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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 sumexp_stable(data):
 """Compute the sum of exponents for a list of samples Parameters data : array, shape=[features, samples] A data array containing samples. Returns ------- result_sum : array, shape=[samples,] The sum of exponents for each sample divided by the exponent of the maximum feature value in the sample. max_value : array, shape=[samples,] The maximum feature value for each sample. result_exp : array, shape=[features, samples] The exponent of each element in each sample divided by the exponent of the maximum feature value in the sample. Note ---- This function is more stable than computing the sum(exp(v)). It useful for computing the softmax_i(v)=exp(v_i)/sum(exp(v)) function. """

    max_value = data.max(axis=0)
    result_exp = np.exp(data - max_value)
    result_sum = np.sum(result_exp, axis=0)
    return result_sum, max_value, result_exp