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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict(self, X=None, **kwargs):
 """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. """

        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