.Case("TANH", tflite::ActivationFunctionType_TANH)
      .Case("SIGN_BIT", tflite::ActivationFunctionType_SIGN_BIT);
}

static tflite::TensorType ConvertDerivedTFLiteTypeAttrForOptionWriter(
    tflite::TensorType type, flatbuffers::FlatBufferBuilder* builder) {
  if (type == tflite::TensorType_INT64) {
    return tflite::TensorType_INT64;
  } else if (type == tflite::TensorType_INT32) {

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/validators.h"
#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_config.h"

        }
      }
    }

    os << "  tflite::" << tflite_option_name << "Builder b(*fbb);\n";
    for (const auto &option : options)
      os << formatv("  b.add_{0}(std::move({0}));\n", option);
    os << "  return b.Finish();\n}\n";
  }
}

// For each TFLite op, emits a builder function that packs the TFLite op into
// the corresponding FlatBuffer object.
//

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();

// (The caller handles Inf before invoking fmtB.)
func (x *Float) fmtB(buf []byte) []byte {
	if x.form == zero {
		return append(buf, '0')
	}

	if debugFloat && x.form != finite {
		panic("non-finite float")
	}
	// x != 0

	// adjust mantissa to use exactly x.prec bits
	m := x.mant
	switch w := uint32(len(x.mant)) * _W; {
	case w < x.prec:
		m = nat(nil).shl(m, uint(x.prec-w))

If you want to explore the SQLite database (file) directly, independently of FastAPI, to debug its contents, add tables, columns, records, modify data, etc. you can use <a href="https://sqlitebrowser.org/" class="external-link" target="_blank">DB Browser for SQLite</a>.

It will look like this:

<img src="/img/tutorial/sql-databases/image02.png">

   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

bool IsStablehloOp(const tflite::OperatorCodeT &op_code);

// Returns the MLIR op name for the flatbuffer operator corresponding to
// `op_code`.
std::string GetMlirOpNameFromOpCode(const ::tflite::OperatorCodeT &op_code);

// Returns the builtin op code for the given MLIR operation on success; emits
// error and returns std::nullopt on failure.
std::optional<tflite::BuiltinOperator> GetBuiltinOpCode(Operation *mlir_op);

  for (auto dim : tensor.shape) {
    shape->add_dim()->set_size(int64_t{dim});
  }
  // TensorFlow Lite uses tflite::DynamicBufer to encode vector of strings.
  if (tensor.type == tflite::TensorType_STRING) {
    for (int i = 0; i < tflite::GetStringCount(buffer.data()); ++i) {
      tflite::StringRef str = tflite::GetString(buffer.data(), i);
      ret.add_string_val(str.str, str.len);
    }
    return ret;
  }

          llvm::dyn_cast_or_null<mlir::TF::TensorListReserveOp>(op)) {
    tflite::TensorType tflite_type =
        tflite::ConvertTypeToTensorType(reserve.getElementDtype());

    return CreateListReserveOptions(context, tflite_type);
  }
  if (auto empty = llvm::dyn_cast_or_null<mlir::TF::EmptyTensorListOp>(op)) {
    tflite::TensorType tflite_type =
        tflite::ConvertTypeToTensorType(empty.getElementDtype());