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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 run(self, x):
 """ Function for batch usage of already trained and tested MLP. **Args:** * `x` : input array (2-dimensional array). Every row represents one input vector (features). **Returns:** * `y`: output vector (n-dimensional array). Every row represents output (outputs) for an input vector. """

        # measure the data and check if the dimmension agree
        try:    
            x = np.array(x)
        except:
            raise ValueError('Impossible to convert x to a numpy array')
        N = len(x)   
        # create empty arrays     
        if self.outputs == 1:
            y = np.zeros(N)
        else:
            y = np.zeros((N, self.outputs))
        # predict data in loop        
        for k in range(N):
            y[k] = self.predict(x[k])
        return y