TFLite dynamic input shape

Question

I am using TensorFlow 2.1 and trying to use variable-length input sequences for a recurrent neural network after conversion to TFLite. I first converted my Keras model to TFLite model using TFLite Converter. I built the model with input shapes [(None, 40), (1, 6, 2, 32)]. But after conversion into TFLite, the model accepts only input shape [(1, 40), (1, 6, 2, 32)]. I want my TFLite model to accept variable values for None. I tried to resize my tensor input using resize_tensor_input, but still the shape of the input is not changing.

I have pasted my code snippet and its output below.

interpreter = tf.lite.Interpreter(model_path=my_model_path)

input_details = interpreter.get_input_details()

interpreter.resize_tensor_input(0, [3, 40], strict=False)
interpreter.allocate_tensors()

output_details = interpreter.get_output_details()

interpreter.set_tensor(input_details[0]["index"], np.random.uniform(size=[3, 40]).astype(np.float32))
interpreter.set_tensor(input_details[1]["index"], np.zeros(shape=[1, 6, 2, 32]).astype(np.float32))
tf.print("Input details : ", input_details)

interpreter.invoke()

result = interpreter.get_tensor(output_details[0]["index"])
final_state = interpreter.get_tensor(output_details[1]["index"])

I printed my input details inside the code pasted above and the output is pasted below. Here I am getting my first input shape as [1, 40] instead of [3, 40].

Input details :  [{'dtype': <class 'numpy.float32'>,
  'index': 0,
  'name': 'input_1',
  'quantization': (0.0, 0),
  'shape': array([ 1, 40], dtype=int32)},
 {'dtype': <class 'numpy.float32'>,
  'index': 1,
  'name': 'input_2',
  'quantization': (0.0, 0),
  'shape': array([ 1,  6,  2, 32], dtype=int32)}]
Output shape : (1, 1, 32)
Final state shape : (1, 6, 2, 32)

What am I doing wrong in the above code? Or if my approach to achieve the desired result is wrong, please help me to find the right method to achieve the same, or is there any other workaround for the same?