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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 n_feature_hash(feature, dims, seeds):
 """N-hot-encoded feature hashing. Args: feature (str):
 Target feature represented as string. dims (list of int):
 Number of dimensions for each hash value. seeds (list of float):
 Seed of each hash function (mmh3). Returns: numpy 1d array: n-hot-encoded feature vector for `s`. """

    vec = np.zeros(sum(dims))
    offset = 0

    for seed, dim in zip(seeds, dims):
        vec[offset:(offset + dim)] = feature_hash(feature, dim, seed)
        offset += dim

    return vec