parameters: (self, validation_data, batch_size,
                                 validation_steps)
return_statement: return self._standardize_user_data(
          val_x,
          val_y,
          sample_weight=val_sample_weights,
          batch_size=batch_size,
          steps=validation_steps,
          steps_name='validation_steps')
docstring: Unpack and check the validation data.
docstring_summary: Unpack and check the validation data.
docstring_tokens: Unpack and check the validation data .
function: def _prepare_validation_data(self, validation_data, batch_size,
                                 validation_steps):
      """Unpack and check the validation data."""
      val_x, val_y, val_sample_weights = training_utils_v1.unpack_validation_data(
          validation_data)
      return self._standardize_user_data(
          val_x,
          val_y,
          sample_weight=val_sample_weights,
          batch_size=batch_size,
          steps=validation_steps,
          steps_name='validation_steps')
function_tokens: def _prepare_validation_data ( self , validation_data , batch_size , validation_steps ) : val_x , val_y , val_sample_weights = training_utils_v1 . unpack_validation_data ( validation_data ) return self . _standardize_user_data ( val_x , val_y , sample_weight = val_sample_weights , batch_size = batch_size , steps = validation_steps , steps_name = 'validation_steps' )