Implement reduce_to_k_dim codePython Construct a method that performs dimensionality reduction on the matrix to produce k-dimensional embeddings Use SVD to take the top k components and produce a new

from sklearn.decomposition import TruncatedSVD

def reduce_to_k_dim(matrix, k=2):
    """
    Perform dimensionality reduction on the matrix to produce k-dimensional embeddings
    using TruncatedSVD

    :param matrix: numpy array of shape (n_samples, n_features)
    :param k: int, number of dimensions to reduce to (default=2)
    :return: numpy array of shape (n_samples, k), k-dimensional embeddings of the original matrix
    """
    svd = TruncatedSVD(n_components=k)
    embedding = svd.fit_transform(matrix)
    return embedding

Note: The input matrix should be of shape (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features. The output will be a numpy array of shape (n_samples, k), where k is the number of dimensions to reduce to