code: def predict(self, X=None, **kwargs):
          
          context = {
              'X': X
          }
          context.update(kwargs)
  
          last_block_name = list(self.blocks.keys())[-1]
          for block_name, block in self.blocks.items():
              LOGGER.debug("Producing block %s", block_name)
              try:
                  produce_args = self._get_block_args(block_name, block.produce_args, context)
                  outputs = block.produce(**produce_args)
  
                  if block_name != last_block_name:
                      output_dict = self._get_outputs(block_name, outputs, block.produce_output)
                      context.update(output_dict)
  
              except Exception:
                  LOGGER.exception("Exception caught producing MLBlock %s", block_name)
                  raise
  
          return outputs
docs: Produce predictions using the blocks of this pipeline.
  
          Sequentially call the `produce` method of each block, capturing the
          outputs before calling the next one.
  
          During the whole process a context dictionary is built, where both the
          passed arguments and the captured outputs of the `produce` methods
          are stored, and from which the arguments for the next `produce` calls
          will be taken.
  
          Args:
              X: Data which the pipeline will use to make predictions.
              **kwargs: Any additional keyword arguments will be directly added
                        to the context dictionary and available for the blocks.