Fixed typos and grammatical errors

CONTRIBUTING.md

@@ -89,8 +89,8 @@ Follow either of the two links above to access the appropriate CLA and instructi
 ### Contributing code
 
 If you have improvements to TensorFlow, send us your pull requests! For those
-just getting started, Github has a
-[how to](https://help.github.com/articles/using-pull-requests/).
+just getting started, GitHub has a
+[how-to](https://help.github.com/articles/using-pull-requests/).
 
 TensorFlow team members will be assigned to review your pull requests. Once the
 pull requests are approved and pass continuous integration checks, a TensorFlow
@@ -101,7 +101,7 @@ automatically on GitHub.
 
 If you want to contribute, start working through the TensorFlow codebase,
 navigate to the
-[Github "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
+[GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
 looking through interesting issues. If you are not sure of where to start, then
 start by trying one of the smaller/easier issues here i.e.
 [issues with the "good first issue" label](https://github.com/tensorflow/tensorflow/labels/good%20first%20issue)

ISSUES.md

@@ -1,9 +1,9 @@
 If you open a GitHub Issue, here is our policy:
 
-1.  It must be a bug/performance issue or a feature request or a build issue or
+1. It must be a bug/performance issue or a feature request or a build issue or
     a documentation issue (for small doc fixes please send a PR instead).
-1.  Make sure the Issue Template is filled out.
-1.  The issue should be related to the repo it is created in.
+2. Make sure the Issue Template is filled out.
+3. The issue should be related to the repo it is created in.
 
 **Here's why we have this policy:** We want to focus on the work that benefits
 the whole community, e.g., fixing bugs and adding features. Individual support

README.md

@@ -14,7 +14,7 @@
 [![TF Official Continuous](https://tensorflow.github.io/build/TF%20Official%20Continuous.svg)](https://tensorflow.github.io/build#TF%20Official%20Continuous)
 [![TF Official Nightly](https://tensorflow.github.io/build/TF%20Official%20Nightly.svg)](https://tensorflow.github.io/build#TF%20Official%20Nightly)
 
-**`Documentation`** |
+ **`Documentation`** |
 ------------------- |
 [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) |
 
@@ -114,7 +114,7 @@ apply fixes to bugs or security vulnerabilities:
 
 *   Clone the TensorFlow repo and switch to the corresponding branch for your
     desired TensorFlow version, for example, branch `r2.8` for version 2.8.
-*   Apply (that is, cherry pick) the desired changes and resolve any code
+*   Apply (that is, cherry-pick) the desired changes and resolve any code
     conflicts.
 *   Run TensorFlow tests and ensure they pass.
 *   [Build](https://www.tensorflow.org/install/source) the TensorFlow pip

RELEASE.md

@@ -97,7 +97,7 @@
     *   Optimized this function for some cases by fusing internal operations.
 
 *   `tf.saved_model.SaveOptions`
-    *   Provided a new `experimental_skip_saver` argument which if specified,
+    *   Provided a new `experimental_skip_saver` argument which, if specified,
         will suppress the addition of `SavedModel`-native save and restore ops
         to the `SavedModel`, for cases where users already build custom
         save/restore ops and checkpoint formats for the model being saved, and

SECURITY.md

@@ -8,12 +8,12 @@ TensorFlow and how to report them.
 This document applies to other repositories in the TensorFlow organization,
 covering security practices for the entirety of the TensorFlow ecosystem.
 
-## TensorFlow models are programs
+## TensorFlow's models are programs
 
 TensorFlow
 [**models**](https://developers.google.com/machine-learning/glossary/#model) (to
 use a term commonly used by machine learning practitioners) are expressed as
-programs that TensorFlow executes. TensorFlow programs are encoded as
+programs that TensorFlow executes. TensorFlow's programs are encoded as
 computation
 [**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
 The model's parameters are often stored separately in **checkpoints**.
@@ -31,7 +31,7 @@ The computation graph may also accept **inputs**. Those inputs are the
 data you supply to TensorFlow to train a model, or to use a model to run
 inference on the data.
 
-**TensorFlow models are programs, and need to be treated as such from a security
+**TensorFlow's models are programs, and need to be treated as such from a security
 perspective.**
 
 ## Execution models of TensorFlow code
@@ -101,7 +101,7 @@ compare model quality for a fixed model architecture), you must carefully audit
 your model, and we recommend you run the TensorFlow process in a sandbox.
 
 Similar considerations should apply if the model uses **custom ops** (C++ code
-written outside of the TensorFlow tree and loaded as plugins).
+written outside the TensorFlow tree and loaded as plugins).
 
 ## Accepting untrusted inputs
 

tensorflow/python/autograph/g3doc/reference/common_errors.md

@@ -88,7 +88,7 @@ AutoGraph turned off.
   * placing a Tensor-dependent `break`, `continue` or `return` inside a Python
     loop (see example below)
   * attempting to use a `tf.Tensor` in a list comprehension, by iterating over
-    it or using it in a condition)
+    it or using it in a condition
 
 A typical example of mixing Python and TF control flow in an incompatible way
 is:
@@ -156,7 +156,7 @@ exceptions, expect them to be wrapped by this exception.
 This error usually appears in the context of a conversion warning. It indicates
 that a lambda function could not be parsed (see [Limitations](limitations.md)).
 
-This type of errors can usually be avoided by creating lambda functions in
+This type of error can usually be avoided by creating lambda functions in
 separate simple assignments, for example:
 
 ```

tensorflow/python/autograph/g3doc/reference/control_flow.md

@@ -46,7 +46,7 @@ In the example above, we've optimized away the conditional on a constant
 condition. The AutoGraph dispatch rules have the same effect: anything that is
 not a TensorFlow object is a compile-time constant for TensorFlow, and can be
 optimized away. For this reason, you can usually mix Python and TensorFlow
-computation and it will transparently have the expected result even
+computation, and it will transparently have the expected result even
 when only some computations are executed in the graph.
 
 <!-- TODO(mdan): This is actually a limitation (a very subtle one) -->

tensorflow/python/autograph/g3doc/reference/error_handling.md

@@ -24,7 +24,7 @@ exception inside `tf.function`, you will obtain the original exception.
 The exception traceback still contains the entire call stack, including frames
 corresponding to generated code.
 
-AutoGraph tries to re-raise an exception of the same type as the original
+AutoGraph tries to re-raise an exception to the same type as the original
 exception. This is usually possible for subclasses of
 `Exception` that do not define a custom `__init__`. For more complex
 exception types which define a custom constructor, AutoGraph raises a
@@ -144,7 +144,7 @@ refer to symbols used in your code.
 
 ### TensorFlow execution errors
 
-TensorFlow execution errors are displayed normally, but the portions of the
+TensorFlow's execution errors are displayed normally, but the portions of the
 error message which correspond to user code contain references to the original
 code.
 

tensorflow/python/autograph/g3doc/reference/functions.md

@@ -45,7 +45,7 @@ are handled correctly.
 The following types of functions are not converted:
 
 *   functions already converted
-*   functions defined in a allowlisted module (see autograph/core/config.py)
+*   functions defined in an allowlisted module (see autograph/core/config.py)
 *   non-Python functions (such as native bindings)
 *   `print`, `pdb.set_trace`, `ipdb.set_trace`
 *   most built-in functions (exceptions are listed in

tensorflow/python/autograph/g3doc/reference/limitations.md

@@ -43,7 +43,7 @@ tf.print(x)  # Error -- x may be None here
 ```
 
 For this reason, AutoGraph forbids variables to be defined in only one branch
-of a TensorFlow conditional, if the variable is used afterwards:
+of a TensorFlow conditional, if the variable is used afterward:
 
 ```
 del x
@@ -172,7 +172,7 @@ The examples below use a `while` loop, but the same notions extend to all
 control flow such as `if` and `for` statements.
 
 In the example below, `x` needs to become a loop variable of the
-corresponding `tf.while_loop':
+corresponding 'tf.while_loop':
 
 ```
 while x > 0:
@@ -343,7 +343,7 @@ recognizes.
 
 AutoGraph assumes that variables that local functions close over may be used
 anywhere in the parent function, because in general it is possible to hide a
-function call in almost any Python statement). For this reason, these variables
+function call in almost any Python statement. For this reason, these variables
 are accounted within TensorFlow loops.
 
 For example, the following code correctly captures `a` in the TensorFlow loop
@@ -358,7 +358,7 @@ for i in tf.range(3):
 f()  # Prints 2
 ```
 
-An consequence is that these variables must be defined before the loop (see
+A consequence is that these variables must be defined before the loop (see
 Undefined and None values above). So the following code will raise an error,
 even if the variable is never used after the loop:
 
@@ -462,7 +462,7 @@ for i in tf.range(10):
 
 #### Python collections of fixed structure are allowed TensorFlow control flow
 
-An exception from the previous rule is made by Python collections that are
+An exception to the previous rule is made by Python collections that are
 static, that is, they don't grow in size for the duration of the computation.
 
 Caution: Use functional programming style when manipulating static collections.
@@ -503,7 +503,7 @@ for i in tf.range(10):
     d[key] += i  # Problem -- accessing `dict` using non-constant key
 ```
 
-The code above will raises an "illegal capture" error. To remedy it, write it
+The code above will raise an "illegal capture" error. To remedy it, write it
 in functional programming style:
 
 ```
@@ -530,7 +530,7 @@ rank is dynamic.
 
 TensorFlow has optional static types and shapes: the shape of tensors may be
 static (e.g. `my_tensor.shape=(3, 3)` denotes a three by three matrix) or
-dynamic (e.g. `my_tensor.shape=(None, 3)` denotes a matrix with a dynamic
+dynamic (e.g. `my_tensor.shape=(None, 3)`) denotes a matrix with a dynamic
 number of rows and three columns. When the shapes are dynamic, you can still
 query it at runtime by using the `tf.shape()` function.
 

tensorflow/python/autograph/g3doc/reference/operators.md

@@ -70,7 +70,7 @@ Generally, the dispatch follows these rules:
 
 The first rule above means that if you convert normal, non-TensorFlow code with
 AutoGraph and call it with non-TensorFlow inputs, executing the generated code
-should be no different than executing the original.
+should be no different from executing the original.
 
 ### Functional form
 
@@ -95,7 +95,7 @@ Args:
   cond: expression condition; same as `cond` in `_ if cond else _`.
   if_true: true value (as thunk); same as `lambda: x` in `x if _ else _`.
   if_false: false value (as thunk); same as `lambda: x` in `_ if _ else x`.
-  expr_repr: human readable string representing `cond`. Used for error messages.
+  expr_repr: human-readable string representing `cond`. Used for error messages.
 
 Example:
 
@@ -147,7 +147,7 @@ Args:
     <b>`.
 *   get_state: returns the current value of the loop variables
 *   set_state: sets new values into the loop variables
-*   symbol_names: human readable string representing each loop variable. Used
+*   symbol_names: human-readable string representing each loop variable. Used
     for error messages.
 *   opts: additional, implementation-specific, keyword arguments.
 
@@ -232,7 +232,7 @@ Args:
     <b>`.
 *   get_state: returns the current value of the conditional variables
 *   set_state: sets new values into the conditional variables
-*   symbol_names: human readable string representing each conditional variable.
+*   symbol_names: human-readable string representing each conditional variable.
     Used for error messages.
 *   nouts: number of output conditional variables. Not all conditional variables
     are outputs - some are just inputs. The first nouts values in get_state and
@@ -280,7 +280,7 @@ Args:
 *   body: loop body (as thunk); same as `def body(): <b>` in `while _: <b>`.
 *   get_state: returns the current value of the loop variables
 *   set_state: sets new values into the loop variables
-*   symbol_names: human readable string representing each loop variable. Used
+*   symbol_names: human-readable string representing each loop variable. Used
     for error messages.
 *   opts: additional, implementation-specific, keyword arguments.