floor_div:
  description: |
    Divides the first input tensor by the second input tensor element-wise and round down to the closest integer.

    Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types consistent.
    When the inputs are two tensors,
    dtypes of them cannot be bool at the same time, and the shapes of them could be broadcast.
    When the inputs are one tensor and one scalar,
    the scalar could only be a constant.

    .. math::
        out_{i} = \text{floor}( \frac{input_i}{other_i})

    where the :math:`floor` indicates the Floor operator, for more details,
    please refer to the :class:`mindspore.ops.Floor` operator.

    .. warning::
        This is an experimental API that is subject to change or deletion.

    Args:
        input (Union[Tensor, Number, bool]): The first input is a number or
            a bool or a tensor whose data type is number or bool.
        other (Union[Tensor, Number, bool]): The second input is a number or
            a bool or a tensor whose data type is number or bool.
    
    Returns:
        Tensor, the shape is the same as the one after broadcasting,
        and the data type is the one with higher precision or higher digits among the two inputs.

    Raises:
        TypeError: If `input` and `other` are not the following: Tensor, number.Number or bool.

    Supported Platforms:
        ``Ascend`` ``GPU`` ``CPU``

    Examples:
        >>> import mindspore
        >>> from mindspore import Tensor, ops
        >>> import numpy as np
        >>> input = Tensor(np.array([2, 4, -1]), mindspore.int32)
        >>> other = Tensor(np.array([3, 3, 3]), mindspore.int32)
        >>> output = ops.floor_divide(input, other)
        >>> print(output)
        [ 0  1 -1]
        >>> input = Tensor(2.0, mindspore.float32)
        >>> other = Tensor(2.0, mindspore.float32)
        >>> output = ops.floor_divide(input, other)
        >>> print(output)
        1.0