def single_model_analysis(model_location: Text, data_location: Text, output_path: Text=None, slice_spec: Optional[List[slicer.SingleSliceSpec]]=None) -> EvalResult:
    'Run model analysis for a single model on a single data set.\n\n  This is a convenience wrapper around run_model_analysis for a single model\n  with a single data set. For more complex use cases, use\n  tfma.run_model_analysis.\n\n  Args:\n    model_location: Path to the export eval saved model.\n    data_location: The location of the data files.\n    output_path: The directory to output metrics and results to. If None, we use\n      a temporary directory.\n    slice_spec: A list of tfma.slicer.SingleSliceSpec.\n\n  Returns:\n    An EvalResult that can be used with the TFMA visualization functions.\n  '
    if (output_path is None):
        output_path = tempfile.mkdtemp()
    if (not tf.io.gfile.exists(output_path)):
        tf.io.gfile.makedirs(output_path)
    eval_config = config.EvalConfig(slicing_specs=[s.to_proto() for s in slice_spec])
    return run_model_analysis(eval_config=eval_config, eval_shared_model=default_eval_shared_model(eval_saved_model_path=model_location), data_location=data_location, output_path=output_path)
Run model analysis for a single model on a single data set.

This is a convenience wrapper around run_model_analysis for a single model
with a single data set. For more complex use cases, use
tfma.run_model_analysis.

Args:
  model_location: Path to the export eval saved model.
  data_location: The location of the data files.
  output_path: The directory to output metrics and results to. If None, we use
    a temporary directory.
  slice_spec: A list of tfma.slicer.SingleSliceSpec.

Returns:
  An EvalResult that can be used with the TFMA visualization functions.
def single_model_analysis(model_location: Text, data_location: Text, output_path: Text=None, slice_spec: Optional[List[slicer.SingleSliceSpec]]=None) -> EvalResult:
    'Run model analysis for a single model on a single data set.\n\n  This is a convenience wrapper around run_model_analysis for a single model\n  with a single data set. For more complex use cases, use\n  tfma.run_model_analysis.\n\n  Args:\n    model_location: Path to the export eval saved model.\n    data_location: The location of the data files.\n    output_path: The directory to output metrics and results to. If None, we use\n      a temporary directory.\n    slice_spec: A list of tfma.slicer.SingleSliceSpec.\n\n  Returns:\n    An EvalResult that can be used with the TFMA visualization functions.\n  '
    if (output_path is None):
        output_path = tempfile.mkdtemp()
    if (not tf.io.gfile.exists(output_path)):
        tf.io.gfile.makedirs(output_path)
    eval_config = config.EvalConfig(slicing_specs=[s.to_proto() for s in slice_spec])
    return run_model_analysis(eval_config=eval_config, eval_shared_model=default_eval_shared_model(eval_saved_model_path=model_location), data_location=data_location, output_path=output_path)