println("internal fun $CONVERT_ARGUMENT(argument: Any?, analysisSession: KtFirAnalysisSession): Any? {")
        withIndent {
            println("return $CONVERT_ARGUMENT(argument, analysisSession.firSymbolBuilder)")
        }
        println("}")
        println()

        println("private fun $CONVERT_ARGUMENT(argument: Any?, firSymbolBuilder: KtSymbolByFirBuilder): Any? {")

For example, you cannot put a Pydantic model in a `JSONResponse` without first converting it to a `dict` with all the data types (like `datetime`, `UUID`, etc) converted to JSON-compatible types.

For those cases, you can use the `jsonable_encoder` to convert your data before passing it to a response:

```Python hl_lines="6-7  21-22"
{!../../../docs_src/response_directly/tutorial001.py!}
```

!!! note "Technical Details"

# JSON Compatible Encoder

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder`

Let's imagine that you have a database `fake_db` that only receives JSON compatible data.

                        BooleanConstantReference.FALSE -> KtContractConstantType.FALSE
                        else -> error("Can't convert $value to the Analysis API")
                    },
                    analysisContext.token
                )
            )
            else -> error("Can't convert $returnsEffect to the Analysis API")
        }

                        else -> errorWithAttachment("Can't convert ${value::class} to the Analysis API") {
                            withEntry("value", value) { value.toString() }
                        }
                    },
                    builder.token
                )
            )
            else -> errorWithAttachment("Can't convert ${returnsEffect::class} to the Analysis API")  {

2)  Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).

```
(You can paste links or attach files by dragging & dropping them below)

    throttlePeriodUnit = unit
  }

  fun getThrottlePeriod(unit: TimeUnit): Long = unit.convert(throttlePeriodAmount, throttlePeriodUnit)

  fun setBodyDelay(
    delay: Long,
    unit: TimeUnit,
  ) = apply {
    bodyDelayAmount = delay
    bodyDelayUnit = unit
  }

  fun getBodyDelay(unit: TimeUnit): Long = unit.convert(bodyDelayAmount, bodyDelayUnit)

  fun setHeadersDelay(
    delay: Long,

But if the data didn't exist, you want to create it, and return an HTTP status code of "CREATED" `201`.

But you still want to be able to filter and convert the data you return with a `response_model`.

For those cases, you can use a `Response` parameter.

## Use a `Response` parameter

    assertThat(formEncode(8)).isEqualTo("%08")
    assertThat(formEncode(9)).isEqualTo("%09")
    // Browsers convert '\n' to '\r\n'
    assertThat(formEncode(10)).isEqualTo("%0A")
    assertThat(formEncode(11)).isEqualTo("%0B")
    assertThat(formEncode(12)).isEqualTo("%0C")
    // Browsers convert '\r' to '\r\n'
    assertThat(formEncode(13)).isEqualTo("%0D")
    assertThat(formEncode(14)).isEqualTo("%0E")

Most of the standard headers are separated by a "hyphen" character, also known as the "minus symbol" (`-`).

But a variable like `user-agent` is invalid in Python.

So, by default, `Header` will convert the parameter names characters from underscore (`_`) to hyphen (`-`) to extract and document the headers.

Also, HTTP headers are case-insensitive, so, you can declare them with standard Python style (also known as "snake_case").