0d87aa868a723719e6e95ab8c08d7a473b60f378,python/mxnet/ndarray/numpy/random.py,,uniform,#Any#Any#Any#Any#Any#Any#,93

Before Change

        Drawn samples from the parameterized uniform distribution.
    
    from ...numpy import ndarray as np_ndarray
    input_type = (isinstance(low, np_ndarray), isinstance(high, np_ndarray))
    if dtype is None:
        dtype = "float32"
    if ctx is None:
        ctx = current_context()
    if size == ():
        size = None
    if input_type == (True, True):
        return _npi.uniform(low, high, low=None, high=None, size=size,
                            ctx=ctx, dtype=dtype, out=out)
    elif input_type == (False, True):
        return _npi.uniform(high, low=low, high=None, size=size,
                            ctx=ctx, dtype=dtype, out=out)
    elif input_type == (True, False):
        return _npi.uniform(low, low=None, high=high, size=size,
                            ctx=ctx, dtype=dtype, out=out)
    else:
        return _npi.uniform(low=low, high=high, size=size,
                            ctx=ctx, dtype=dtype, out=out)


def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
    rDraw random samples from a normal (Gaussian) distribution.

    Samples are distributed according to a normal distribution parametrized

After Change

    if dtype is None:
        dtype = "float32"
    if ctx is None:
        ctx = str(current_context())
    else:
        ctx = str(ctx)
    if dtype is not None and not isinstance(dtype, str):
        dtype = np.dtype(dtype).name
    if size == ():
        size = None
    return _api_internal.uniform(low, high, size, ctx, dtype, out)


def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
    rDraw random samples from a normal (Gaussian) distribution.

Instances