code: def test_api_forecast_numpy_input_with_static_model_correct(
      task_type: str = "ts_forecasting",
  ):
      forecast_length = 2
      train_data, test_data, _ = get_dataset(
          task_type, validation_blocks=1
      )
      model = Fedot(
          problem="ts_forecasting",
          task_params=TsForecastingParams(
              forecast_length=forecast_length
          ),
      )
  
      # Define pipeline for prediction
      node_lagged = PipelineNode("lagged")
      pipeline = Pipeline(
          PipelineNode("linear", nodes_from=[node_lagged])
      )
  
      model.fit(
          features=train_data.features,
          target=train_data.target,
          predefined_model=pipeline,
      )
      ts_forecast = model.predict(features=test_data, in_sample=False)
      metric = model.get_metrics(
          target=test_data.target, metric_names="rmse"
      )
  
      assert len(ts_forecast) == forecast_length
      assert all(value > 0 for value in metric.values())
  
imports: import os
  import shutil
  from copy import deepcopy
  import numpy
  import pandas
  import pytest
  from golem.core.dag.graph_utils import graph_structure
  from sklearn.datasets import load_iris
  from sklearn.preprocessing import LabelEncoder
  from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline
  from fedot import Fedot
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.tasks import TsForecastingParams
  from test.data.datasets import get_dataset, get_multimodal_ts_data, load_categorical_unimodal, load_categorical_multidata
  from test.unit.common_tests import is_predict_ignores_target
  from test.unit.tasks.test_multi_ts_forecast import get_multi_ts_data
label_desc: Use image node_id 7 for calling a global function with example usage: test_api_forecast_numpy_input_with_static_model_correct(task_type) without return types