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());

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

# FastAPI helfen – Hilfe erhalten

Gefällt Ihnen **FastAPI**?

Möchten Sie FastAPI, anderen Benutzern und dem Autor helfen?

Oder möchten Sie Hilfe zu **FastAPI** erhalten?

Es gibt sehr einfache Möglichkeiten zu helfen (manche erfordern nur ein oder zwei Klicks).

Und es gibt auch viele Möglichkeiten, Hilfe zu bekommen.

## Newsletter abonnieren

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();

#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/quantization/quantization_passes.h"
#include "tensorflow/compiler/mlir/lite/quantization/tensorflow/passes.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_tf_xla_call_module_to_stablehlo_pass.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h"

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link