code: def validate_model(data: "tabular.Dataset", model: t.Any):
      """Check model is able to predict on the dataset.
  
      Parameters
      ----------
      data : t.Union['base.Dataset', pd.DataFrame]
      model : t.Any
      Raises
      ------
      DeepchecksValueError
          if dataset does not match model.
      """
      error_message = (
          "In order to evaluate model correctness we need not empty dataset "
          "with the same set of features that was used to fit the model. {0}"
      )
  
      if isinstance(data, tabular.Dataset):
          features = data.data[data.features]
      else:
          features = data
  
      if features is None:
          raise errors.DeepchecksValueError(
              error_message.format(
                  "But function received dataset without feature columns."
              )
          )
  
      if len(features) == 0:
          raise errors.DeepchecksValueError(
              error_message.format(
                  "But function received empty dataset."
              )
          )
  
      try:
          model.predict(features.head(1))
      except Exception as exc:
          raise errors.ModelValidationError(
              f"Got error when trying to predict with model on dataset: {str(exc)}"
          )
  
file_path: deepchecks/deepchecks/tabular/utils/validation.py
imports: import typing
  import numpy
  import pandas
  from deepchecks import tabular
  from deepchecks.core import errors
  from deepchecks.utils.typing import BasicModel
label_desc: Use image node_id 2 for calling a global function with example usage: validate_model(data, model) without return types