TF_RETURN_IF_ERROR(GetValue(theta, &theta_tensor));

  // Get number of elements and fill data.
  int num_elems = TF_TensorElementCount(theta_tensor);
  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {
  const auto& debug_str = DebugString(func->record->fdef());
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

    memcpy(&result_data[0], TF_TensorData(f_t), TF_TensorByteSize(f_t));

    // Verify results for each output
    for (int j = 0; j < 4; j++) {
      ASSERT_EQ(result_data[j], expected_outputs[idx][j]);
    }

    TF_DeleteTensor(f_t);
  }

  // Free memory associated with add and MatMul outputs
  for (int idx = 0; idx < 3; ++idx) {

  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);
  }

  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])

  }

  TF_Tensor* FloatTensor2x2(const float* values) {
    const int64_t dims[2] = {2, 2};
    TF_Tensor* t = TF_AllocateTensor(TF_FLOAT, dims, 2, sizeof(float) * 4);
    memcpy(TF_TensorData(t), values, sizeof(float) * 4);
    return t;
  }

  TF_Operation* FloatConst2x2(TF_Graph* graph, TF_Status* s,
                              const float* values, const char* name) {

| `minio_node_mem_buffers_avg`   | Buffers memory on the node (avg).         |
| `minio_node_mem_buffers_max`   | Buffers memory on the node (max).         |
| `minio_node_mem_cache`         | Cache memory on the node.                 |
| `minio_node_mem_cache_avg`     | Cache memory on the node (avg).           |
| `minio_node_mem_cache_max`     | Cache memory on the node (max).           |

    ]),
  ], panelHeight=5)
  + grid.makeGrid([
    row.new('Resource Usage')
    + row.withPanels([
      panels.timeSeries.bytes('Memory Usage', queries.goMemoryUsage, 'Memory usage of each running instance'),
      panels.timeSeries.allocations('Memory Allocations', queries.goAllocations, 'Details about memory allocations'),
      panels.timeSeries.base('CPU Usage', queries.cpuUsage, 'CPU usage of each running instance'),