memcpy(buf, new_handle.c_str(), new_handle.size() + 1);
    *handle = buf;
  }
}

void TF_DeletePRunHandle(const char* handle) {
  delete[] handle;
  // TODO(suharshs): Free up any resources held by the partial run state.
}

void TF_SessionPRun(TF_Session* session, const char* handle,
                    const TF_Output* inputs, TF_Tensor* const* input_values,

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  Status BuildOnesLike(const TapeTensor& t,
                       AbstractTensorHandle** result) const override;

  // Looks up the ID of a Gradient.
  int64_t TensorId(AbstractTensorHandle* tensor) const override;

  // Converts a Gradient to a TapeTensor.
  TapeTensor TapeTensorFromGradient(AbstractTensorHandle* g) const override;

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(0, num_dims);
  TF_GraphGetTensorShape(graph, three_out_0, returned_dims, num_dims, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Clean up
  TF_DeleteGraph(graph);
  TF_DeleteStatus(s);
}

TEST(CAPI, Graph) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Make a placeholder operation.

  tensorflow::unwrap(ctx)->SetThreadLocalDevicePlacementPolicy(
      static_cast<tensorflow::ContextDevicePlacementPolicy>(policy));
}

// Note: this function looks up a thread local policy. So it should be called in
// the appropriate client thread. In particular, in async mode, it may not be
// safe to call this function from the async EagerExecutor threads.