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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 make_regression( n_samples=100, n_features=100, n_informative=10, n_targets=1, bias=0.0, effective_rank=None, tail_strength=0.5, noise=0.0, shuffle=True, coef=False, random_state=None, chunks=None, ):
 """ Generate a random regression problem. The input set can either be well conditioned (by default) or have a low rank-fat tail singular profile. See :func:`sklearn.datasets.make_low_rank_matrix` for more details. This can be used to generate very large Dask arrays on a cluster of machines. When using Dask in distributed mode, the client machine only needs to allocate a single block's worth of data. Parameters n_samples : int, optional (default=100) The number of samples. n_features : int, optional (default=100) The number of features. n_informative : int, optional (default=10) The number of informative features, i.e., the number of features used to build the linear model used to generate the output. n_targets : int, optional (default=1) The number of regression targets, i.e., the dimension of the y output vector associated with a sample. By default, the output is a scalar. bias : float, optional (default=0.0) The bias term in the underlying linear model. effective_rank : int or None, optional (default=None) if not None: The approximate number of singular vectors required to explain most of the input data by linear combinations. Using this kind of singular spectrum in the input allows the generator to reproduce the correlations often observed in practice. if None: The input set is well conditioned, centered and gaussian with unit variance. tail_strength : float between 0.0 and 1.0, optional (default=0.5) The relative importance of the fat noisy tail of the singular values profile if `effective_rank` is not None. noise : float, optional (default=0.0) The standard deviation of the gaussian noise applied to the output. shuffle : boolean, optional (default=True) Shuffle the samples and the features. coef : boolean, optional (default=False) If True, the coefficients of the underlying linear model are returned. random_state : int, RandomState instance or None (default) Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. chunks : int, tuple How to chunk the array. Must be one of the following forms: - A blocksize like 1000. - A blockshape like (1000, 1000). - Explicit sizes of all blocks along all dimensions like ((1000, 1000, 500), (400, 400)). Returns ------- X : Dask array of shape [n_samples, n_features] The input samples. y : Dask array of shape [n_samples] or [n_samples, n_targets] The output values. coef : array of shape [n_features] or [n_features, n_targets], optional The coefficient of the underlying linear model. It is returned only if coef is True. """

    chunks = da.core.normalize_chunks(chunks, (n_samples, n_features))
    _check_axis_partitioning(chunks, n_features)

    rng = sklearn.utils.check_random_state(random_state)
    return_coef = coef is True

    if chunks[1][0] != n_features:
        raise ValueError(
            "Can only generate arrays partitioned along the "
            "first axis. Specifying a larger chunksize for "
            "the second axis."
        )
    _, _, coef = sklearn.datasets.make_regression(
        n_samples=chunks[0][0],
        n_features=n_features,
        n_informative=n_informative,
        n_targets=n_targets,
        bias=bias,
        effective_rank=effective_rank,
        tail_strength=tail_strength,
        noise=noise,
        shuffle=shuffle,
        coef=True,  # hardcode here
        random_state=rng,
    )
    seed = da.random.random_state_data(1, random_state=rng)
    da_rng = da.random.RandomState(seed[0])

    X_big = da_rng.normal(size=(n_samples, n_features), chunks=(chunks[0], n_features))
    y_big = da.dot(X_big, coef) + bias

    if noise > 0:
        y_big = y_big + da_rng.normal(
            scale=noise, size=y_big.shape, chunks=y_big.chunks
        )

    y_big = y_big.squeeze()

    if return_coef:
        return X_big, y_big, coef
    else:
        return X_big, y_big