Si vous souhaitez suivre un cours pour débutants avancés pour compléter cette section de la documentation, vous pouvez consulter : <a href="https://testdrive.io/courses/tdd-fastapi/" class="external- link" target="_blank">Développement piloté par les tests avec FastAPI et Docker</a> par **TestDriven.io**.

#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"

#include <string>

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/op_or_arg_name_mapper.h"

namespace tflite {

// Translates the given MLIR `module` into a FlatBuffer and stores the
// serialized flatbuffer into the string. This uses OpOrArgLocNameMapper to

// RUN: tf-opt --tfl-legalize-tf-while %s -o - | flatbuffer_translate -mlir-to-tflite-flatbuffer - -o -  | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s

// CHECK-LABEL: main
func.func @main(%arg0: tensor<*xf32>) -> tensor<*xf32> {
  // CHECK:     %{{.*}} = tfl.add %{{.*}}, %{{.*}} {fused_activation_function = "NONE"} : tensor<*xf32>
  // CHECK:     return %{{.*}} : tensor<*xf32>

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/stateful_ops_utils.h"

// Background info:
// Currently the model taken to MLIRConverter is frozen (all the variables have
// been converted to constants, all the assign ops are gone, etc.). However,

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s
// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | flatbuffer_translate -mlir-to-tflite-flatbuffer - -o - | flatbuffer_to_string - | FileCheck --check-prefix=RoundTrip %s

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

func.func @main(%arg0 : tensor<!tf_type.variant<tensor<2xi32>>>, %arg1: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>> {
  func.return %arg0 : tensor<!tf_type.variant<tensor<2xi32>>>
}

are left as TensorFlow operations. Unsupported op handling follows the
proposed TFLite mechanism.
- Optimizations are performed in both the TF & TFLite dialect; aiming for small
size and high performance (among the core value proposition of
TensorFlow Lite models).
- The Export pass writes out TensorFlow Lite FlatBuffer format. This pass
operates on MLIR TensorFlow Lite dialect and is simple/direct translation.

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

// Check to see if nested regions in while loops are preserved
// CHECK:     %{{.*}}:2 = "tfl.while"(%{{.*}}, %{{.*}}) ({
// CHECK:     ^bb0(%{{.*}}: tensor<*xi32>, %{{.*}}: tensor<*xf32>):
// CHECK:       "tfl.yield"(%{{.*}}) : (tensor<i1>) -> ()
// CHECK:     },  {
// CHECK:     ^bb0(%{{.*}}: tensor<*xi32>, %{{.*}}: tensor<*xf32>):

   *
   * <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