errors.failed_to_download_elevate_file = Yükseltme kelimesi dosyası indirilemedi.
errors.failed_to_upload_elevate_file = Yükseltme kelimesi dosyası yüklenemedi.
errors.failed_to_download_badword_file = Kötü kelime dosyası indirilemedi.
errors.failed_to_upload_badword_file = Kötü kelime dosyası yüklenemedi.
errors.failed_to_download_mapping_file = Eşleme dosyası indirilemedi.
errors.failed_to_upload_mapping_file = Eşleme dosyası yüklenemedi.

          });
          $boxElement.on("mouseleave", function () {
            isMouseHover = false;
          });

          this.resize();
          suggestor = this;
          $(window).resize(function () {
            suggestor.resize();
          });

          $("body").append($boxElement);
        },

        /**
         * Fetch and display suggestions based on current input

Türleriyle standart Python yazarsınız:

```Python
from datetime import date

from pydantic import BaseModel

# Bir değişkeni str olarak belirt
# ve fonksiyon içinde editör desteği al
def main(user_id: str):
    return user_id


# Bir Pydantic modeli
class User(BaseModel):
    id: int
    name: str
    joined: date
```

**Remarque** : si vous le faites, je vais vous demander d'aider aussi les autres. 😉

## Relire des Pull Requests { #review-pull-requests }

Vous pouvez m'aider à relire les pull requests des autres.

Encore une fois, essayez autant que possible d'être aimable. 🤗

---

Voici ce à garder à l'esprit et comment relire une pull request :

### Comprendre le problème { #understand-the-problem }

	if !reflect.DeepEqual(lc.Keys(), []string{"key1"}) {
		t.Fatalf("value differs from expected")
	}

	if lc.Resize(0) != 0 {
		t.Fatalf("evicted count differs from expected")
	}
	if lc.Resize(2) != 0 {
		t.Fatalf("evicted count differs from expected")
	}
	lc.Add("key2", "val2")
	if lc.Resize(1) != 1 {
		t.Fatalf("evicted count differs from expected")
	}
}

func TestLRUEdgeCases(t *testing.T) {

      }
      // Bool
      if (const int b_size = default_value.list().b_size()) {
        absl::InlinedVector<unsigned char, 4> b_vector;
        b_vector.reserve(b_size);
        for (int i = 0; i < b_size; i++) {
          b_vector.push_back(default_value.list().b(i));
        }
        TFE_OpSetAttrBoolList(op, attr_name, b_vector.data(), b_size);
      }
      // Type

  dlm_tensor->dl_tensor.dtype = tf_dlm_type;

  std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;
  std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
  shape_arr->resize(ndim);
  stride_arr->resize(ndim, 1);
  for (int i = 0; i < ndim; i++) {
    (*shape_arr)[i] = tensor->dim_size(i);
  }
  for (int i = ndim - 2; i >= 0; --i) {
    (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];

  outputs_.clear();
  for (TF_Operation* o : outputs) {
    outputs_.emplace_back(TF_Output{o, 0});
  }
  output_values_.resize(outputs_.size());
}

void CSession::SetOutputs(const std::vector<TF_Output>& outputs) {
  ResetOutputValues();
  outputs_ = outputs;
  output_values_.resize(outputs_.size());
}

void CSession::SetTargets(std::initializer_list<TF_Operation*> targets) {
  targets_.clear();

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger