// to the `output_buffer`. Both `model_buffer` and `output_buffer` should be a
// valid FlatBuffer format for Model supported by TFLite.
//
// The `input_type`, `output_type` and `inference_type` can be float32 / qint8 /
// int8 / int16.
//
// Returns a partially quantized model if `fully_quantize` is false. Returns a
// non-OK status if the quantization fails.
//
// When `verify_numeric` is true, the model will have it's original float ops

			db.RowsAffected++
			db.AddError(rows.Scan(values...))

			mapValue := map[string]interface{}{}
			scanIntoMap(mapValue, values, columns)
			*dest = append(*dest, mapValue)
		}
	case *int, *int8, *int16, *int32, *int64,
		*uint, *uint8, *uint16, *uint32, *uint64, *uintptr,
		*float32, *float64,
		*bool, *string, *time.Time,
		*sql.NullInt32, *sql.NullInt64, *sql.NullFloat64,

	} else if resPointer.ID != user3.ID || resPointer.Name != user3.Name || resPointer.Age != int(user3.Age) {
		t.Fatalf("Scan into struct should work, got %#v, should %#v", res, user3)
	}

	DB.Table("users").Select("id, name, age").Where("id = ?", user2.ID).Scan(&res)
	if res.ID != user2.ID || res.Name != user2.Name || res.Age != int(user2.Age) {
		t.Fatalf("Scan into struct should work, got %#v, should %#v", res, user2)
	}

          /*used_tensors=*/used_tensors, /*quantized=*/true);
      // If the tensor is a weight, it should have type INT8.
      // If the tensor is a bias, it should have type FLOAT32.
      // If the tensor is an input or output it should have type FLOAT32.
      // The input to dequantize should be INT8, and all other tensors should be
      // FLOAT32.
      if (i == dequant_input_idx) {

	assert.NoError(t, err)

	init1 := makeTestContainer("init1", "busybox")
	init2 := makeTestContainer("init2", "busybox")
	pod := &v1.Pod{
		ObjectMeta: metav1.ObjectMeta{
			UID:       "12345678",
			Name:      "foo",
			Namespace: "new",
		},
		Spec: v1.PodSpec{
			InitContainers: []v1.Container{init1, init2},
		},
	}

	templates := []containerTemplate{

}

// PopulateABIInRegArgOps examines the entry block of the function
// and looks for incoming parameters that have missing or partial
// OpArg{Int,Float}Reg values, inserting additional values in
// cases where they are missing. Example:
//
//	func foo(s string, used int, notused int) int {
//	  return len(s) + used
//	}
//
// In the function above, the incoming parameter "used" is fully live,

    }
  }
  return 0;
}

PyObject* MlirQuantizeModel(PyObject* data, bool disable_per_channel,
                            bool fully_quantize, int inference_type,
                            int input_data_type, int output_data_type,
                            bool enable_numeric_verify,
                            bool enable_whole_model_verify,

)

// ConstBool returns an int constant representing its argument.
func (f *Func) ConstBool(t *types.Type, c bool) *Value {
	i := int64(0)
	if c {
		i = 1
	}
	return f.constVal(OpConstBool, t, i, true)
}
func (f *Func) ConstInt8(t *types.Type, c int8) *Value {
	return f.constVal(OpConst8, t, int64(c), true)
}
func (f *Func) ConstInt16(t *types.Type, c int16) *Value {

# tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int32)
# tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int64)

lhs = np.array([-2, 64, 101, 32], dtype=np.int8)
rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
bitwise_ops.left_shift(lhs, rhs)
# <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2,  64, 101,  32], dtype=int8)>
```
  }];

  let arguments = (ins
    TF_IntTensor:$x,

  ASSERT_THAT(status, Eq(kTfLiteOk));
  const auto& subgraph = model_.subgraphs[0];
  auto conv_op = subgraph->operators[0].get();
  const int input_tensor_idx = 0;
  const int weights_tensor_idx = 1;
  const int bias_tensor_index = 2;
  const int output_tensor_idx = 0;
  const auto bias_tensor =
      subgraph->tensors[conv_op->inputs[bias_tensor_index]].get();
  const auto input_tensor =