TEST_F(CApiAttributesTest, ShapeList) {
  const int64_t shape_1[] = {1, 3};
  const int64_t shape_2[] = {2, 4, 6};
  const int64_t* list[] = {&shape_1[0], &shape_2[0]};
  const size_t list_size = TF_ARRAYSIZE(list);
  const int ndims[] = {TF_ARRAYSIZE(shape_1), TF_ARRAYSIZE(shape_2)};
  const int total_ndims = 5;  // ndims[0] + ndims[1]

  auto desc = init("list(shape)");
  TF_SetAttrShapeList(desc, "v", list, ndims, list_size);

// setting a shape of [-1, 2] with an existing shape [2, -1] would set
// a final shape of [2, 2] based on shape merging semantics.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.
//   * An invalid shape is being set (e.g., the shape being set
//     is incompatible with the existing shape).
TF_CAPI_EXPORT extern void TF_GraphSetTensorShape(TF_Graph* graph,

    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

    TFE_Op* shape_op = ShapeOp(ctx, hgpu);
    TFE_OpSetDevice(shape_op, gpu_device_name.c_str(), status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
    TFE_TensorHandle* retvals[1];
    int num_retvals = 1;
    TFE_Execute(shape_op, &retvals[0], &num_retvals, status.get());

                                                           TF_DeleteStatus);

  TF_Tensor* tensor_shape = Int32Tensor({37, 1});
  TF_Operation* shape = Const(tensor_shape, func_graph.get(), s.get(), "shape");
  TF_Operation* random =
      RandomUniform(shape, TF_FLOAT, func_graph.get(), s.get());

  TF_Output outputs[] = {{random, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name,

        assertFalse(sentPrompt.contains("{{conversationHistory}}"));
    }

    // ========== History content mode pattern tests ==========
    // These test the different history shapes that extractHistory() produces
    // depending on rag.chat.history.assistant.content mode (full, source_titles,
    // source_titles_and_urls, truncated, none).

    @Test