}

  for (auto it = dimension_to_splits_map->rbegin();
       it != dimension_to_splits_map->rend(); ++it) {
    int concat_dimension = it->first;
    int num_splits = it->second;

    llvm::SmallVector<mlir::Value, 4> new_outputs;
    new_outputs.reserve(num_splits);
    for (int i = 0, end = outputs_to_merge.size(); i < end;
         i = i + num_splits) {
      mlir::TF::ConcatOp concat_op =

Pydantic est une bibliothèque permettant de définir la validation, la sérialisation et la documentation des données (à l'aide de JSON Schema) en se basant sur les Python type hints.

Cela le rend extrêmement intuitif.

Il est comparable à Marshmallow. Bien qu'il soit plus rapide que Marshmallow dans les benchmarks. Et comme il est
basé sur les mêmes type hints Python, le support de l'éditeur est grand.

  if (!tail_type || !full_type || !tail_type.hasRank() ||
      !full_type.hasRank() || tail_type.getRank() > full_type.getRank())
    return false;
  auto i1 = tail_type.getShape().rbegin(), e1 = tail_type.getShape().rend();
  auto i2 = full_type.getShape().rbegin();
  return std::equal(i1, e1, i2);
}

// This function removes explicit broadcasting on type1 and returns whether if

// Read generates len(p) random bytes from the default [Source] and
// writes them into p. It always returns len(p) and a nil error.
// Read, unlike the [Rand.Read] method, is safe for concurrent use.
//
// Deprecated: For almost all use cases, [crypto/rand.Read] is more appropriate.
// If a deterministic source is required, use [math/rand/v2.ChaCha8.Read].

inline std::vector<int64_t> GetPaddedShape(ArrayRef<int64_t> old_shape,
                                           int new_dims) {
  std::vector<int64_t> new_shape(new_dims, 1);
  std::copy_backward(old_shape.begin(), old_shape.end(), new_shape.end());
  return new_shape;
}

// Helper method that given and 'current_index' representing
// index in broadcasted tensor, get the index in the flat original tensor.

See the License for the specific language governing permissions and
limitations under the License.
*/

// Package rand provides utilities related to randomization.
package rand

import (
	"math/rand"
	"sync"
	"time"
)

var rng = struct {
	sync.Mutex
	rand *rand.Rand
}{
	rand: rand.New(rand.NewSource(time.Now().UnixNano())),
}

// Int returns a non-negative pseudo-random int.
func Int() int {

	unlock(&globalRand.lock)
}

// rand32 is uint32(rand()), called from compiler-generated code.
//
//go:nosplit
func rand32() uint32 {
	return uint32(rand())
}

// rand returns a random uint64 from the per-m chacha8 state.
// Do not change signature: used via linkname from other packages.
//
//go:nosplit
//go:linkname rand
func rand() uint64 {
	// Note: We avoid acquirem here so that in the fast path

// license that can be found in the LICENSE file.

// Starting in Go 1.20, the global rand is auto-seeded,
// with a better value than the current Unix nanoseconds.
// Only seed if we're using older versions of Go.

//go:build !go1.20

package main

import (
	"math/rand"
	"time"
)

func init() {
	rand.Seed(time.Now().UnixNano())

		}
		if a.Start != b.Start {
			return cmp.Compare(aStart, bStart)
		}
		// Break ties with the end time.
		aEnd, bEnd := parsed.endTime(), parsed.endTime()
		if a.End != nil {
			aEnd = a.End.Time()
		}
		if b.End != nil {
			bEnd = b.End.Time()
		}
		return cmp.Compare(aEnd, bEnd)
	})
}

func defaultGenOpts() *genOpts {
	return &genOpts{
		startTime: time.Duration(0),

  // rank of 2 (no batch dimension).
  SmallVector<int64_t, 2> new_filter_shape(filter_shape.rbegin(),
                                           filter_shape.rend());

  // Construct the value array of transposed filter. Assumes 2D matrix.
  SmallVector<int8_t> new_filter_values(filter_values.size(), /*Value=*/0);
  for (int i = 0; i < filter_shape[0]; ++i) {