# CHECK:     version: 1
# CHECK:     builtin_code: RESHAPE
# CHECK:   } ],
# CHECK:   subgraphs: [ {
# CHECK:     tensors: [ {
# CHECK:       shape: [ 1, 1, 1, 256 ],
# CHECK:       type: INT8,
# CHECK:       buffer: 1,
# CHECK:       name: "input",
# CHECK:       quantization: {
# CHECK:         scale: [ 0.216328 ],
# CHECK:         zero_point: [ 27 ]
# CHECK:       }
# CHECK:     }, {

	} 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)
	}

			}
		}
	case reflect.Int, reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64:
		field.DataType = Int
		if field.HasDefaultValue && !skipParseDefaultValue {
			if field.DefaultValueInterface, err = strconv.ParseInt(field.DefaultValue, 0, 64); err != nil {
				schema.err = fmt.Errorf("failed to parse %s as default value for int, got error: %v", field.DefaultValue, err)
			}
		}

// license that can be found in the LICENSE file.

package types

// BasicKind describes the kind of basic type.
type BasicKind int

const (
	Invalid BasicKind = iota // type is invalid

	// predeclared types
	Bool
	Int
	Int8
	Int16
	Int32
	Int64
	Uint
	Uint8
	Uint16
	Uint32
	Uint64
	Uintptr
	Float32
	Float64
	Complex64
	Complex128
	String

// The input of the quantize function has one operand of
// IsValueWithQuantizablePrecision and the output is a tensor with supported
// quantized precision (like int8). For dequantize function, it is the other way
// around.

#ifndef TENSORFLOW_COMPILER_MLIR_QUANTIZATION_TENSORFLOW_OPS_TF_QUANTIZE_OP_H_
#define TENSORFLOW_COMPILER_MLIR_QUANTIZATION_TENSORFLOW_OPS_TF_QUANTIZE_OP_H_

}

type mapResult struct {
	value any
	ok    bool
}

func randValue(r *rand.Rand) any {
	b := make([]byte, r.Intn(4))
	for i := range b {
		b[i] = 'a' + byte(rand.Intn(26))
	}
	return string(b)
}

func (mapCall) Generate(r *rand.Rand, size int) reflect.Value {
	c := mapCall{op: mapOps[rand.Intn(len(mapOps))], k: randValue(r)}
	switch c.op {
	case opStore, opLoadOrStore:
		c.v = randValue(r)
	}

  }
  const double fraction = std::frexp(double_multiplier, &shift);
  quantized_fraction = static_cast<int32_t>(std::round(fraction * (1L << 15)));
  // Clip extreme values.  These are more than enough to overflow int8, the
  // storage type for quantized values, and the final values will be clamped
  // no matter what.
  if (quantized_fraction == (1L << 15)) {
    quantized_fraction /= 2;
    ++shift;
  }
  if (shift < -15) {

    } else if (IsQI8Type(input_type)) {
      int8_type_observed = true;
    } else if (IsQUI8Type(input_type)) {
      uint8_type_observed = true;
    }
  }

  // We should not observe both uint8 & int8.
  if (int8_type_observed && uint8_type_observed) return UNKNOWN;

  if (float_type_observed) {
    if (int8_type_observed || uint8_type_observed) {
      return HYBRID;
    } else {
      return FLOAT;
    }

  } : (tensor<2x2xf32>, tensor<f32>, tensor<i32>) -> tensor<2x2x!tf_type.qint8>

  // CHECK: %[[WHILE_RESULT:.*]]:2 = mhlo.while(%[[ARG0:.*]] = %[[INT0]], %[[ARG1:.*]] = %[[INT1]])
  // CHECK-SAME: tensor<2x2xi8>, tensor<2x2xi8>

  // CHECK: cond

  // CHECK: do
  // CHECK: mhlo.return %[[ARG0]], %[[ARG1]] : tensor<2x?xi8>, tensor<?x2xi8>

  %2:2 = "tf.WhileRegion"(%0, %1) ({

		want string
	}{
		{"uint64", uint64(12345678), "12345678"},
		{"uint32", uint32(1234), "1234"},
		{"uint16", uint16(12), "12"},
		{"uint8", uint8(1), "1"},
		{"uint", uint(123), "123"},
		{"int", int(123), "123"},
		{"int8", int8(1), "1"},
		{"int16", int16(12), "12"},
		{"int32", int32(1234), "1234"},
		{"int64", int64(12345678), "12345678"},
		{"float32", float32(1.5), "1.5"},
		{"float64", float64(64), "64"},