[xdoctest] reformat example code with google style in No.91-94 (#55832)

* test=docs_preview

* test=docs_preview

* test=docs_preview

* test=docs_preview

python/paddle/nn/decode.py

@@ -189,20 +189,20 @@ class BeamSearchDecoder(Decoder):
 
         .. code-block:: python
 
-            import numpy as np
-            import paddle
-            from paddle.nn import BeamSearchDecoder, dynamic_decode
-            from paddle.nn import GRUCell, Linear, Embedding
-            trg_embeder = Embedding(100, 32)
-            output_layer = Linear(32, 32)
-            decoder_cell = GRUCell(input_size=32, hidden_size=32)
-            decoder = BeamSearchDecoder(decoder_cell,
-                                        start_token=0,
-                                        end_token=1,
-                                        beam_size=4,
-                                        embedding_fn=trg_embeder,
-                                        output_fn=output_layer)
-
+            >>> import numpy as np
+            >>> import paddle
+            >>> from paddle.nn import BeamSearchDecoder, dynamic_decode
+            >>> from paddle.nn import GRUCell, Linear, Embedding
+            >>> trg_embeder = Embedding(100, 32)
+            >>> output_layer = Linear(32, 32)
+            >>> decoder_cell = GRUCell(input_size=32, hidden_size=32)
+            >>> decoder = BeamSearchDecoder(decoder_cell,
+            ...                             start_token=0,
+            ...                             end_token=1,
+            ...                             beam_size=4,
+            ...                             embedding_fn=trg_embeder,
+            ...                             output_fn=output_layer)
+            ...
     """
 
     def __init__(
@@ -1054,22 +1054,24 @@ def dynamic_decode(
 
         .. code-block:: python
 
-            import paddle
-            from paddle.nn import BeamSearchDecoder, dynamic_decode
-            from paddle.nn import GRUCell, Linear, Embedding
-            trg_embeder = Embedding(100, 32)
-            output_layer = Linear(32, 32)
-            decoder_cell = GRUCell(input_size=32, hidden_size=32)
-            decoder = BeamSearchDecoder(decoder_cell,
-                                        start_token=0,
-                                        end_token=1,
-                                        beam_size=4,
-                                        embedding_fn=trg_embeder,
-                                        output_fn=output_layer)
-            encoder_output = paddle.ones((4, 8, 32), dtype=paddle.get_default_dtype())
-            outputs = dynamic_decode(decoder=decoder,
-                                    inits=decoder_cell.get_initial_states(encoder_output),
-                                    max_step_num=10)
+            >>> import paddle
+            >>> from paddle.nn import BeamSearchDecoder, dynamic_decode
+            >>> from paddle.nn import GRUCell, Linear, Embedding
+            >>> trg_embeder = Embedding(100, 32)
+            >>> output_layer = Linear(32, 32)
+            >>> decoder_cell = GRUCell(input_size=32, hidden_size=32)
+            >>> decoder = BeamSearchDecoder(decoder_cell,
+            ...                             start_token=0,
+            ...                             end_token=1,
+            ...                             beam_size=4,
+            ...                             embedding_fn=trg_embeder,
+            ...                             output_fn=output_layer)
+            >>> encoder_output = paddle.ones((4, 8, 32), dtype=paddle.get_default_dtype())
+            >>> outputs = dynamic_decode(decoder=decoder,
+            ...                          inits=decoder_cell.get_initial_states(encoder_output),
+            ...                          max_step_num=10)
+            >>> print(outputs[0].shape)
+            [4, 11, 4]
     """
     if in_dynamic_mode():
         return _dynamic_decode_imperative(

python/paddle/nn/quant/format.py

@@ -149,29 +149,31 @@ class ConvertibleQuantedLayer(Layer, metaclass=abc.ABCMeta):
     It defines some functions to convert quantizers and observers to quantize
     or dequantize operators that maintain the quantization parameters used
     during inference.
+
     Examples:
-       .. code-block:: python
-
-            # Given codes in ./customized_quanter.py
-            class CustomizedQuantedLayer(ConvertibleQuantedLayer):
-                def __init__(self):
-                    super().__init__()
-                    self.weight_a = paddle.create_parameter(shape=[1], dtype='float32')
-                    self.weight_b = paddle.create_parameter(shape=[1], dtype='float32')
-                    self.quanter_for_weight_a = None
-                    self.activation_weight = None
-                def forward(self, input):
-                    qweight_a = self.quanter_for_weight_a(self.weight_a)
-                    weight_b = self.weight_b
-                    qinput = self.activation_weight(input)
-                    // compute with qweight_a, weight_b and qinput.
-                    return qweight * qinput + weight_b
-
-                def weights_to_quanters(self):
-                    return [('weight_a', 'quanter_for_weight_a')]
-
-                def activation_quanters(self):
-                    return ['activation_weight']
+        .. code-block:: python
+
+            >>> # Given codes in ./customized_quanter.py
+            >>> class CustomizedQuantedLayer(ConvertibleQuantedLayer):
+            ...     def __init__(self):
+            ...         super().__init__()
+            ...         self.weight_a = paddle.create_parameter(shape=[1], dtype='float32')
+            ...         self.weight_b = paddle.create_parameter(shape=[1], dtype='float32')
+            ...         self.quanter_for_weight_a = None
+            ...         self.activation_weight = None
+            ...
+            ...     def forward(self, input):
+            ...         qweight_a = self.quanter_for_weight_a(self.weight_a)
+            ...         weight_b = self.weight_b
+            ...         qinput = self.activation_weight(input)
+            ...         # compute with qweight_a, weight_b and qinput.
+            ...         return qweight * qinput + weight_b
+            ...
+            ...     def weights_to_quanters(self):
+            ...         return [('weight_a', 'quanter_for_weight_a')]
+            ...
+            ...     def activation_quanters(self):
+            ...         return ['activation_weight']
     """
 
     def __init__(self):