dyn_cast_or_null<TFL::MulOp>(fc_op.getInput().getDefiningOp());
    if (!mul_op) return failure();
    if (mul_op.getFusedActivationFunction() != "NONE") return failure();

    // Don't match muls where the multiplier constant is not 1D.
    {
      auto multiplier_shape = mlir::cast<ShapedType>(mul_op.getRhs().getType());
      if (!multiplier_shape.hasStaticShape()) return failure();

* L'apprentissage profond (ou **Deep Learning**) : est un sous-domaine du **Machine Learning**, donc les mêmes raisons s'appliquent. Avec la différence qu'il n'y a pas une unique feuille de calcul de nombres à multiplier, mais une énorme quantité d'entre elles, et dans de nombreux cas, on utilise un processeur spécial pour construire et...

			//
			//   for i := 0; i < n; i++ {
			//       d := bLimbs[i]
			//       T[n+i] = addMulVVW(T[i:n+i], aLimbs, d)
			//   }
			//
			// where d is a digit of the multiplier, T[i:n+i] is the shifted
			// position of the product of that digit, and T[n+i] is the final carry.
			// Note that T[i] isn't modified after processing the i-th digit.
			//

    if (is_depthwise) {
      // The total number of depthwise convolution output channels will be
      // equal to input channel * `depth_multiplier`.
      const int64_t multiplier = dimension_numbers.getOutputFeatureDimension() /
                                 dimension_numbers.getInputFeatureDimension();

      rewriter.replaceOpWithNewOp<TFL::DepthwiseConv2DOp>(

    /**
     * A helper method to determine the value to resume the build with {@code -rf} taking into account the edge case
     *   where multiple modules in the reactor have the same artifactId.
     * <p>
     * {@code -rf :artifactId} will pick up the first module which matches, but when multiple modules in the reactor
     *   have the same artifactId, effective failed module might be later in build reactor.

    // results for depthwise transpose convolutions with non-1 channel
    // multiplier.
    if ((kernel_output_channels / feature_group_count) != 1) {
      return rewriter.notifyMatchFailure(
          conv_op,
          "Unsupported detphwise transpose convolution with non-1 channel "
          "multiplier");
    }

    // Slicing with dynamic offsets (helper method advised)

## Multiple body parameters

In the previous example, the *path operations* would expect a JSON body with the attributes of an `Item`, like:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

But you can also declare multiple body parameters, e.g. `item` and `user`:

=== "Python 3.10+"

  const int64_t outer_size = std::accumulate(
      outer_dims.begin(), outer_dims.end(), 1, std::multiplies<int64_t>());

  const auto base_inner_dims = output_type.getShape().drop_front(axis + 1);
  const int64_t base_inner_size =
      std::accumulate(base_inner_dims.begin(), base_inner_dims.end(), 1,
                      std::multiplies<int64_t>());

  // Splits each input operand into outer_size pieces and combines them in

// This test verifies there is no warning about shape inference failure in TPU
// bridge in handling multiple usage of the same function.

// Since it is possible that this warning may become an error in the future,
// only check the message content here.

Jonh Wendell <******@****.***> 1700195286 -0500