ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float sum = 0;
  EXPECT_EQ(sizeof(sum), TF_TensorByteSize(t));
  memcpy(&sum, TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);
  EXPECT_EQ(sum, 6.0);

  TFE_DeleteTensorHandle(h0);
  TFE_DeleteTensorHandle(h1);
  TFE_DeleteTensorHandle(h2);

  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);
  ASSERT_EQ(num_elem_numerical, num_grad);

  float* dnumerical = new float[num_elem_numerical]{0};
  memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),
         TF_TensorByteSize(numerical_tensor));

  for (int j = 0; j < num_grad; j++) {
    ASSERT_NEAR(dnumerical[j], expected_grad[j], abs_error);
  }

        final boolean unicode = getFlag(NTLMSSP_NEGOTIATE_UNICODE);
        final String oemCp = unicode ? null : getOEMEncoding();

        final String domainName = getDomain();
        byte[] domainBytes = null;
        if (domainName != null && domainName.length() != 0) {
            domainBytes = unicode ? domainName.getBytes(UNI_ENCODING) : domainName.getBytes(oemCp);
            size += domainBytes.length;
        }

  int64_t num_values = 1;
  for (int i = 0; i < num_dims; ++i) {
    num_values *= dims[i];
  }
  TF_Tensor* t =
      TF_AllocateTensor(TF_INT8, dims, num_dims, sizeof(char) * num_values);
  memcpy(TF_TensorData(t), values, sizeof(char) * num_values);
  return t;
}

TF_Tensor* Int32Tensor(const int64_t* dims, int num_dims,
                       const int32_t* values) {
  int64_t num_values = 1;

  status->status = s->session->PRunSetup(input_names, output_names,
                                         target_oper_names, &new_handle);
  if (status->status.ok()) {
    char* buf = new char[new_handle.size() + 1];
    memcpy(buf, new_handle.c_str(), new_handle.size() + 1);
    *handle = buf;
  }
}

void TF_PRun(TF_DeprecatedSession* s, const char* handle,
             // Input tensors

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  std::unique_ptr<float[]> actual_values(new float[expected_values.size()]);
  EXPECT_EQ(sizeof(float) * expected_values.size(), TF_TensorByteSize(t));
  memcpy(actual_values.get(), TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);

  for (int i = 0; i < expected_values.size(); i++) {
    EXPECT_EQ(expected_values[i], actual_values[i])

  - [alpha] Kubelet launches container in a per pod cgroup hierarchy based on quality of service tier ([kubernetes/features#126](https://github.com/kubernetes/enhancements/issues/126))
  - [beta] Kubelet integrates with memcg notification API to detect when a hard eviction threshold is crossed ([kubernetes/features#125](https://github.com/kubernetes/enhancements/issues/125))

  ([#33684](https://github.com/kubernetes/kubernetes/pull/33684),
  [@fraenkel](https://github.com/fraenkel))
* Flag gate faster evictions based on node memory pressure using kernel memcg
  notifications - `--experimental-kernel-memcg-notification`.
  ([#38258](https://github.com/kubernetes/kubernetes/pull/38258),
  [@derekwaynecarr](https://github.com/derekwaynecarr))

- Kubelet's deprecated `--experimental-kernel-memcg-notification` flag is now removed.  Use `--kernel-memcg-notification` instead. ([#109388](https://github.com/kubernetes/kubernetes/pull/109388), [@ialidzhikov](https://github.com/ialidzhikov))

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