!27851 cast output dtype for RNNs operators and add `num_directions` description for LSTM

Merge pull request !27851 from 吕昱峰（Nate.River）/rnn_op

mindspore/python/mindspore/nn/layer/rnns.py

@@ -483,7 +483,9 @@ class _RNNBase(Cell):
             x, h = self._stacked_dynamic_rnn(x, hx, seq_length)
         if self.batch_first:
             x = P.Transpose()(x, (1, 0, 2))
-        return x, h
+        if not self.is_lstm:
+            return x.astype(mstype.float32), h.astype(mstype.float32)
+        return x.astype(mstype.float32), (h[0].astype(mstype.float32), h[1].astype(mstype.float32))
 
 class RNN(_RNNBase):
     r"""
@@ -690,7 +692,8 @@ class LSTM(_RNNBase):
         batch_first (bool): Specifies whether the first dimension of input `x` is batch_size. Default: False.
         dropout (float, int): If not 0, append `Dropout` layer on the outputs of each
             LSTM layer except the last layer. Default 0. The range of dropout is [0.0, 1.0].
-        bidirectional (bool): Specifies whether it is a bidirectional LSTM. Default: False.
+        bidirectional (bool): Specifies whether it is a bidirectional LSTM,
+            num_directions=2 if bidirectional=True otherwise 1. Default: False.
 
     Inputs:
         - **x** (Tensor) - Tensor of shape (seq_len, batch_size, `input_size`) or