def det(x, name=None):
    '\n    Calculates determinant value of a square matrix or batches of square matrices.\n    Args:\n        x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n                    `(*, n, n)` where `*` is one or more batch dimensions.\n    Returns:\n        y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n    Examples:\n        .. code-block:: python\n\n        import paddle\n\n        x =  paddle.randn([3,3,3])\n\n        A = paddle.linalg.det(x)\n\n        print(A)\n\n        # [ 0.02547996,  2.52317095, -6.15900707])\n\n\n    '
    if paddle.in_dynamic_mode():
        return _C_ops.determinant(x)
    check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')
    input_shape = list(x.shape)
    assert (len(input_shape) >= 2), ("The x must be at least 2-dimensional, but received Input x's dimensional: %s.\n" % len(input_shape))
    assert (input_shape[(- 1)] == input_shape[(- 2)]), ('Expect squared input,but received %s by %s matrix.\n' % (input_shape[(- 2)], input_shape[(- 1)]))
    helper = LayerHelper('determinant', **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})
    return out
Calculates determinant value of a square matrix or batches of square matrices.
Args:
    x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size
                `(*, n, n)` where `*` is one or more batch dimensions.
Returns:
    y (Tensor):the determinant value of a square matrix or batches of square matrices.

Examples:
    .. code-block:: python

    import paddle

    x =  paddle.randn([3,3,3])

    A = paddle.linalg.det(x)

    print(A)

    # [ 0.02547996,  2.52317095, -6.15900707])
def det(x, name=None):
    '\n    Calculates determinant value of a square matrix or batches of square matrices.\n    Args:\n        x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n                    `(*, n, n)` where `*` is one or more batch dimensions.\n    Returns:\n        y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n    Examples:\n        .. code-block:: python\n\n        import paddle\n\n        x =  paddle.randn([3,3,3])\n\n        A = paddle.linalg.det(x)\n\n        print(A)\n\n        # [ 0.02547996,  2.52317095, -6.15900707])\n\n\n    '
    if paddle.in_dynamic_mode():
        return _C_ops.determinant(x)
    check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')
    input_shape = list(x.shape)
    assert (len(input_shape) >= 2), ("The x must be at least 2-dimensional, but received Input x's dimensional: %s.\n" % len(input_shape))
    assert (input_shape[(- 1)] == input_shape[(- 2)]), ('Expect squared input,but received %s by %s matrix.\n' % (input_shape[(- 2)], input_shape[(- 1)]))
    helper = LayerHelper('determinant', **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})
    return out