def test_multilingual_clip_encode_image_not_normalized(self, mock_autocast):
        """
        A test to ensure that the multilingual clip models generates the same embeddings as the pipeline for image
        inputs when normalize is set to False.
        """

        image_urls = [
            TestImageUrls.IMAGE0.value,
            TestImageUrls.IMAGE1.value,
            TestImageUrls.IMAGE2.value,
        ]

        images = [load_image_from_path(image, media_download_headers=dict()) for image in image_urls]

        preprocessed_images = self.model.get_preprocessor().preprocess(images, modality=Modality.IMAGE)
        raw_embeddings = self.model.encode(preprocessed_images, modality=Modality.IMAGE, normalize=False)

        pipeline_embeddings = self.encode_content_helper(
            content=image_urls,
            model_name=self.model_name,
            modality=Modality.IMAGE,
            device=self.device,
            normalize_embeddings=False
        )
        for i, raw_embedding in enumerate(raw_embeddings):
            pipeline_embedding = pipeline_embeddings[i]
            self.assertEqual(raw_embedding.shape, pipeline_embedding.shape)
            self.assertTrue((raw_embedding - pipeline_embedding < self.eps).all())
            self.assertEqual(raw_embedding.shape[0], self.model.model_properties.dimensions)
            self.validate_norm(raw_embedding, epsilon=self.eps, normalize=False)
            self.validate_norm(pipeline_embedding, epsilon=self.eps, normalize=False)

        mock_autocast.assert_called()

        A test to ensure that the multilingual clip models generates the same embeddings as the pipeline for image
        inputs when normalize is set to False.
        
tests/integ_tests/inference/native_inference/embedding_models/test_large_multilingual_clip_model_encode.py