#
# Names should be added to this file as:
#     Name <email address>
Raul Silvera <******@****.***>
Tipp Moseley <******@****.***>
Hyoun Kyu Cho <******@****.***>
Martin Spier <******@****.***>
Taco de Wolff <******@****.***>

			Annotations: map[string]string{kubetypes.ConfigSourceAnnotationKey: "file"},
		},
	}

	expectedPods := []*v1.Pod{
		{
			ObjectMeta: metav1.ObjectMeta{
				UID:         "999999999",
				Name:        "taco",
				Namespace:   "default",
				Annotations: map[string]string{kubetypes.ConfigSourceAnnotationKey: "api"},
			},
		},
		staticPod,
	}
	updates := append(expectedPods, mirrorPod)

  //   t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
  //   t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3
  quantized_dimension:int;
}

// Sparse tensors.
// We use a modification of the TACO format.
// Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
//
// To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),

  //   t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
  //   t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3
  quantized_dimension:int;
}

// Sparse tensors.
// We use a modification of the TACO format.
// Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
//
// To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),

  //   t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
  //   t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3
  quantized_dimension:int;
}

// Sparse tensors.
// We use a modification of the TACO format.
// Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
//
// To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),

	"syria":                                "\U0001f1f8\U0001f1fe",
	"syringe":                              "\U0001f489",
	"t-rex":                                "\U0001f996",
	"taco":                                 "\U0001f32e",
	"tada":                                 "\U0001f389",
	"taiwan":                               "\U0001f1f9\U0001f1fc",

# limitations under the License.
# ==============================================================================
"""Target aware conversion for TFLite model."""

from tensorflow.compiler.mlir.lite.experimental.tac.py_wrapper import _pywrap_tac_wrapper


def run_tac(model_path, targets, output_path):
  """Run target aware conversion for the given tflite model file.

  Args:
    model_path: Path to the tflite model file.

Bạn sẽ thấy một JSON response như:

```JSON
{"message": "Hello World"}
```

### Tài liệu tương tác API

Bây giờ tới <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.

Bạn sẽ thấy một tài liệu tương tác API (cung cấp bởi <a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank">Swagger UI</a>):

* _Đường dẫn_ `/items/{item_id}`  có một _tham số query string_ `q`, nó là một tham số tùy chọn kiểu `str`.

### Tài liệu tương tác API

Truy cập <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.

Bạn sẽ thấy tài liệu tương tác API được tạo tự động (cung cấp bởi <a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank">Swagger UI</a>):

        // file
        assertUrlLink("file:/home/taro/test.txt", //
                "file://home/taro/test.txt");
        assertUrlLink("file:/home/taro/あ.txt", //
                "file://home/taro/あ.txt");
        assertUrlLink("file:/home/taro/%E3%81%82.txt", //
                "file://home/taro/あ.txt");

        // smb->file
        assertUrlLink("smb:/home/taro/test.txt", //
                "file://home/taro/test.txt");