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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 keras_tuples(stream, inputs=None, outputs=None):
 """Reformat data objects as keras-compatible tuples. For more detail: https://keras.io/models/model/#fit Parameters stream : iterable Stream of data objects. inputs : string or iterable of strings, None Keys to use for ordered input data. If not specified, returns `None` in its place. outputs : string or iterable of strings, default=None Keys to use for ordered output data. If not specified, returns `None` in its place. Yields ------ x : np.ndarray, list of np.ndarray, or None If `inputs` is a string, `x` is a single np.ndarray. If `inputs` is an iterable of strings, `x` is a list of np.ndarrays. If `inputs` is a null type, `x` is None. y : np.ndarray, list of np.ndarray, or None If `outputs` is a string, `y` is a single np.ndarray. If `outputs` is an iterable of strings, `y` is a list of np.ndarrays. If `outputs` is a null type, `y` is None. Raises ------ DataError If the stream contains items that are not data-like. """

    flatten_inputs, flatten_outputs = False, False
    if inputs and isinstance(inputs, six.string_types):
        inputs = [inputs]
        flatten_inputs = True
    if outputs and isinstance(outputs, six.string_types):
        outputs = [outputs]
        flatten_outputs = True

    inputs, outputs = (inputs or []), (outputs or [])
    if not inputs + outputs:
        raise PescadorError('At least one key must be given for '
                            '`inputs` or `outputs`')

    for data in stream:
        try:
            x = list(data[key] for key in inputs) or None
            if len(inputs) == 1 and flatten_inputs:
                x = x[0]

            y = list(data[key] for key in outputs) or None
            if len(outputs) == 1 and flatten_outputs:
                y = y[0]

            yield (x, y)
        except TypeError:
            raise DataError("Malformed data stream: {}".format(data))