

def make_node(self, x, values, ilist):
    '\n\n        Parameters\n        ----------\n        x\n            A dense matrix that specify the output shape.\n        values\n            A dense matrix with the values to use for output.\n        ilist\n            A dense vector with the same length as the number of rows of values.\n            It specify where in the output to put the corresponding rows.\n\n        This create a sparse matrix with the same shape as `x`. Its\n        values are the rows of `values` moved. Pseudo-code::\n\n            output = csc_matrix.zeros_like(x, dtype=values.dtype)\n            for in_idx, out_idx in enumerate(ilist):\n                output[out_idx] = values[in_idx]\n\n        '
    x_ = theano.tensor.as_tensor_variable(x)
    values_ = theano.tensor.as_tensor_variable(values)
    ilist_ = theano.tensor.as_tensor_variable(ilist)
    if (ilist_.type.dtype[:3] not in ('int', 'uin')):
        raise TypeError('index must be integers')
    if (ilist_.type.ndim != 1):
        raise TypeError('index must be vector')
    if (x_.type.ndim != 2):
        raise TypeError(('cannot create a sparse matrix with %d dimensions' % x_.type.ndim))
    if (values_.type.ndim != 2):
        raise TypeError(('cannot create a sparse matrix from values with %d ndim' % values_.type.ndim))
    return gof.Apply(self, [values.shape, values_, ilist_], [csc_matrix(dtype=x.dtype)])

dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/sparse/basic.py ConstructSparseFromList.make_node/VarMisuse@164/494 `x_`->`values`