How to use the fastapi.File function in fastapi

def process_pdf(file: UploadFile = File(None)):
    """ Classifies every line in the document to the logical section of the document. The logical
    section can be title, author, email, section header, subsection header etc

    Parameters
    ----------
    file : File
        The Bytestream of a file to be uploaded

    Returns
    -------
    JSON
        ``{"labels": [(line, label)]}``

    """
    global sectlabel_model
    if sectlabel_model is None:

def extract_pdf(file: UploadFile = File(None)):
    """ Extracts the abstract from a scholarly article

    Parameters
    ----------
    file : uploadFile
        Byte Stream of a file uploaded.

    Returns
    -------
    JSON
        ``{"abstract": The abstract found in the scholarly document}``

    """

    global sectlabel_model
    if sectlabel_model is None:

async def predict_image(model_name: str, input_data: UploadFile = File(...)):
	"""
	Draws bounding box(es) on image and returns it.
	:param model_name: Model name
	:param input_data: Image file
	:return: Image file
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=True, predict_batch=False)
		error_logging.info('request successful;' + str(output))
		return FileResponse("/main/result.jpg", media_type="image/jpg")
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error')

def pdf_pipeline(file: UploadFile = File(None)):
    """ Parses the file and returns various analytics about the pdf

    Parameters
    ----------
    file: File
        A File stream

    Returns
    -------
    JSON
        Returns a JSON where the key can be a section in the document with value as the text of the document.
        It can also be other information such as parsed reference strings in the document, or normalised
        section headers of the document. This is a feature in development. Be careful in using this.
    """
    file_handle = file.file
    file_name = file.filename

async def create_file(*, file: bytes = File(...)):
    return {"file_size": len(file)}

    def upgrade(self, file: UploadFile = File(...)):
        upgrade_opsi(file, self.program.lifespan)

def predict(request: Request, img_file: bytes=File(...)):
    data = {"success": False}

    if request.method == "POST":
        image = Image.open(io.BytesIO(img_file))
        image = prepare_image(image,
                              (int(os.environ.get("IMAGE_WIDTH")),
                               int(os.environ.get("IMAGE_HEIGHT")))
                              )

        # Ensure our NumPy array is C-contiguous as well, otherwise we won't be able to serialize it
        image = image.copy(order="C")

        # Generate an ID for the classification then add the classification ID + image to the queue
        k = str(uuid.uuid4())
        image = base64.b64encode(image).decode("utf-8")
        d = {"id": k, "image": image}

    def save_calibration(self, *, file: UploadFile = File(...)):
        if file.content_type == "application/x-yaml":
            self.program.lifespan.persist.add_calibration_file(file)
        else:
            json = {
                "error": "Invalid calibration file",
                "message": "Calibration file must be a .yaml file",
            }

            return JSONResponse(status_code=400, content=json)