@Override
  public int intValue() {
    return (int) sum();
  }

  /** Returns the {@link #sum} as a {@code float} after a widening primitive conversion. */
  @Override
  public float floatValue() {
    return (float) sum();
  }

  /** Returns the {@link #sum} as a {@code double} after a widening primitive conversion. */
  @Override
  public double doubleValue() {

)

// ODirectPlatform indicates if the platform supports O_DIRECT
const ODirectPlatform = true

// OpenFileDirectIO - bypass kernel cache.
func OpenFileDirectIO(filePath string, flag int, perm os.FileMode) (*os.File, error) {
	return directio.OpenFile(filePath, flag, perm)
}

// DisableDirectIO - disables directio mode.
func DisableDirectIO(f *os.File) error {
	fd := f.Fd()
	_, err := unix.FcntlInt(fd, unix.F_NOCACHE, 0)
	return err

  values[0] = v;
  return TF_NewTensor(TF_DOUBLE, nullptr, 0, values, num_bytes,
                      &DoubleDeallocator, nullptr);
}

TF_Tensor* FloatTensor(float v) {
  const int num_bytes = sizeof(float);
  float* values = new float[1];
  values[0] = v;
  return TF_NewTensor(TF_FLOAT, nullptr, 0, values, num_bytes,
                      &FloatDeallocator, nullptr);
}

    visibility = ["//visibility:public"],
)

# This flag disables generating tensorflow.lite.python under LiteRT repo.
# Avoid using flag for creating tflite wheels as tensorflow/lite is not yet fully split from tf.
config_setting(
    name = "disable_tf_lite_py",
    define_values = {"disable_tf_lite_py": "true"},
    visibility = ["//visibility:public"],
)

# This flag enables experimental MLIR support.
config_setting(

    enum: Optional[list[Any]] = None
    const: Optional[Any] = None
    multipleOf: Optional[float] = Field(default=None, gt=0)
    maximum: Optional[float] = None
    exclusiveMaximum: Optional[float] = None
    minimum: Optional[float] = None
    exclusiveMinimum: Optional[float] = None
    maxLength: Optional[int] = Field(default=None, ge=0)
    minLength: Optional[int] = Field(default=None, ge=0)

package tests_test

import (
	"errors"
	"testing"

	"gorm.io/gorm"
	"gorm.io/gorm/utils/tests"
)

func TestDialectorWithErrorTranslatorSupport(t *testing.T) {
	// it shouldn't translate error when the TranslateError flag is false
	translatedErr := errors.New("translated error")
	untranslatedErr := errors.New("some random error")
	db, _ := gorm.Open(tests.DummyDialector{TranslatedErr: translatedErr})

	err := db.AddError(untranslatedErr)

        }
      }
    }
  }
}
"""


class SponsorEntity(BaseModel):
    login: str
    avatarUrl: str
    url: str


class Tier(BaseModel):
    name: str
    monthlyPriceInDollars: float


class SponsorshipAsMaintainerNode(BaseModel):
    sponsorEntity: SponsorEntity
    tier: Tier


class SponsorshipAsMaintainerEdge(BaseModel):
    cursor: str
    node: SponsorshipAsMaintainerNode

  @CanIgnoreReturnValue
  public final Hasher putDouble(double d) {
    return putLong(Double.doubleToRawLongBits(d));
  }

  @Override
  @CanIgnoreReturnValue
  public final Hasher putFloat(float f) {
    return putInt(Float.floatToRawIntBits(f));
  }

  @Override
  @CanIgnoreReturnValue
  public Hasher putUnencodedChars(CharSequence charSequence) {
    for (int i = 0, len = charSequence.length(); i < len; i++) {

        try {
            if (threadConfiguration.endsWith("C")) {
                String str = threadConfiguration.substring(0, threadConfiguration.length() - 1);
                float coreMultiplier = Float.parseFloat(str);

                if (coreMultiplier <= 0.0f) {
                    throw new IllegalArgumentException("Invalid threads core multiplier value: '" + threadConfiguration

// Create 1 dimensional tensor with values from `values`
TF_Tensor* Int32Tensor(const std::vector<int32_t>& values);

TF_Tensor* Int32Tensor(int32_t v);

TF_Tensor* DoubleTensor(double v);

TF_Tensor* FloatTensor(float v);

TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
                          const char* name = "feed",
                          TF_DataType dtype = TF_INT32,