         :class:`~fedot.core.repository.metrics_repository.TimeSeriesForecastingMetricsEnum`
              - pipeline complexity (task-independent) ->                         :class:`~fedot.core.repository.metrics_repository.ComplexityMetricsEnum`
  
      cv_folds: number of folds for cross-validation.
  
          .. details:: Default value depends on a given ``problem``:
  
              - ``5`` -> for classification and regression tasks
              - ``3`` -> for time series forecasting task
  
      max_pipeline_fit_time: time constraint for operation fitting (in minutes).
          Once the limit is reached, a candidate pipeline will be dropped. Defaults to ``None`` - no limit.
  
      collect_intermediate_metric: save metrics for intermediate (non-root) nodes in composed
          :class:`Pipeline`.
  
  Returns:
      :class:`FedotBuilder` instance.
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code: def setup_pipeline_evaluation(
      self,
      metric: Union[
          MetricIDType, Sequence[MetricIDType]
      ] = DEFAULT_VALUE,
      cv_folds: int = DEFAULT_VALUE,
      max_pipeline_fit_time: Optional[int] = DEFAULT_VALUE,
      collect_intermediate_metric: bool = DEFAULT_VALUE,
  ) -> FedotBuilder:
      """ Sets parameters of ML pipelines quality evaluation.
  
          Args:
              metric:
                  metric for quality calculation during composing, also is used for tuning if ``with_tuning=True``.
  
                  .. details:: Default value depends on a given task:
  
                      - ``roc_auc_pen`` -> for classification
                      - ``rmse`` -> for regression & time series forecasting
  
                  .. details:: Available metrics are listed in the following enumerations:
  
                      - classification -> \
                          :class:`~fedot.core.repository.metrics_repository.ClassificationMetricsEnum`
                      - regression -> \
                          :class:`~fedot.core.repository.metrics_repository.RegressionMetricsEnum`
                      - time series forcasting -> \
                          :class:`~fedot.core.repository.metrics_repository.TimeSeriesForecastingMetricsEnum`
                      - pipeline complexity (task-independent) -> \
                          :class:`~fedot.core.repository.metrics_repository.ComplexityMetricsEnum`
  
              cv_folds: number of folds for cross-validation.
  
                  .. details:: Default value depends on a given ``problem``:
  
                      - ``5`` -> for classification and regression tasks
                      - ``3`` -> for time series forecasting task
  
              max_pipeline_fit_time: time constraint for operation fitting (in minutes).
                  Once the limit is reached, a candidate pipeline will be dropped. Defaults to ``None`` - no limit.
  
              collect_intermediate_metric: save metrics for intermediate (non-root) nodes in composed
                  :class:`Pipeline`.
  
          Returns:
              :class:`FedotBuilder` instance.
          """
      self.__update_params(
          metric=metric,
          cv_folds=cv_folds,
          max_pipeline_fit_time=max_pipeline_fit_time,
          collect_intermediate_metric=collect_intermediate_metric,
      )
      return self
  
imports: from __future__ import annotations
  from typing import Any, Dict, List, Optional, Sequence, Type, Union
  from golem.core.optimisers.optimizer import GraphOptimizer
  from fedot.api.main import Fedot
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.metrics_repository import MetricIDType
  from fedot.core.repository.tasks import TaskParams
label_desc: Use image node_id 8 for calling the FedotBuilder obj's underlying member method code with example usage: obj.setup_pipeline_evaluation(metric, cv_folds, max_pipeline_fit_time, collect_intermediate_metric) and returns: self