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    This operator implements the compat.softmax. The calculation process is as follows:

    1. The dimension :attr:`dim` of ``input`` will be permuted to the last.

    2. Then ``input`` will be logically flattened to a 2-D matrix. The matrix's second
    dimension(row length) is the same as the dimension :attr:`axis` of ``input``,
    and the first dimension(column length) is the product of all other dimensions
    of ``input``. For each row of the matrix, the softmax operator squashes the
    K-dimensional(K is the width of the matrix, which is also the size of ``input``'s
    dimension :attr:`dim`) vector of arbitrary real values to a K-dimensional
    vector of real values in the range [0, 1] that add up to 1.

    3. After the softmax operation is completed, the inverse operations of steps 1 and 2
    are performed to restore the two-dimensional matrix to the same dimension as the ``input`` .

    It computes the exponential of the given dimension and the sum of exponential
    values of all the other dimensions in the K-dimensional vector input.
    Then the ratio of the exponential of the given dimension and the sum of
    exponential values of all the other dimensions is the output of the softmax
    operator.

    For each row :math:`i` and each column :math:`j` in the matrix, we have:

    .. math::

        softmax[i, j] = \frac{\exp(input[i, j])}{\sum_j(exp(input[i, j])}

    Example:

    .. code-block:: text

        Case 1:
          Input:
            input.shape = [2, 3, 4]
            input.data = [[[2.0, 3.0, 4.0, 5.0],
                       [3.0, 4.0, 5.0, 6.0],
                       [7.0, 8.0, 8.0, 9.0]],
                      [[1.0, 2.0, 3.0, 4.0],
                       [5.0, 6.0, 7.0, 8.0],
                       [6.0, 7.0, 8.0, 9.0]]]

          Attrs:
            dim = -1

          Output:
            out.shape = [2, 3, 4]
            out.data = [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                         [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                         [0.07232949, 0.19661193, 0.19661193, 0.53444665]],
                        [[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                         [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                         [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]]

        Case 2:
          Input:
            input.shape = [2, 3, 4]
            input.data = [[[2.0, 3.0, 4.0, 5.0],
                       [3.0, 4.0, 5.0, 6.0],
                       [7.0, 8.0, 8.0, 9.0]],
                      [[1.0, 2.0, 3.0, 4.0],
                       [5.0, 6.0, 7.0, 8.0],
                       [6.0, 7.0, 8.0, 9.0]]]
          Attrs:
            dim = 1

          Output:
            out.shape = [2, 3, 4]
            out.data = [[[0.00657326, 0.00657326, 0.01714783, 0.01714783],
                         [0.01786798, 0.01786798, 0.04661262, 0.04661262],
                         [0.97555875, 0.97555875, 0.93623955, 0.93623955]],
                        [[0.00490169, 0.00490169, 0.00490169, 0.00490169],
                         [0.26762315, 0.26762315, 0.26762315, 0.26762315],
                         [0.72747516, 0.72747516, 0.72747516, 0.72747516]]]

    Parameters:
        input (Tensor): The input Tensor with data type bfloat16, float16, float32, float64.
        dim (int, optional): The dim along which to perform softmax
            calculations. It should be in range [-D, D), where D is the
            rank of ``input`` . If ``dim`` < 0, it works the same way as
            :math:`dim + D` . Default is None.
        dtype (str, optional): The data type of the output tensor, can be bfloat16, float16, float32, float64.
        out (Tensor, optional): The output Tensor.

    Returns:
        A Tensor with the same shape and data type (use ``dtype`` if it is
        specified) as input.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> x = paddle.to_tensor([[[2.0, 3.0, 4.0, 5.0],
            ...                        [3.0, 4.0, 5.0, 6.0],
            ...                        [7.0, 8.0, 8.0, 9.0]],
            ...                       [[1.0, 2.0, 3.0, 4.0],
            ...                        [5.0, 6.0, 7.0, 8.0],
            ...                        [6.0, 7.0, 8.0, 9.0]]],dtype='float32')
            >>> out1 = paddle.compat.softmax(x, -1)
            >>> out2 = paddle.compat.softmax(x, -1, dtype='float64')
            >>> #out1's data type is float32; out2's data type is float64
            >>> #out1 and out2's value is as follows:
            >>> print(out1)
            >>> print(out2)
            Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
            [[[0.03205860, 0.08714432, 0.23688284, 0.64391428],
              [0.03205860, 0.08714432, 0.23688284, 0.64391428],
              [0.07232949, 0.19661194, 0.19661194, 0.53444666]],
             [[0.03205860, 0.08714432, 0.23688284, 0.64391428],
              [0.03205860, 0.08714432, 0.23688284, 0.64391428],
              [0.03205860, 0.08714432, 0.23688284, 0.64391428]]])
            Tensor(shape=[2, 3, 4], dtype=float64, place=Place(cpu), stop_gradient=True,
            [[[0.03205860, 0.08714432, 0.23688282, 0.64391426],
              [0.03205860, 0.08714432, 0.23688282, 0.64391426],
              [0.07232949, 0.19661193, 0.19661193, 0.53444665]],
             [[0.03205860, 0.08714432, 0.23688282, 0.64391426],
              [0.03205860, 0.08714432, 0.23688282, 0.64391426],
              [0.03205860, 0.08714432, 0.23688282, 0.64391426]]])
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