[Doc] Add cross library en doc (#703)

* [Doc] Add cross library en doc.

* fix

* fix

* Apply suggestions from code review

Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

docs/en/advanced_tutorials/cross_library.md

 # Use modules from other libraries
 
-Coming soon. Please refer to [chinese documentation](https://mmengine.readthedocs.io/zh_CN/latest/examples/cross_library.html).
+Based on MMEngine's [Registry](registry.md) and [Config](config.md), users can build modules across libraries.
+For example, use [MMClassification](https://github.com/open-mmlab/mmclassification)'s backbones in [MMDetection](https://github.com/open-mmlab/mmdetection), or [MMDetection](https://github.com/open-mmlab/mmdetection)'s data transforms in [MMRotate](https://github.com/open-mmlab/mmrotate), or using [MMDetection](https://github.com/open-mmlab/mmdetection)'s detectors in [MMTracking](https://github.com/open-mmlab/mmtracking).
+
+Modules registered in the same registry tree can be called across libraries by adding the **package name prefix** before the module's type in the config. Here are some common examples:
+
+## Use backbone across libraries
+
+Taking the example of using MMClassification's ConvNeXt in MMDetection:
+
+Firstly, adding the `custom_imports` field to the config to register the backbones of MMClassification to the registry.
+
+Secondly, adding the package name of MMClassification `mmcls` to the `type` of the backbone as a prefix: `mmcls.ConvNeXt`
+
+```python
+# Use custom_imports to register mmcls models to the registry
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+
+model = dict(
+  type='MaskRCNN',
+  data_preprocessor=dict(...),
+  backbone=dict(
+      type='mmcls.ConvNeXt', # Add mmcls prefix to enable cross-library mechanism
+      arch='tiny',
+      out_indices=[0, 1, 2, 3],
+      drop_path_rate=0.4,
+      layer_scale_init_value=1.0,
+      gap_before_final_norm=False,
+      init_cfg=dict(
+          type='Pretrained',
+          checkpoint=
+          'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth',
+          prefix='backbone.')),
+  neck=dict(...),
+  rpn_head=dict(...))
+```
+
+## Use data transform across libraries
+
+As with the example of backbone above, cross-library calls can be simply achieved by adding custom_imports and prefix in the config:
+
+```python
+# Use custom_imports to register mmdet transforms to the registry
+custom_imports = dict(imports=['mmdet.datasets.transforms'], allow_failed_imports=False)
+
+# Add mmdet prefix to enable cross-library mechanism
+train_pipeline=[
+    dict(type='mmdet.LoadImageFromFile'),
+    dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
+    dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
+    dict(type='mmdet.Resize', scale=(1024, 2014), keep_ratio=True),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='mmdet.PackDetInputs')
+]
+```
+
+## Use detector across libraries
+
+Using an algorithm from another library is a little bit complex.
+
+An algorithm contains multiple submodules. Each submodule needs to add a prefix to its `type`. Take  using MMDetection's YOLOX in MMTracking as an example:
+
+```python
+# Use custom_imports to register mmdet models to the registry
+custom_imports = dict(imports=['mmdet.models'], allow_failed_imports=False)
+
+model = dict(
+    type='mmdet.YOLOX',
+    backbone=dict(type='mmdet.CSPDarknet', deepen_factor=1.33, widen_factor=1.25),
+    neck=dict(
+        type='mmdet.YOLOXPAFPN',
+        in_channels=[320, 640, 1280],
+        out_channels=320,
+        num_csp_blocks=4),
+    bbox_head=dict(
+        type='mmdet.YOLOXHead', num_classes=1, in_channels=320, feat_channels=320),
+    train_cfg=dict(assigner=dict(type='mmdet.SimOTAAssigner', center_radius=2.5)))
+```
+
+To prevent adding prefix to all of the submodules manually, the `_scope_` keyword is introduced. When the `_scope_` keyword is added to the config of a module, all submodules' scope will be changed by the `_scope_` keyword. Here is an example config:
+
+```python
+# Use custom_imports to register mmdet models to the registry
+custom_imports = dict(imports=['mmdet.models'], allow_failed_imports=False)
+
+model = dict(
+    _scope_='mmdet', # use the _scope_ keyword to avoid adding prefix to all submodules
+    type='YOLOX',
+    backbone=dict(type='CSPDarknet', deepen_factor=1.33, widen_factor=1.25),
+    neck=dict(
+        type='YOLOXPAFPN',
+        in_channels=[320, 640, 1280],
+        out_channels=320,
+        num_csp_blocks=4),
+    bbox_head=dict(
+        type='YOLOXHead', num_classes=1, in_channels=320, feat_channels=320),
+    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)))
+```
+
+These two examples are equivalent to each other.
+
+If you want to know more about the registry and config, please refer to [Config Tutorial](config.md) and [Registry Tutorial](registry.md)