diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec
index 049b9ae2897c168d5ac2178a07aec130939a66a2..b5974eba2715ddd9161b2033d5247e3d64a06eb1 100644
--- a/paddle/fluid/API.spec
+++ b/paddle/fluid/API.spec
@@ -253,7 +253,7 @@ paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'nam
 paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '6fc5d7492830d60c7fa61b3bc8f0d7e7'))
 paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '4101ee1f9280f00dce54054ccc434890'))
 paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '67e6101c31314d4082621e8e443cfb68'))
-paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', 'cfa120e583cd4a5bfa120c8a26f98a28'))
+paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', '571c963b9b49f1a323d2ea2343f10dd2'))
 paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
 paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '9ac9bdc45be94494d8543b8cec5c26e0'))
 paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '2aed0f546f220364fb1da724a3176f74'))
@@ -307,7 +307,7 @@ paddle.fluid.layers.filter_by_instag (ArgSpec(args=['ins', 'ins_tag', 'filter_ta
 paddle.fluid.layers.shard_index (ArgSpec(args=['input', 'index_num', 'nshards', 'shard_id', 'ignore_value'], varargs=None, keywords=None, defaults=(-1,)), ('document', '3c6b30e9cd57b38d4a5fa1ade887f779'))
 paddle.fluid.layers.hard_swish (ArgSpec(args=['x', 'threshold', 'scale', 'offset', 'name'], varargs=None, keywords=None, defaults=(6.0, 6.0, 3.0, None)), ('document', 'bd763b9ca99239d624c3cb4626e3627a'))
 paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '88b967ef5132567396062d5d654b3064'))
-paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '126ede8ce0e751244b1b54cd359c89d7'))
+paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '34e7c1ff0263baf9551000b6bb3bc47e'))
 paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9d7806e31bdf727c1a23b8782a09b545'))
 paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'd5b41c7b2df1b064fbd42dcf435268cd'))
 paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '556fa82daf62cbb0fb393f4125daba77'))
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index bdbd4cf723bc207a2105ab4cb40e5ada6d71530b..40f0d717cb59b3718ac4430a1602ea207587e81c 100755
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -12602,28 +12602,64 @@ def uniform_random_batch_size_like(input,
                                    max=1.0,
                                    seed=0):
     """
-    ${comment}
+    This OP initializes a variable with random values sampled from a
+    uniform distribution in the range [min, max). The input_dim_idx used to get the input dimension value which will be used to resize the output dimension.
+
+    .. code-block:: text
+
+        *Case 1:
 
+            Given:
+                input =[[0.946741  , 0.1357001 , 0.38086128]]    # input.shape=[1,3]
+                shape=[2,4]
+
+            result.shape[output_dim_idx] = input.shape[input_dim_idx],
+            output_dim_idx = 0, 
+            input_dim_idx = 0,
+            result.shape[0] = input.shape[0], 
+            then:
+                result=[[ 0.3443427 , -0.23056602,  0.3477049 ,  0.06139076]]    # result.shape=[1,4]
+            
+       *Case 2:
+           
+           Given:
+               input =[[0.946741  , 0.1357001 , 0.38086128]]     # input.shape=[1,3]
+               shape=[2,4]
+               input_dim_idx=1
+               output_dim_idx=1
+         
+           result.shape[output_dim_idx] = input.shape[input_dim_idx],
+           output_dim_idx = 1, 
+           input_dim_idx = 1,
+           result.shape[1] = input.shape[1], 
+           then:
+               result=[[-0.23133647, -0.84195036,  0.21441269],
+                       [-0.08774924,  0.25605237, -0.09403259]]    # result.shape=[2,3]
     Args:
-        input (Variable): ${input_comment}
-        shape (tuple|list): ${shape_comment}
-        input_dim_idx (Int): ${input_dim_idx_comment}
-        output_dim_idx (Int): ${output_dim_idx_comment}
-        min (Float): ${min_comment}
-        max (Float): ${max_comment}
-        seed (Int): ${seed_comment}
-        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
+        input (Variable): A Tensor. Supported data types: float32, float64.
+        shape (tuple|list): A python list or python tuple. The shape of the output Tensor, the data type is int.
+        input_dim_idx (int, optional): An index used to get the input dimension value which will be used to resize the output dimension. Default  0. 
+        output_dim_idx (int, optional): An index used to indicate the specific dimension that will be replaced by corresponding input dimension value. Default 0.
+        min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
+        max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
+        seed (int, optional):  Random seed used for generating samples. 0 means use a seed generated by the system.Note that if seed is not 0, this operator will always generate the same random numbers every time.
+        dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of output Tensor. Supported data types: float32, float64. Default float32.
     Returns:
-        out (Variable): ${out_comment}
+        Variable: A Tensor of the specified shape filled with uniform_random values. The shape of the Tensor is determined by the shape parameter and the specified dimension of the input Tensor.
 
     Examples:
         .. code-block:: python
 
             import paddle.fluid as fluid
-            import paddle.fluid.layers as layers 
+            
+            # example 1: 
+            input = fluid.data(name="input", shape=[1, 3], dtype='float32')
+            out_1 = fluid.layers.uniform_random_batch_size_like(input, [2, 4]) # out_1.shape=[1, 4]
 
-            input = layers.data(name="input", shape=[13, 11], dtype='float32')
-            out = layers.uniform_random_batch_size_like(input, [-1, 11])
+            # example 2: 
+            out_2 = fluid.layers.uniform_random_batch_size_like(input, [2, 4], input_dim_idx=1, output_dim_idx=1) # out_2.shape=[2, 3]
+
+            
     """
 
     helper = LayerHelper('uniform_random_batch_size_like', **locals())
@@ -16982,8 +17018,8 @@ def mse_loss(input, label):
 @templatedoc()
 def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
     """
-    This operator initializes a variable with random values sampled from a
-    uniform distribution. The random result is in set [min, max).
+    This OP initializes a variable with random values sampled from a
+    uniform distribution in the range [min, max).
 
     Examples:
     ::
@@ -16995,24 +17031,23 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
           result=[[0.8505902, 0.8397286]]
 
     Args:
-        shape (list|tuple|Variable): The shape of the output tensor, the data type of the integer is int,
-                                     and if the shape type is list or tuple, its elements can be an integer
-                                     or a tensor with the shape [1], the data type of the tensor is int64. 
-                                     If the shape type is Variable,it ia a 1D tensor, the data type of the tensor is int64.
-        dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of the output tensor, such as float32, float64.
+        shape (list|tuple|Variable): The shape of the output Tensor,  if the shape is a list or tuple, 
+                                     its elements can be an integer
+                                     or a Tensor with the shape [1], and the type of the Tensor is int64. 
+                                     If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor is int64.
+        dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the output Tensor. Supported data types: float32, float64.
                                                   Default: float32.
-        min (float, optional): Minimum value of uniform random, It's a closed interval. Default -1.0.
-        max (float, optional): Maximun value of uniform random, It's an open interval. Default 1.0.
+        min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
+        max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
         seed (int, optional): Random seed used for generating samples. 0 means use a
             seed generated by the system. Note that if seed is not 0, this
             operator will always generate the same random numbers every time.
             Default 0.
 
-    Returns: a Tensor with randomly initialized results whose data type is determined by the dtype parameter 
-                and whose dimension is determined by the shape parameter.
-    Return type: Variable
+    Returns: 
+        Variable: A Tensor of the specified shape filled with uniform_random values.
 
-    Throw exception:
+    Raises:
         TypeError: The shape type should be list or tupple or variable.
     
     Examples:
@@ -17031,7 +17066,7 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
 
             # example 3:
             # attr shape is a Variable, the data type must be int64
-            var_shape = fluid.layers.data(name='var_shape',shape=[2],append_batch_size=False)
+            var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
             result_3 = fluid.layers.uniform_random(var_shape)
 
     """