[Docs] Fix python specifiers in tutorials

[vacu9708]

Fixes #18073 (This issue was already fixed before this PR)

Problem

The setup tutorial prescribes:

conda create -yn executorch python=3.10.0

This version has a CPython bug that leaves PyTorch's LeafSpec's init=False
fields uninitialized. run_decompositions() in ExecuTorch deepcopies the
LeafSpec nodes and crashes.
This breaks the export pipeline (to_edge_transform_and_lower, to_edge, run_decompositions),
which leads to any model export attempt failing unconditionally on Python 3.10.0.

The error message (AttributeError: 'LeafSpec' object has no attribute 'type')
gives no indication that Python version is the cause, making it hard for tutorial followers to diagnose.

Upstream Fix Status

The fix has already landed in PyTorch main via pytorch/pytorch#177154 and is available in recent nightly builds.
However, ExecuTorch currently pins to torch==2.11.
The 2.11 branch was cut before the fix was merged, which means
the upstream fix does not apply until the ExecutTorch's torch pin is bumped to 2.12.
Until then, this PR is necessary to prevent tutorial followers from hitting the bug and getting lost.

Resolution

The CI build script (.ci/docker/build.sh) uses:

PYTHON_VERSION=3.10

conda interprets python=3.10 as a prefix, installing the latest available
3.10.x — currently 3.10.16 — which does not have this bug.
The CPython fix landed in 3.10.1.
This discrepancy (3.10 vs 3.10.0) makes the bug invisible to CI while affecting any user who follows the setup tutorial.

Fix python=3.10.0 -> python=3.10 to align the docs with CI.

Test plan

Not working

conda create -yn executorch3100 python=3.10.0
conda activate executorch3100

Working

conda create -yn executorch310 python=3.10
conda activate executorch310

import torch
from executorch.exir import to_edge_transform_and_lower
from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner

class MyModel(torch.nn.Module):
    def forward(self, x):
        return x + 1

# 1. Export your PyTorch model
model = MyModel().eval()
example_inputs = (torch.randn(1, 3, 224, 224),)
exported_program = torch.export.export(model, example_inputs)

# 2. Optimize for target hardware (switch backends with one line)
program = to_edge_transform_and_lower(
    exported_program,
    partitioner=[XnnpackPartitioner()]  # CPU | CoreMLPartitioner() for iOS | QnnPartitioner() for Qualcomm
).to_executorch()

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/19280

• 📄 Preview Python docs built from this PR

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❗ 1 Active SEVs

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❌ 2 New Failures, 1 Cancelled Job, 3 Unrelated Failures

As of commit a645615 with merge base a79521b ():

NEW FAILURES - The following jobs have failed:

• pull / test-lora-linux / linux-job (gh)
  RuntimeError: Command docker exec -t 3c601b1e7d2b0d1d0b0a6627de150a789aacbf824432924a04f2040f1f65526a /exec failed with exit code 1
• pull / test-lora-multimethod-linux / linux-job (gh)
  RuntimeError: Command docker exec -t af63b9deb864a61d6a749ad6b7a76bfdc86f92a03dfdfc61ba8d5f8349af30f7 /exec failed with exit code 1

CANCELLED JOB - The following job was cancelled. Please retry:

• pull / unittest / macos / macos-job (gh)

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / unittest / linux / linux-job (gh) (trunk failure)
  examples/models/llama/tests/test_static_attention.py::StaticAttentionTest::test_within_transformer
• pull / unittest-editable / linux / linux-job (gh) (trunk failure)
  examples/models/llama/tests/test_static_attention.py::StaticAttentionTest::test_within_transformer
• pull / unittest-editable / macos / macos-job (gh) (trunk failure)
  ##[error]The operation was canceled.

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