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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 create_state_multi_precision(self, index, weight):
 """Creates auxiliary state for a given weight, including FP32 high precision copy if original weight is FP16. This method is provided to perform automatic mixed precision training for optimizers that do not support it themselves. Parameters index : int An unique index to identify the weight. weight : NDArray The weight. Returns ------- state : any obj The state associated with the weight. """

        weight_master_copy = None
        if self.multi_precision and weight.dtype == numpy.float16:
            weight_master_copy = weight.astype(numpy.float32)
            return (weight_master_copy,) + (self.create_state(index, weight_master_copy),)
        if weight.dtype == numpy.float16 and not self.multi_precision:
            warnings.warn("Accumulating with float16 in optimizer can lead to "
                          "poor accuracy or slow convergence. "
                          "Consider using multi_precision=True option of the "
                          "optimizer")
        return self.create_state(index, weight)