[Docs] Fix delimiter of argument in docstring

python/paddle/base/framework.py

@@ -6963,7 +6963,7 @@ def block(self, index):
         Get the :code:`index`  :ref:`api_guide_Block_en`  of this Program
 
         Args:
-            index (int) - The index of  :ref:`api_guide_Block_en`  to get
+            index (int): The index of  :ref:`api_guide_Block_en`  to get
 
         Returns:
             :ref:`api_guide_Block_en`: The :code:`index` block

python/paddle/nn/functional/vision.py

@@ -36,7 +36,7 @@ def affine_grid(theta, out_shape, align_corners=True, name=None):
     output feature map.
 
     Args:
-        theta (Tensor) - A tensor with shape [N, 2, 3] or [N, 3, 4]. It contains a batch of affine transform parameters.
+        theta (Tensor): A tensor with shape [N, 2, 3] or [N, 3, 4]. It contains a batch of affine transform parameters.
                            The data type can be float32 or float64.
         out_shape (Tensor | list | tuple): Type can be a 1-D Tensor, list, or tuple. It is used to represent the shape of the output in an affine transformation, in the format ``[N, C, H, W]`` or ``[N, C, D, H, W]``.
                                            When the format is ``[N, C, H, W]``, it represents the batch size, number of channels, height and width. When the format is ``[N, C, D, H, W]``, it represents the batch size, number of channels, depth, height and width.

python/paddle/nn/quant/stub.py

@@ -26,8 +26,8 @@ class Stub(Layer):
     stub will observe or quantize the inputs of the functional API.
 
     Args:
-        observer(QuanterFactory) - The configured information of the observer to be inserted.
-        It will use a global configuration to create the observers if the 'observer' is none.
+        observer(QuanterFactory): The configured information of the observer to be inserted.
+            It will use a global configuration to create the observers if the 'observer' is none.
 
     Examples:
         .. code-block:: python
@@ -81,9 +81,9 @@ class QuanterStub(Layer):
     The user should not use this class directly.
 
     Args:
-        layer(paddle.nn.Layer) - The stub layer with an observer configure factory. If the observer
+        layer(paddle.nn.Layer): The stub layer with an observer configure factory. If the observer
         of the stub layer is none, it will use 'q_config' to create an observer instance.
-        q_config(QuantConfig) - The quantization configuration for the current stub layer.
+        q_config(QuantConfig): The quantization configuration for the current stub layer.
     """
 
     def __init__(self, layer: Stub, q_config):

python/paddle/quantization/factory.py

@@ -78,7 +78,7 @@ def quanter(class_name):
     Annotation to declare a factory class for quanter.
 
     Args:
-        class_name (str) - The name of factory class to be declared.
+        class_name (str): The name of factory class to be declared.
 
     Examples:
         .. code-block:: python

python/paddle/quantization/ptq.py

@@ -47,8 +47,8 @@ def quantize(self, model: Layer, inplace=False):
         quantization parameters.
 
         Args:
-            model(Layer) - The model to be quantized.
-            inplace(bool) - Whether to modify the model in-place.
+            model(Layer): The model to be quantized.
+            inplace(bool): Whether to modify the model in-place.
 
         Return: The prepared model for post-training quantization.
 

python/paddle/quantization/qat.py

@@ -24,7 +24,7 @@ class QAT(Quantization):
     r"""
     Tools used to prepare model for quantization-aware training.
     Args:
-        config(QuantConfig) - Quantization configuration
+        config(QuantConfig): Quantization configuration
 
     Examples:
         .. code-block:: python
@@ -47,8 +47,8 @@ def quantize(self, model: Layer, inplace=False):
         And it will insert fake quanters into the model to simulate the quantization.
 
         Args:
-            model(Layer) - The model to be quantized.
-            inplace(bool) - Whether to modify the model in-place.
+            model(Layer): The model to be quantized.
+            inplace(bool): Whether to modify the model in-place.
 
         Return: The prepared model for quantization-aware training.
 

python/paddle/quantization/quantize.py

@@ -29,7 +29,7 @@ class Quantization(metaclass=abc.ABCMeta):
     r"""
     Abstract class used to prepares a copy of the model for quantization calibration or quantization-aware training.
     Args:
-        config(QuantConfig) - Quantization configuration
+        config(QuantConfig): Quantization configuration
     """
 
     def __init__(self, config: QuantConfig):
@@ -44,9 +44,9 @@ def convert(self, model: Layer, inplace=False, remain_weight=False):
         r"""Convert the quantization model to ONNX style. And the converted
         model can be saved as inference model by calling paddle.jit.save.
         Args:
-            model(Layer) - The quantized model to be converted.
-            inplace(bool, optional) - Whether to modify the model in-place, default is False.
-            remain_weight(bool, optional) - Whether to remain weights in floats, default is False.
+            model(Layer): The quantized model to be converted.
+            inplace(bool, optional): Whether to modify the model in-place, default is False.
+            remain_weight(bool, optional): Whether to remain weights in floats, default is False.
 
         Return: The converted model
 

python/paddle/quantization/wrapper.py

@@ -22,9 +22,9 @@ class ObserveWrapper(Layer):
     Put an observer layer and an observed layer into a wrapping layer.
     It is used to insert layers into the model for QAT or PTQ.
     Args:
-        observer(BaseQuanter) - Observer layer
-        observed(Layer) - Observed layer
-        observe_input(bool) - If it is true the observer layer will be called before observed layer.
+        observer(BaseQuanter): Observer layer
+        observed(Layer): Observed layer
+        observe_input(bool): If it is true the observer layer will be called before observed layer.
             If it is false the observed layer will be called before observer layer. Default: True.
     """
 

python/paddle/text/datasets/wmt14.py

@@ -54,9 +54,9 @@ class WMT14(Dataset):
 
     Returns:
         Dataset: Instance of WMT14 dataset
-            - src_ids (np.array) - The sequence of token ids of source language.
-            - trg_ids (np.array) - The sequence of token ids of target language.
-            - trg_ids_next (np.array) - The next sequence of token ids of target language.
+            - src_ids (np.array): The sequence of token ids of source language.
+            - trg_ids (np.array): The sequence of token ids of target language.
+            - trg_ids_next (np.array): The next sequence of token ids of target language.
     Examples:
 
         .. code-block:: python

python/paddle/text/datasets/wmt16.py

@@ -69,9 +69,9 @@ class WMT16(Dataset):
 
     Returns:
         Dataset: Instance of WMT16 dataset. The instance of dataset has 3 fields:
-            - src_ids (np.array) - The sequence of token ids of source language.
-            - trg_ids (np.array) - The sequence of token ids of target language.
-            - trg_ids_next (np.array) - The next sequence of token ids of target language.
+            - src_ids (np.array): The sequence of token ids of source language.
+            - trg_ids (np.array): The sequence of token ids of target language.
+            - trg_ids_next (np.array): The next sequence of token ids of target language.
 
     Examples: