[CodeStyle][Typos][T-[6-10]] Fix typo('Ture',''traget,''tempory,'tenosr','thier')

_typos.toml

@@ -53,7 +53,6 @@ Similarily = "Similarily"
 Simle = "Simle"
 Sovler = "Sovler"
 Successed = "Successed"
-Ture = "Ture"
 accordding = "accordding"
 accoustic = "accoustic"
 accpetance = "accpetance"
@@ -190,9 +189,6 @@ sucess = "sucess"
 sucessor = "sucessor"
 sucessors = "sucessors"
 szie = "szie"
-tempory = "tempory"
-thier = "thier"
-traget = "traget"
 traing = "traing"
 trainning = "trainning"
 transfered = "transfered"

docs/api/paddle/combinations_cn.rst

@@ -6,7 +6,7 @@ combinations
 .. py:function:: paddle.combinations(x, r=2, with_replacement=False, name=None)
 
 对输入 Tensor 计算长度为 r 的情况下的所有组合，当 `with_replacement` 设为 False，可类比 python 内置 API `itertools.combinations` 。
-当 `with_replacement` 设为 True，可类比 python 内置 API `itertools.combinations_with_replacement(with_replacement=Ture)`。
+当 `with_replacement` 设为 True，可类比 python 内置 API `itertools.combinations_with_replacement(with_replacement=True)`。
 
 参数
 ::::::::::

docs/design/memory/memory_optimization.md

@@ -49,7 +49,7 @@ It's not enough to only have some basic strategies. The pre-requisite of memory
 
 In our design, the neural network topology is defined as a program. Luckily, [live variable analysis](https://en.wikipedia.org/wiki/Live_variable_analysis) is a classic problem in compilers which can be used in many stages, such as register allocation.
 
-In compilers, the front end of the compiler translates programs into an intermediate language with an unbounded number of temporary variables. This program must run on a machine with a bounded number of registers. Two temporary variables a and b can fit into the same register, if a and b are never "in use" at the same time. Thus, many temporary variables can fit in few registers; if they don't all fit, the excess tempory variables can be kept in memory.
+In compilers, the front end of the compiler translates programs into an intermediate language with an unbounded number of temporary variables. This program must run on a machine with a bounded number of registers. Two temporary variables a and b can fit into the same register, if a and b are never "in use" at the same time. Thus, many temporary variables can fit in few registers; if they don't all fit, the excess temporary variables can be kept in memory.
 
 Therefore, the compiler needs to analyze the intermediate-representation program to determine which temporary variables are in use at the same time. We say a variable is "live" if it holds a value that may be needed in the future, so this analysis is called liveness analysis.
 

docs/design/modules/prune.md

@@ -45,7 +45,7 @@ If an operator needs to be run, it must fall into one of the following cases:
 1. It is the target.
 2. It is depended by some other ops, meaning its output is some other op's input.
 
-The first case can be checked by `op_desc.is_traget()` . The second case can be implement as
+The first case can be checked by `op_desc.is_target()` . The second case can be implement as
 
 ```c++
 bool HasDependentVar(const OpDesc& op_desc, const std::set<string>& dependent_vars) {

docs/guides/advanced/layer_and_model_en.md

@@ -11,7 +11,7 @@ In this guide, you will learn how to define and make use of models in Paddle, an
 
 In Paddle, most models consist of a series of layers. Layer serves as the foundamental logical unit of a model, composed of two parts: the variable that participates in the computation and the operator(s) that actually perform the execution.
 
-Constructing a model from scratch could be painful, with tons of nested codes to write and maintain. To make life easier, Paddle provides foundamental data structure ``paddle.nn.Layer`` to simplify the contruction of layer or model. One may easily inherit from ``paddle.nn.Layer`` to define thier custom layers or models. In addition, since both model and layer are essentially inherited from ``paddle.nn.Layer``, model is nothing but a special layer in Paddle.
+Constructing a model from scratch could be painful, with tons of nested codes to write and maintain. To make life easier, Paddle provides foundamental data structure ``paddle.nn.Layer`` to simplify the contruction of layer or model. One may easily inherit from ``paddle.nn.Layer`` to define their custom layers or models. In addition, since both model and layer are essentially inherited from ``paddle.nn.Layer``, model is nothing but a special layer in Paddle.
 
 Now let us construct a model using ``paddle.nn.Layer``: