I am trying to do inference on deployed model, An error occurred (ModelError) when calling the InvokeEndpoint operation.

## Code
#importing required libreries
from sagemaker import get_execution_role
from sagemaker import Session
role = get_execution_role()
sess = Session()
region = sess.boto_region_name
bucket = sess.default_bucket()

from sagemaker import get_execution_role
from sagemaker.utils import name_from_base
from sagemaker.tensorflow import TensorFlowModel
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from IPython.display import display

tf_framework_version = tf.__version__
tf_framework_version

#packages from TFOD models/research
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

#List of the strings that is used to add correct label for each box.
category_index = label_map_util.create_category_index_from_labelmap('label_map.pbtxt', use_display_name=True)

#model.tar.gz file path from s3
model_artefact = 's3://{my-bucket-name}/model/model.tar.gz'

#deploying
from sagemaker.tensorflow import TensorFlowModel
model_tfm = TensorFlowModel(model_data=model_artefact, framework_version=tf_framework_version, role=role)
predictor_tfm = model_tfm.deploy(initial_instance_count=1, instance_type='ml.m5.xlarge')

#function to convert image into numpy array
def load_image_into_numpy_array(image):
image_rez = image.resize((256, 256))
(im_width, im_height) = image_rez.size
return np.array(image_rez.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)

test_image = 'test-images/beet_test.jpg'
image = Image.open(test_image)
image_arr = load_image_into_numpy_array(image)

input = {
'instances': [image_arr.tolist()]
}

#inference on test image
#this below line results in error
result = predictor_tfm.predict(input)['predictions'][0]

## problem description
I am trying to deploy an already trained TFOD v2 mask rcnn model on sagemaker. Deployement is succesfully done and created endpoint. But when I do inference on test image this error occured,
line: result = predictor_tfm.predict(input)['predictions'][0]
output: ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400) from primary with message "{ "error": "Tensor name: raw_detection_scores has inconsistent batch size: 1 expecting: 300" }"

Please help me.

## Logs
---------------------------------------------------------------------------
ModelError Traceback (most recent call last)
in
----> 1 result = predictor_tfm.predict(input)['predictions'][0]

~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/sagemaker/tensorflow/model.py in predict(self, data, initial_args)
105 args["CustomAttributes"] = self._model_attributes
106
--> 107 return super(TensorFlowPredictor, self).predict(data, args)
108
109

~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/sagemaker/predictor.py in predict(self, data, initial_args, target_model, target_variant, inference_id)
159 data, initial_args, target_model, target_variant, inference_id
160 )
--> 161 response = self.sagemaker_session.sagemaker_runtime_client.invoke_endpoint(**request_args)
162 return self._handle_response(response)
163

~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/botocore/client.py in _api_call(self, *args, **kwargs)
413 "%s() only accepts keyword arguments." % py_operation_name)
414 # The "self" in this scope is referring to the BaseClient.
--> 415 return self._make_api_call(operation_name, kwargs)
416
417 _api_call.__name__ = str(py_operation_name)

~/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/botocore/client.py in _make_api_call(self, operation_name, api_params)
743 error_code = parsed_response.get("Error", {}).get("Code")
744 error_class = self.exceptions.from_code(error_code)
--> 745 raise error_class(parsed_response, operation_name)
746 else:
747 return parsed_response

ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400) from primary with message "{ "error": "Tensor name: refined_box_encodings has inconsistent batch size: 300 expecting: 1" }". See {cloudwatch-link}#logEventViewer:group=/aws/sagemaker/Endpoints/tensorflow-inference-2022-06-16-10-58-30-168 in account {acc-number} for more information.

Start at the notebook code around predictor_tfm.predict(input) and review the SageMaker endpoint's CloudWatch logs at the linked log location. Compare the request structure with the deployed TensorFlow object-detection model's expected input and output batch dimensions; the issue is done when inference completes without the reported tensor batch-size errors.

Written by the indexing model from the issue text.