code: def test_from_numpy_features_only(tmp_path):
      features = np.array([1, 2, 3])
      path = tmp_path / "temp.csv"
      pd.DataFrame(features).to_csv(path)
      mlflow_features = mlflow.data.from_numpy(features, source=path)
  
      assert isinstance(mlflow_features, NumpyDataset)
      assert np.array_equal(mlflow_features.features, features)
      assert mlflow_features.schema == TensorDatasetSchema(
          features=_infer_schema(features)
      )
      assert mlflow_features.profile == {
          "features_shape": features.shape,
          "features_size": features.size,
          "features_nbytes": features.nbytes,
      }
  
      assert isinstance(mlflow_features.source, FileSystemDatasetSource)
  
imports: import json
  import numpy
  import pandas
  import pytest
  import mlflow.data
  from mlflow.data.code_dataset_source import CodeDatasetSource
  from mlflow.data.filesystem_dataset_source import FileSystemDatasetSource
  from mlflow.data.numpy_dataset import NumpyDataset
  from mlflow.data.pyfunc_dataset_mixin import PyFuncInputsOutputs
  from mlflow.data.schema import TensorDatasetSchema
  from mlflow.models.evaluation.base import EvaluationDataset
  from mlflow.types.utils import _infer_schema
  from tests.resources.data.dataset_source import TestDatasetSource
label_desc: Use image node_id 9 for calling a global function with example usage: test_from_numpy_features_only(tmp_path) without return types