// (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

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/runtime_metadata_generated.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.h"

  bool outline_tf_while = false;
  // Whether to do shape inference.
  bool shape_inference = true;
  // Whether to do TFLite runtime verification.
  bool runtime_verification = true;
  // Whether to enable TFLite variables or not, this will allow
  // mutable variables and produce ReadVariable/AssignVariable ops in TFLite.
  bool enable_tflite_variables = false;
  // Whether to disable the variable freezing pass or not.

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);

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Ensure bucketize roundtrip exactly

func.func @main(%arg0: tensor<3x2xf32>) -> tensor<3x2xi32> {
  // CHECK-LABEL: @main
  // CHECK: "tfl.bucketize"(%arg0) <{boundaries = [0.000000e+00 : f32, 1.000000e+01 : f32, 1.000000e+02 : f32]}> : (tensor<3x2xf32>) -> tensor<3x2xi32>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Ensure cast with bfloat16 roundtrip exactly

func.func @main(tensor<4x5xbf16>) -> tensor<4x5xbf16> {
^bb0(%arg0: tensor<4x5xbf16>):
  // CHECK-LABEL: @main
  // CHECK:  (tensor<4x5xbf16>) -> tensor<4x5xf32>
  // CHECK-NEXT:  (tensor<4x5xf32>) -> tensor<4x5xbf16>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Ensure "quantfork.stats" roundtrip exactly

func.func @main(%arg0: tensor<1x512x672x8xf32>) -> tensor<1x512x672x8xf32> {
// CHECK-LABEL: @main
// CHECK: %[[RES0:.*]] = "quantfork.stats"(%arg0) <{layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>}> : (tensor<1x512x672x8xf32>) -> tensor<1x512x672x8xf32>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s

// CHECK-LABEL: main
func.func @main() -> tensor<3x2xi32> {
  // CHECK: "tfl.pseudo_const"() <{value = dense<0> : tensor<3x2xi32>}> {tfl.is_variable} : () -> tensor<3x2xi32>
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>