[doc] Code pointers in CoreML doc should point to release/0.6 codebase (#10013)

### Summary
as titled.

Author: larryliu0820

docs/source/backends-coreml.md

@@ -55,24 +55,24 @@ with open("mv2_coreml.pte", "wb") as file:
 
 ### Partitioner API
 
-The CoreML partitioner API allows for configuration of the model delegation to CoreML. Passing an `CoreMLPartitioner` instance with no additional parameters will run as much of the model as possible on the CoreML backend with default settings. This is the most common use-case. For advanced use cases, the partitioner exposes the following options via the [constructor](https://github.com/pytorch/executorch/blob/14ff52ff89a89c074fc6c14d3f01683677783dcd/backends/apple/coreml/partition/coreml_partitioner.py#L60):
+The CoreML partitioner API allows for configuration of the model delegation to CoreML. Passing an `CoreMLPartitioner` instance with no additional parameters will run as much of the model as possible on the CoreML backend with default settings. This is the most common use-case. For advanced use cases, the partitioner exposes the following options via the [constructor](https://github.com/pytorch/executorch/blob/release/0.6/backends/apple/coreml/partition/coreml_partitioner.py#L60):
 
 
- - `skip_ops_for_coreml_delegation`: Allows you to skip ops for delegation by CoreML.  By default, all ops that CoreML supports will be delegated.  See [here](https://github.com/pytorch/executorch/blob/14ff52ff89a89c074fc6c14d3f01683677783dcd/backends/apple/coreml/test/test_coreml_partitioner.py#L42) for an example of skipping an op for delegation.
+ - `skip_ops_for_coreml_delegation`: Allows you to skip ops for delegation by CoreML.  By default, all ops that CoreML supports will be delegated.  See [here](https://github.com/pytorch/executorch/blob/release/0.6/backends/apple/coreml/test/test_coreml_partitioner.py#L42) for an example of skipping an op for delegation.
 - `compile_specs`: A list of CompileSpec for the CoreML backend.  These control low-level details of CoreML delegation, such as the compute unit (CPU, GPU, ANE), the iOS deployment target, and the compute precision (FP16, FP32).  These are discussed more below.
 - `take_over_mutable_buffer`: A boolean that indicates whether PyTorch mutable buffers in stateful models should be converted to [CoreML MLState](https://developer.apple.com/documentation/coreml/mlstate).  If set to false, mutable buffers in the PyTorch graph are converted to graph inputs and outputs to the CoreML lowered module under the hood.  Generally setting take_over_mutable_buffer to true will result in better performance, but using MLState requires iOS >= 18.0, macOS >= 15.0, and XCode >= 16.0.
 
 #### CoreML CompileSpec
 
-A list of CompileSpec is constructed with [CoreMLBackend.generate_compile_specs](https://github.com/pytorch/executorch/blob/14ff52ff89a89c074fc6c14d3f01683677783dcd/backends/apple/coreml/compiler/coreml_preprocess.py#L210).  Below are the available options:
+A list of CompileSpec is constructed with [CoreMLBackend.generate_compile_specs](https://github.com/pytorch/executorch/blob/release/0.6/backends/apple/coreml/compiler/coreml_preprocess.py#L210).  Below are the available options:
 - `compute_unit`: this controls the compute units (CPU, GPU, ANE) that are used by CoreML.  The default value is coremltools.ComputeUnit.ALL.  The available options from coremltools are:
     - coremltools.ComputeUnit.ALL (uses the CPU, GPU, and ANE)
     - coremltools.ComputeUnit.CPU_ONLY (uses the CPU only)
     - coremltools.ComputeUnit.CPU_AND_GPU (uses both the CPU and GPU, but not the ANE)
     - coremltools.ComputeUnit.CPU_AND_NE (uses both the CPU and ANE, but not the GPU)
 - `minimum_deployment_target`: The minimum iOS deployment target (e.g., coremltools.target.iOS18).  The default value is coremltools.target.iOS15.
 - `compute_precision`: The compute precision used by CoreML (coremltools.precision.FLOAT16, coremltools.precision.FLOAT32).  The default value is coremltools.precision.FLOAT16.  Note that the compute precision is applied no matter what dtype is specified in the exported PyTorch model.  For example, an FP32 PyTorch model will be converted to FP16 when delegating to the CoreML backend by default.  Also note that the ANE only supports FP16 precision.
-- `model_type`: Whether the model should be compiled to the CoreML [mlmodelc format](https://developer.apple.com/documentation/coreml/downloading-and-compiling-a-model-on-the-user-s-device) during .pte creation ([CoreMLBackend.MODEL_TYPE.COMPILED_MODEL](https://github.com/pytorch/executorch/blob/14ff52ff89a89c074fc6c14d3f01683677783dcd/backends/apple/coreml/compiler/coreml_preprocess.py#L71)), or whether it should be compiled to mlmodelc on device ([CoreMLBackend.MODEL_TYPE.MODEL](https://github.com/pytorch/executorch/blob/14ff52ff89a89c074fc6c14d3f01683677783dcd/backends/apple/coreml/compiler/coreml_preprocess.py#L70)).  Using CoreMLBackend.MODEL_TYPE.COMPILED_MODEL and doing compilation ahead of time should improve the first time on-device model load time.
+- `model_type`: Whether the model should be compiled to the CoreML [mlmodelc format](https://developer.apple.com/documentation/coreml/downloading-and-compiling-a-model-on-the-user-s-device) during .pte creation ([CoreMLBackend.MODEL_TYPE.COMPILED_MODEL](https://github.com/pytorch/executorch/blob/release/0.6/backends/apple/coreml/compiler/coreml_preprocess.py#L71)), or whether it should be compiled to mlmodelc on device ([CoreMLBackend.MODEL_TYPE.MODEL](https://github.com/pytorch/executorch/blob/release/0.6/backends/apple/coreml/compiler/coreml_preprocess.py#L70)).  Using CoreMLBackend.MODEL_TYPE.COMPILED_MODEL and doing compilation ahead of time should improve the first time on-device model load time.
 
 ### Testing the Model