TF_DeleteCheckpointReader(reader);
    return nullptr;
  }
  const auto& m = reader->GetVariableToDataTypeMap();
  for (auto it = m.begin(); it != m.end(); ++it)
    reader->variable_list.push_back(it->first);
  std::sort(reader->variable_list.begin(), reader->variable_list.end());
  return reader;
}

void TF_DeleteCheckpointReader(TF_CheckpointReader* reader) { delete reader; }

namespace gradients {

using namespace std;

// ================== Helper functions =================

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.

    input_tensors->emplace_back(node, idx);

    const auto& iter = input_nodes->find(node);
    if (iter == input_nodes->end()) {
      input_nodes->insert({node, {idx}});
    } else {
      auto& indices = iter->second;
      if (std::find(indices.begin(), indices.end(), idx) != indices.end()) {
        return InvalidArgument("TF_Output ", node->name(), ":", idx,

  ASSERT_TF_OK(status_);
  ASSERT_EQ(bucket, "bucket");

  ParseGCSPath("bucket/path/to/object", false, &bucket, &object, status_);
  ASSERT_EQ(TF_GetCode(status_), TF_INVALID_ARGUMENT);

  // bucket name must end with "/"
  ParseGCSPath("gs://bucket", true, &bucket, &object, status_);
  ASSERT_EQ(TF_GetCode(status_), TF_INVALID_ARGUMENT);

  ParseGCSPath("gs://bucket/", false, &bucket, &object, status_);

  for (size_t i = 0; i < nodes_preserved.size(); ++i) {
    EXPECT_EQ(nodes_preserved.find(string(static_cast<const char*>(values[i]),
                                          lens[i])) != nodes_preserved.end(),
              true);
  }
  TF_DeleteStatus(status);
}

TEST(TF_GrapplerItem, FetchNodes) {
  GrapplerItem item;
  item.fetch = std::vector<string>{"Conv", "BiasAdd"};

    //     and places the 'read0_maybe_with_graph_error' op on task:2
    //   * task:0 partitions the main function with a subgraph containing
    //     'read0_maybe_with_graph_error' sent to task:2
    //   * task:2 graph pass reports an error when it sees
    //     'read0_maybe_with_graph_error' with dev2_name
    tensorflow::function_optimization_registration::

              combined_shape.SetDimWithStatus(axis_index, axis_length));
        }
      }
    }
    auto dim_sizes = combined_shape.dim_sizes();
    shape_ = std::vector<int64_t>(dim_sizes.begin(), dim_sizes.end());
  }
  *shape = &*shape_;
  return absl::OkStatus();
}

Status ParallelTensor::SummarizeValue(std::string& summary) {
  summary = "{";

    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());
  }

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_1);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());
  }

  // Create a variable using `ctx_0`.

  TFE_OpSetAttrString(op, "tensor_name", "dummy", 5);
  TFE_OpSetAttrString(op, "send_device", send_device.c_str(),
                      send_device.size());
  TFE_OpSetAttrString(op, "recv_device", recv_device.c_str(),
                      recv_device.size());
  TFE_OpSetAttrInt(op, "send_device_incarnation", send_device_incarnation);

  return op;
}

  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle(ctx);
  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle(ctx);
  std::vector<TFE_TensorHandle*> handles_task0;
  if (heavy_load_on_streaming_rpc) {
    // Send 50 tensor copy requests to simulate that there have been some RPC
    // requests been enqueued.
    for (int i = 0; i < 50; ++i) {
      handles_task0.push_back(TestMatrixTensorHandle(ctx));
    }
  }