Fix paddle.t doc en and the annotation display on 4 en docs (#41699)

* gradients; test=document_fix

* fix VarType; test=document_fix

* fix vartype; test=document_fix

* cumsum; test=document_fix

* t; test=document_fix

python/paddle/fluid/backward.py

@@ -2054,7 +2054,7 @@ def gradients(targets, inputs, target_gradients=None, no_grad_set=None):
             y = paddle.static.nn.conv2d(x, 4, 1, bias_attr=False)
             y = F.relu(y)
             z = paddle.static.gradients([y], x)
-            print(z) # [var x@GRAD : fluid.VarType.LOD_TENSOR.shape(-1L, 2L, 8L, 8L).astype(VarType.FP32)]
+            print(z) # [var x@GRAD : LOD_TENSOR.shape(-1, 2, 8, 8).dtype(float32).stop_gradient(False)]
     """
     check_type(targets, 'targets', (framework.Variable, list, tuple),
                'paddle.static.gradients')

python/paddle/fluid/framework.py

@@ -6085,8 +6085,8 @@ def list_vars(self):
                 for var in prog.list_vars():
                     print(var)
 
-                # var img : paddle.VarType.LOD_TENSOR.shape(-1, 1, 28, 28).astype(VarType.FP32)
-                # var label : paddle.VarType.LOD_TENSOR.shape(-1, 1).astype(VarType.INT64)
+                # var img : LOD_TENSOR.shape(-1, 1, 28, 28).dtype(float32).stop_gradient(True)
+                # var label : LOD_TENSOR.shape(-1, 1).dtype(int64).stop_gradient(True)
         """
         for each_block in self.blocks:
             for each_var in list(each_block.vars.values()):
@@ -6119,8 +6119,8 @@ def all_parameters(self):
                 # Here will print all parameters in current program, in this example,
                 # the result is like:
                 #
-                # persist trainable param fc_0.w_0 : paddle.VarType.LOD_TENSOR.shape(13, 10).astype(VarType.FP32)
-                # persist trainable param fc_0.b_0 : paddle.VarType.LOD_TENSOR.shape(10,).astype(VarType.FP32)
+                # persist trainable param fc_0.w_0 : LOD_TENSOR.shape(13, 10).dtype(float32).stop_gradient(False)
+                # persist trainable param fc_0.b_0 : LOD_TENSOR.shape(10,).dtype(float32).stop_gradient(False)
                 #
                 # Here print(param) will print out all the properties of a parameter,
                 # including name, type and persistable, you can access to specific

python/paddle/static/input.py

@@ -147,8 +147,8 @@ class InputSpec(object):
             input = InputSpec([None, 784], 'float32', 'x')
             label = InputSpec([None, 1], 'int64', 'label')
 
-            print(input)  # InputSpec(shape=(-1, 784), dtype=VarType.FP32, name=x)
-            print(label)  # InputSpec(shape=(-1, 1), dtype=VarType.INT64, name=label)
+            print(input)  # InputSpec(shape=(-1, 784), dtype=paddle.float32, name=x)
+            print(label)  # InputSpec(shape=(-1, 1), dtype=paddle.int64, name=label)
     """
 
     def __init__(self, shape, dtype='float32', name=None):
@@ -190,7 +190,7 @@ def from_tensor(cls, tensor, name=None):
 
                 x = paddle.to_tensor(np.ones([2, 2], np.float32))
                 x_spec = InputSpec.from_tensor(x, name='x')
-                print(x_spec)  # InputSpec(shape=(2, 2), dtype=VarType.FP32, name=x)
+                print(x_spec)  # InputSpec(shape=(2, 2), dtype=paddle.float32, name=x)
 
         """
         if isinstance(tensor, (Variable, core.VarBase, core.eager.Tensor)):
@@ -219,7 +219,7 @@ def from_numpy(cls, ndarray, name=None):
 
                 x = np.ones([2, 2], np.float32)
                 x_spec = InputSpec.from_numpy(x, name='x')
-                print(x_spec)  # InputSpec(shape=(2, 2), dtype=VarType.FP32, name=x)
+                print(x_spec)  # InputSpec(shape=(2, 2), dtype=paddle.float32, name=x)
 
         """
         return cls(ndarray.shape, ndarray.dtype, name)
@@ -241,7 +241,7 @@ def batch(self, batch_size):
 
                 x_spec = InputSpec(shape=[64], dtype='float32', name='x')
                 x_spec.batch(4)
-                print(x_spec) # InputSpec(shape=(4, 64), dtype=VarType.FP32, name=x)
+                print(x_spec) # InputSpec(shape=(4, 64), dtype=paddle.float32, name=x)
 
         """
         if isinstance(batch_size, (list, tuple)):
@@ -273,7 +273,7 @@ def unbatch(self):
 
                 x_spec = InputSpec(shape=[4, 64], dtype='float32', name='x')
                 x_spec.unbatch()
-                print(x_spec) # InputSpec(shape=(64,), dtype=VarType.FP32, name=x)
+                print(x_spec) # InputSpec(shape=(64,), dtype=paddle.float32, name=x)
 
         """
         if len(self.shape) == 0:

python/paddle/tensor/linalg.py

@@ -1166,40 +1166,37 @@ def t(input, name=None):
     the paddle.transpose function which perm dimensions set 0 and 1.
 
     Args:
-        input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.
+        input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float32, float64, int32, int64.
         name(str, optional): The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`
     Returns:
         Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.
 
-    For Example:
-
-        .. code-block:: text
+    Examples:
 
+        .. code-block:: python
+           :name: code-example
+             import paddle
+             
              # Example 1 (0-D tensor)
-             x = tensor([0.79])
-             paddle.t(x) = tensor([0.79])
-
+             x = paddle.to_tensor([0.79])
+             paddle.t(x) # [0.79]
+             
              # Example 2 (1-D tensor)
-             x = tensor([0.79, 0.84, 0.32])
-             paddle.t(x) = tensor([0.79, 0.84, 0.32])
+             x = paddle.to_tensor([0.79, 0.84, 0.32])
+             paddle.t(x) # [0.79000002, 0.83999997, 0.31999999]
+             paddle.t(x).shape # [3]
 
              # Example 3 (2-D tensor)
-             x = tensor([0.79, 0.84, 0.32],
-                        [0.64, 0.14, 0.57])
-             paddle.t(x) = tensor([0.79, 0.64],
-                                  [0.84, 0.14],
-                                  [0.32, 0.57])
+             x = paddle.to_tensor([[0.79, 0.84, 0.32],
+                                  [0.64, 0.14, 0.57]])
+             x.shape # [2, 3]
+             paddle.t(x)
+             # [[0.79000002, 0.63999999],
+             #  [0.83999997, 0.14000000],
+             #  [0.31999999, 0.56999999]]
+             paddle.t(x).shape # [3, 2]
 
-     Examples:
-
-        .. code-block:: python
-
-            import paddle
-            x = paddle.ones(shape=[2, 3], dtype='int32')
-            x_transposed = paddle.t(x)
-            print(x_transposed.shape)
-            # [3, 2]
     """
     if len(input.shape) > 2:
         raise ValueError(

python/paddle/tensor/math.py

@@ -2967,7 +2967,7 @@ def cumsum(x, axis=None, dtype=None, name=None):
 
             y = paddle.cumsum(data, dtype='float64')
             print(y.dtype)
-            # VarType.FP64
+            # paddle.float64
     """
     if axis is None:
         flatten = True