code: def validate_model(
      mod: model.Model, ds: dataset.Dataset, report_all: bool = False
  ) -> None:
      """Validate model usage on dataset against specs.
  
      Args:
        mod: The Model providing the predictions that are being validated.
        ds: The Dataset providing the examples for which the model referenced by
          `mod` makes predictions.
        report_all: If `True`, log all errors before raising the first error
          encountered in the validation of the Model referenced by `mod`.
  
      Raises:
        ValueError: The first instance of one of the following conditions occurring
          during validation:
          * A required output field is missing from a prediction.
          * The value for a predicted field fails valdiation via
            `LitType.validate_output()`.
      """
      # If report_all is True, first_error stores the first error encountered during
      # the validation process, which is then raised at the end of processing.
      first_error: Optional[ValueError] = None
      # If report_all is True, first_error_origin stores the ValueError raised by
      # LitType.validate_output() if a datapoint fails validation.
      first_error_origin: Optional[ValueError] = None
  
      def raise_or_log_error(
          msg: str, origin: Optional[ValueError] = None
      ) -> ValueError:
          """Raise (if report_all=False) or log (and return) a validation error."""
          if report_all:
              logging.error(termcolor.colored(msg, "red"))
              return ValueError(msg)
          else:
              raise ValueError(msg) from origin
  
      outputs = list(mod.predict(ds.examples))
      for ex, output in zip(ds.examples, outputs):
          for key, entry in mod.output_spec().items():
              value = output.get(key)
              if value is None:
                  if entry.required:
                      err = raise_or_log_error(
                          f'Required model output "{key}" is missing from prediction.'
                      )
                      first_error = first_error or err
              else:
                  try:
                      entry.validate_output(
                          value,
                          mod.output_spec(),
                          output,
                          mod.input_spec(),
                          ds.spec(),
                          cast(types.Input, ex),
                      )
                  except ValueError as e:
                      err = raise_or_log_error(
                          f'Failed while validating model output field "{key}" of type'
                          f' "{type(entry)}".',
                          e,
                      )
                      first_error = first_error or err
  
      if first_error:
          raise first_error from first_error_origin
  
file_path: lit/lit_nlp/lib/validation.py
imports: from typing import cast, Optional
  from absl import logging
  from lit_nlp.api import dataset
  from lit_nlp.api import model
  from lit_nlp.api import types
  import termcolor
label_desc: Use image node_id 2 for calling a global function with example usage: validate_model(mod, ds, report_all) and returns: ValueError