class_docstr: The ModelGraph represents the network that is being processed by hls4ml.
  
  Args:
      config (dict):  The configuration dictionary
      layer_list (list(dict)):  The list contains a dictionary for each input layer
      inputs (list, optional):  The inputs to the model. If None, determined from layer_list
      outputs (list, optional):  The outputs to the model. If None, determined from layer_list
code: def predict(self, x):
      top_function, ctype = self._get_top_function(x)
      n_samples = self._compute_n_samples(x)
      n_inputs = len(self.get_input_variables())
      n_outputs = len(self.get_output_variables())
  
      curr_dir = os.getcwd()
      os.chdir(self.config.get_output_dir() + "/firmware")
  
      output = []
      if n_samples == 1 and n_inputs == 1:
          x = [x]
  
      try:
          for i in range(n_samples):
              predictions = [
                  np.zeros(yj.size(), dtype=ctype)
                  for yj in self.get_output_variables()
              ]
              if n_inputs == 1:
                  inp = [np.asarray(x[i])]
              else:
                  inp = [np.asarray(xj[i]) for xj in x]
              argtuple = inp
              argtuple += predictions
              argtuple = tuple(argtuple)
              top_function(*argtuple)
              output.append(predictions)
  
          # Convert to list of numpy arrays (one for each output)
          output = [
              np.asarray(
                  [
                      output[i_sample][i_output]
                      for i_sample in range(n_samples)
                  ]
              )
              for i_output in range(n_outputs)
          ]
      finally:
          os.chdir(curr_dir)
  
      if n_samples == 1 and n_outputs == 1:
          return output[0][0]
      elif n_outputs == 1:
          return output[0]
      elif n_samples == 1:
          return [output_i[0] for output_i in output]
      else:
          return output
  
imports: import ctypes
  import os
  import platform
  from collections import OrderedDict
  import numpy
  import numpy.ctypeslib
  from hls4ml.backends import get_backend
  from hls4ml.model.flow import get_flow
  from hls4ml.model.layers import layer_map
  from hls4ml.model.optimizer import get_available_passes, optimize_model
label_desc: Use image node_id 23 for calling the ModelGraph obj's underlying member method code with example usage: obj.predict(x) and returns: output, output, unknown, output