code: def generate_datapoint(schema):
      """Generates random data point following a schema.
  
      :param schema: an instance of Unischema specifying the columns of the dataset (with name, dtype, shape and codec)
  
      :returns: a randomly generated datapoint with the fields and format specified by schema
      """
  
      # Init dict representing datapoint
      d = {}
  
      for key, f in schema.fields.items():
          dtype = f.numpy_dtype
          shape = tuple(
              d if d is not None else LIST_SIZE for d in f.shape
          )
  
          # Extract range information from data type
          min_val, max_val = 0, 1
  
          if issubclass(dtype, np.integer):
              min_val, max_val = (
                  np.iinfo(dtype).min,
                  np.iinfo(dtype).max,
              )
  
          spread = max_val - min_val
  
          value = rand(*shape) * spread + min_val
          d[key] = np.array(value, dtype=dtype)
  
      return d
  
label_desc: Use image node_id 1 for calling a global function with example usage: generate_datapoint(schema) and returns: d