chore: import upstream snapshot with attribution

tensorflow/lite/g3doc/guide/ios.md

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+# iOS quickstart
+
+To get started with TensorFlow Lite on iOS, we recommend exploring the following
+example:
+
+<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios">iOS
+image classification example</a>
+
+For an explanation of the source code, you should also read
+[TensorFlow Lite iOS image classification](https://github.com/tensorflow/examples/blob/master/lite/examples/image_classification/ios/README.md).
+
+This example app uses
+[image classification](https://www.tensorflow.org/lite/examples/image_classification/overview)
+to continuously classify whatever it sees from the device's rear-facing camera,
+displaying the top most probable classifications. It allows the user to choose
+between a floating point or
+[quantized](https://www.tensorflow.org/lite/performance/post_training_quantization)
+model and select the number of threads to perform inference on.
+
+Note: Additional iOS applications demonstrating TensorFlow Lite in a variety of
+use cases are available in [Examples](https://www.tensorflow.org/lite/examples).
+
+## Add TensorFlow Lite to your Swift or Objective-C project
+
+TensorFlow Lite offers native iOS libraries written in
+[Swift](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/swift)
+and
+[Objective-C](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/objc).
+Start writing your own iOS code using the
+[Swift image classification example](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios)
+as a starting point.
+
+The sections below demonstrate how to add TensorFlow Lite Swift or Objective-C
+to your project:
+
+### CocoaPods developers
+
+In your `Podfile`, add the TensorFlow Lite pod. Then, run `pod install`.
+
+#### Swift
+
+```ruby
+use_frameworks!
+pod 'TensorFlowLiteSwift'
+```
+
+#### Objective-C
+
+```ruby
+pod 'TensorFlowLiteObjC'
+```
+
+#### Specifying versions
+
+There are stable releases, and nightly releases available for both
+`TensorFlowLiteSwift` and `TensorFlowLiteObjC` pods. If you do not specify a
+version constraint as in the above examples, CocoaPods will pull the latest
+stable release by default.
+
+You can also specify a version constraint. For example, if you wish to depend on
+version 2.10.0, you can write the dependency as:
+
+```ruby
+pod 'TensorFlowLiteSwift', '~> 2.10.0'
+```
+
+This will ensure the latest available 2.x.y version of the `TensorFlowLiteSwift`
+pod is used in your app. Alternatively, if you want to depend on the nightly
+builds, you can write:
+
+```ruby
+pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly'
+```
+
+From 2.4.0 version and latest nightly releases, by default
+[GPU](https://www.tensorflow.org/lite/performance/gpu) and
+[Core ML delegates](https://www.tensorflow.org/lite/performance/coreml_delegate)
+are excluded from the pod to reduce the binary size. You can include them by
+specifying subspec:
+
+```ruby
+pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => ['CoreML', 'Metal']
+```
+
+This will allow you to use the latest features added to TensorFlow Lite. Note
+that once the `Podfile.lock` file is created when you run `pod install` command
+for the first time, the nightly library version will be locked at the current
+date's version. If you wish to update the nightly library to the newer one, you
+should run `pod update` command.
+
+For more information on different ways of specifying version constraints, see
+[Specifying pod versions](https://guides.cocoapods.org/using/the-podfile.html#specifying-pod-versions).
+
+### Bazel developers
+
+In your `BUILD` file, add the `TensorFlowLite` dependency to your target.
+
+#### Swift
+
+```python
+swift_library(
+  deps = [
+      "//tensorflow/lite/swift:TensorFlowLite",
+  ],
+)
+```
+
+#### Objective-C
+
+```python
+objc_library(
+  deps = [
+      "//tensorflow/lite/objc:TensorFlowLite",
+  ],
+)
+```
+
+#### C/C++ API
+
+Alternatively, you can use
+[C API](https://www.tensorflow.org/code/tensorflow/lite/c/c_api.h)
+or [C++ API](https://tensorflow.org/lite/api_docs/cc)
+
+```python
+# Using C API directly
+objc_library(
+  deps = [
+      "//tensorflow/lite/c:c_api",
+  ],
+)
+
+# Using C++ API directly
+objc_library(
+  deps = [
+      "//tensorflow/lite:framework",
+  ],
+)
+```
+
+### Import the library
+
+For Swift files, import the TensorFlow Lite module:
+
+```swift
+import TensorFlowLite
+```
+
+For Objective-C files, import the umbrella header:
+
+```objectivec
+#import "TFLTensorFlowLite.h"
+```
+
+Or, the module if you set `CLANG_ENABLE_MODULES = YES` in your Xcode project:
+
+```objectivec
+@import TFLTensorFlowLite;
+```
+
+Note: For CocoaPods developers who want to import the Objective-C TensorFlow
+Lite module, you must also include `use_frameworks!` in your `Podfile`.