zirain <******@****.***> 1657592759 +0800

zirain <******@****.***> 1657592759 +0800

    sub: Optional[SubItem] = None
    if PYDANTIC_V2:
        model_config = {"json_schema_serialization_defaults_required": True}


def get_app_client(separate_input_output_schemas: bool = True) -> TestClient:
    app = FastAPI(separate_input_output_schemas=separate_input_output_schemas)

    @app.post("/items/")
    def create_item(item: Item):
        return item

    @app.post("/items-list/")
    def create_item_list(item: List[Item]):

                                    vector<AbstractTensorHandle*> f_outputs)
      : forward_inputs_(f_inputs), forward_outputs_(f_outputs) {
    for (auto input : forward_inputs_) {
      if (input) {
        input->Ref();
      }
    }
    for (auto output : forward_outputs_) {
      if (output) {
        output->Ref();
      }
    }
  }

  Status Compute(AbstractContext* ctx,

    for (int i = 0; i < noutputs; ++i) {
      output_names_vec.push_back(string(output_names[i]));
    }
  }

  // Process control output names.
  std::vector<string> control_output_names_vec;
  if (control_output_names) {
    control_output_names_vec.reserve(ncontrol_outputs);
    for (int i = 0; i < ncontrol_outputs; ++i) {
      control_output_names_vec.push_back(string(control_output_names[i]));
    }
  }

    }
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {

  sources.reserve(output_tensors.size());
  for (const TapeTensor& output_tensor : output_tensors) {
    // Ownership of `aid` transferred to CallBackwardFunction below.
    Gradient* aid;
    if (output_tensor.GetDType() == tensorflow::DT_VARIANT) {
      // Note: Needs to be zeros rather than ones since there's currently no
      // ones_like for variants.
      aid = output_tensor.ZerosLike();
    } else {

	// Output: true
}

func ExampleFields() {
	fmt.Printf("Fields are: %q", bytes.Fields([]byte("  foo bar  baz   ")))
	// Output: Fields are: ["foo" "bar" "baz"]
}

func ExampleFieldsFunc() {
	f := func(c rune) bool {
		return !unicode.IsLetter(c) && !unicode.IsNumber(c)
	}
	fmt.Printf("Fields are: %q", bytes.FieldsFunc([]byte("  foo1;bar2,baz3..."), f))
	// Output: Fields are: ["foo1" "bar2" "baz3"]

	}

	fErr := cmd.Execute()
	output := out.String()

	if c.expectedException {
		if fErr == nil {
			t.Fatalf("Wanted an exception, "+
				"didn't get one, output was %q", output)
		}
	} else {
		if fErr != nil {
			t.Fatalf("Unwanted exception: %v", fErr)
		}
	}

	if c.expectedOutput != "" && c.expectedOutput != output {
		assert.Equal(t, c.expectedOutput, output)

    // Extract the resulting tensor.
    add_output2 = TF_OutputListGet(add_outputs, 0);
    TF_DeleteOutputList(add_outputs);
  }

  // 3rd Output will be Matrix Multiplication of add_output1 and add_output2
  TF_AbstractTensor* mm_output;
  {
    // Build an abstract operation, inputs and output.
    auto* mm_op = TF_NewAbstractOp(graph_ctx);
    TF_AbstractOpSetOpType(mm_op, "MatMul", s);