chore(evo1): align with policy contribution guide conventions

- Add `src/lerobot/policies/evo1/README.md` symlink into `docs/source/evo1.mdx`
  to match the in-tree README convention (mirroring the EO-1 layout).
- Convert `transformers` import in `internvl3_embedder.py` to the standard
  `TYPE_CHECKING + _transformers_available` two-step gating used by other
  optional-backbone policies (e.g. diffusion). The previous lazy-in-`__init__`
  import was functionally equivalent for runtime gating but didn't expose the
  real symbols to type checkers.
- Add `lerobot[evo1]` to the `all` extra in `pyproject.toml` so
  `pip install 'lerobot[all]'` keeps installing every optional policy.

Per the guidance in https://moon-ci-docs.huggingface.co/docs/lerobot/pr_3534/en/contributing_a_policy.

pyproject.toml

@@ -259,6 +259,7 @@ all = [
     "lerobot[smolvla]",
     # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
     "lerobot[xvla]",
+    "lerobot[evo1]",
     "lerobot[hilserl]",
     "lerobot[async]",
     "lerobot[dev]",

src/lerobot/policies/evo1/README.md

@@ -0,0 +1 @@
+../../../../docs/source/evo1.mdx

src/lerobot/policies/evo1/internvl3_embedder.py

@@ -17,6 +17,7 @@ from __future__ import annotations
 import functools
 import logging
 from collections.abc import Sequence
+from typing import TYPE_CHECKING
 
 import torch
 import torch.nn as nn
@@ -24,7 +25,13 @@ import torchvision.transforms.functional as TF
 from PIL import Image
 from torchvision.transforms.functional import to_pil_image
 
-from lerobot.utils.import_utils import require_package
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoModel, AutoTokenizer
+else:
+    AutoModel = None
+    AutoTokenizer = None
 
 IMAGENET_MEAN = (0.485, 0.456, 0.406)
 IMAGENET_STD = (0.229, 0.224, 0.225)
@@ -111,7 +118,6 @@ class InternVL3Embedder(nn.Module):
         self.gradient_checkpointing_use_reentrant = bool(gradient_checkpointing_use_reentrant)
 
         require_package("transformers", extra="evo1")
-        from transformers import AutoModel, AutoTokenizer
 
         self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)
         if isinstance(model_dtype, str):

uv.lock

@@ -3079,6 +3079,7 @@ requires-dist = [
     { name = "lerobot", extras = ["diffusers-dep"], marker = "extra == 'multi-task-dit'" },
     { name = "lerobot", extras = ["diffusion"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["dynamixel"], marker = "extra == 'all'" },
+    { name = "lerobot", extras = ["evo1"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["feetech"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["feetech"], marker = "extra == 'hopejr'" },
     { name = "lerobot", extras = ["feetech"], marker = "extra == 'lekiwi'" },