# Buggy code
def randn(random_state, *dn, **kw):
    """
    Return a sample (or samples) from the "standard normal" distribution.

    If positive, int_like or int-convertible arguments are provided,
    `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
    with random floats sampled from a univariate "normal" (Gaussian)
    distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
    floats, they are first converted to integers by truncation). A single
    float randomly sampled from the distribution is returned if no
    argument is provided.

    This is a convenience function.  If you want an interface that takes a
    tuple as the first argument, use `numpy.random.standard_normal` instead.

    Parameters
    ----------
    d0, d1, ..., dn : int, optional
        The dimensions of the returned tensor, should be all positive.
        If no argument is given a single Python float is returned.

    Returns
    -------
    Z : Tensor or float
        A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
        the standard normal distribution, or a single such float if
        no parameters were supplied.

    See Also
    --------
    random.standard_normal : Similar, but takes a tuple as its argument.

    Notes
    -----
    For random samples from :math:`N(\mu, \sigma^2)`, use:

    ``sigma * mt.random.randn(...) + mu``

    Examples
    --------
    >>> import mars.tensor as mt

    >>> mt.random.randn().execute()
    2.1923875335537315 #random

    Two-by-four tensor of samples from N(3, 6.25):

    >>> (2.5 * mt.random.randn(2, 4) + 3).execute()
    array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
           [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random
    """
    if "dtype" not in kw:
        kw["dtype"] = np.dtype("f8")
    chunks = kw.pop("chunks", None)
    op = TensorRandn(state=random_state._state, size=dn, **kw)
    return op(chunks=chunks)


# Traceback (TypeError)
In [1]: import numpy as np

In [2]: np.random.rand((2, 3))
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-e49eb55bb286> in <module>()
----> 1 np.random.rand((2, 3))

mtrand.pyx in mtrand.RandomState.rand()

mtrand.pyx in mtrand.RandomState.random_sample()

mtrand.pyx in mtrand.cont0_array()

TypeError: 'tuple' object cannot be interpreted as an integer

# Fixed code
def randn(random_state, *dn, **kw):
    """
    Return a sample (or samples) from the "standard normal" distribution.

    If positive, int_like or int-convertible arguments are provided,
    `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
    with random floats sampled from a univariate "normal" (Gaussian)
    distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
    floats, they are first converted to integers by truncation). A single
    float randomly sampled from the distribution is returned if no
    argument is provided.

    This is a convenience function.  If you want an interface that takes a
    tuple as the first argument, use `numpy.random.standard_normal` instead.

    Parameters
    ----------
    d0, d1, ..., dn : int, optional
        The dimensions of the returned tensor, should be all positive.
        If no argument is given a single Python float is returned.

    Returns
    -------
    Z : Tensor or float
        A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
        the standard normal distribution, or a single such float if
        no parameters were supplied.

    See Also
    --------
    random.standard_normal : Similar, but takes a tuple as its argument.

    Notes
    -----
    For random samples from :math:`N(\mu, \sigma^2)`, use:

    ``sigma * mt.random.randn(...) + mu``

    Examples
    --------
    >>> import mars.tensor as mt

    >>> mt.random.randn().execute()
    2.1923875335537315 #random

    Two-by-four tensor of samples from N(3, 6.25):

    >>> (2.5 * mt.random.randn(2, 4) + 3).execute()
    array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
           [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random
    """
    if len(dn) == 1 and isinstance(dn[0], (tuple, list)):
        raise TypeError("'tuple' object cannot be interpreted as an integer")
    if "dtype" not in kw:
        kw["dtype"] = np.dtype("f8")
    chunks = kw.pop("chunks", None)
    op = TensorRandn(state=random_state._state, size=dn, **kw)
    return op(chunks=chunks)