docstr: Generates output predictions for the input samples.
  
  # Arguments
      x: Input samples. A Numpy array, or a list of arrays in case the model has
          multiple inputs.
      verbose: Verbosity mode, 0 or 1.
  
  # Returns
      Numpy array(s) of output predictions.
docstr_tok: ["Generates","output","predictions","for","the","input","samples",".","#","Arguments","x",":","Input","samples",".","A","Numpy","array",",","or","a","list","of","arrays","in","case","the","model","has","multiple","inputs",".","verbose",":","Verbosity","mode",",","0","or","1",".","#","Returns","Numpy","array","(","s",")","of","output","predictions","."]
code: def predict(
      self, x: Union[Data, Iterator[Data]], verbose: int = 0
  ) -> Data:
      """Generates output predictions for the input samples.
  
      # Arguments
          x: Input samples. A Numpy array, or a list of arrays in case the model has
              multiple inputs.
          verbose: Verbosity mode, 0 or 1.
  
      # Returns
          Numpy array(s) of output predictions.
      """
  
      data_iterator = data_generator(x)
      if verbose >= 1:
          data_iterator = tqdm(data_iterator)
  
      prediction_iter = (
          self.interpreter.predict(inputs) for inputs in data_iterator
      )
      outputs = [
          np.concatenate(batches) for batches in zip(*prediction_iter)
      ]
  
      if len(self.output_shapes) == 1:
          return outputs[0]
      return outputs
  
file_path: compute-engine/larq_compute_engine/tflite/python/interpreter_base.py
imports: from collections.abc import Iterator
  from typing import Union, Optional
  import numpy
  from tqdm import tqdm
label_desc: Use image node_id 10 for calling the InterpreterBase obj's underlying member method code with example usage: obj.predict(x, verbose) and returns: outputs, outputs