code: def get_min_max_input_shape(asr_model):
      max_shape = (1, 64, 1)
      min_shape = (64, 64, 99999)
      for test_batch in asr_model.test_dataloader():
          test_batch = [x.cuda() for x in test_batch]
          (
              processed_signal,
              processed_signal_length,
          ) = asr_model.preprocessor(
              input_signal=test_batch[0], length=test_batch[1]
          )
          shape = processed_signal.cpu().numpy().shape
          if shape[0] > max_shape[0]:
              max_shape = (shape[0], *max_shape[1:])
          if shape[0] < min_shape[0]:
              min_shape = (shape[0], *min_shape[1:])
          if shape[2] > max_shape[2]:
              max_shape = (*max_shape[0:2], shape[2])
          if shape[2] < min_shape[2]:
              min_shape = (*min_shape[0:2], shape[2])
      return min_shape, max_shape
  
file_path: NeMo/examples/asr/quantization/speech_to_text_quant_infer_trt.py
imports: import os
  from argparse import ArgumentParser
  import numpy
  import pycuda.driver
  import tensorrt
  import torch
  from omegaconf import open_dict
  from nemo.collections.asr.metrics.wer import WER, word_error_rate
  from nemo.collections.asr.models import EncDecCTCModel
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: get_min_max_input_shape(asr_model) and returns: min_shape,  max_shape