Both input and output layers of my quantized tflite model are uint8

TFLite input details [{‘name’: ‘rescaling_6_input’, ‘index’: 21, ‘shape’: array([ 1, 64, 64, 1]), ‘shape_signature’: array([-1, 64, 64, 1]), ‘dtype’: <class ‘numpy.uint8’>, ‘quantization’: (1.0, 0), ‘quantization_parameters’: {‘scales’: array([1.], dtype=float32), ‘zero_points’: array([0]), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]

TFLite output details [{‘name’: ‘Identity’, ‘index’: 22, ‘shape’: array([1, 2]), ‘shape_signature’: array([-1, 2]), ‘dtype’: <class ‘numpy.uint8’>, ‘quantization’: (0.025388231500983238, 124), ‘quantization_parameters’: {‘scales’: array([0.03], dtype=float32), ‘zero_points’: array([124]), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]

The tf.load error message seems to be that it is not accepting input type uint8.

Error message “input->type == kTfLiteFloat32 || input->type == kTfLiteInt16 || input->type == kTfLiteInt8 was not true”