Keep getting error '400' status code
I keep running into this error:ModelError                                Traceback (most recent call last)Cell In[13], line 4643 serialized_payload = json.dumps(payload)  # Serialize the payload to JSON format45 # Make API call—> 46 response = client.invoke_endpoint(47     EndpointName=endpoint_name,48     Body=serialized_payload,49     ContentType=‘application/json’  # Specify the format of the payload50 )52 response_payload = json.loads(response[‘Body’].read().decode(“utf-8”))54 end_time = time.time()File /opt/conda/lib/python3.10/site-packages/botocore/client.py:535, in ClientCreator._create_api_method.._api_call(self, *args, **kwargs)531     raise TypeError(532         f"{py_operation_name}() only accepts keyword arguments."533     )534 # The “self” in this scope is referring to the BaseClient.→ 535 return self._make_api_call(operation_name, kwargs)File /opt/conda/lib/python3.10/site-packages/botocore/client.py:980, in BaseClient._make_api_call(self, operation_name, api_params)978     error_code = parsed_response.get(“Error”, {}).get(“Code”)979     error_class = self.exceptions.from_code(error_code)→ 980     raise error_class(parsed_response, operation_name)981 else:982     return parsed_responseModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400) from primary with message "{“code”: 400,“type”: “InternalServerException”,“message”: “\u0027NoneType\u0027 object has no attribute \u0027startswith\u0027”}This is my model’s inference script:%%writefile code/inference.pyimport base64import torchfrom io import BytesIOfrom diffusers import StableDiffusionPipelinedef model_fn(model_dir):# Load stable diffusion and move it to the GPUpipe = StableDiffusionPipeline.from_pretrained(model_dir, torch_dtype=torch.float16)pipe = pipe.to(“cuda”)return pipedef predict_fn(data, pipe):# get prompt & parameters
prompt = data.pop("inputs", data)
# set valid HP for stable diffusion
num_inference_steps = data.pop("num_inference_steps", 50)
guidance_scale = data.pop("guidance_scale", 7.5)
num_images_per_prompt = data.pop("num_images_per_prompt", 4)

# run generation with parameters
generated_images = pipe(
    prompt,
    num_inference_steps=num_inference_steps,
    guidance_scale=guidance_scale,
    num_images_per_prompt=num_images_per_prompt,
)["images"]

# create response
encoded_images = []
for image in generated_images:
    buffered = BytesIO()
    image.save(buffered, format="JPEG")
    encoded_images.append(base64.b64encode(buffered.getvalue()).decode())

# create response
return {"generated_images": encoded_images}Does anyone know how to fix this?