class_docstr: Distribution that shards model variables.
  
  Compare to `DataParallel` which replicates the variables across all devices,
  `ModelParallel` allows you to shard variables in addition to the input data.
  
  To construct a `ModelParallel` distribution, you need to provide a
  `DeviceMesh` and a `LayoutMap`.
  
  1. `DeviceMesh` contains physcial device information. The axis names in
      the mesh will be used to map the variable and data layout.
  2. `LayoutMap` contains the mapping between variable paths to their
      corresponding `TensorLayout`.
  
  Example:
  
  ```python
  devices = list_devices()    # Assume there are 8 devices.
  
  # Create a mesh with 2 devices for data parallelism and 4 devices for
  # model parallelism.
  device_mesh = DeviceMesh(shape=(2, 4), axis_names=('batch', 'model'),
                           devices=devices)
  # Create a layout map that shard the `Dense` layer and `Conv2D`
  # layer variables on the last dimension.
  # Based on the `device_mesh`, this means the variables
  # will be split across 4 devices. Any other variable that doesn't
  # match any key in the layout map will be fully replicated.
  layout_map = LayoutMap(device_mesh)
  layout_map['dense.*kernel'] = (None, 'model')
  layout_map['dense.*bias'] = ('model',)
  layout_map['conv2d.*kernel'] = (None, None, None, 'model')
  layout_map['conv2d.*bias'] = ('model',)
  
  distribution = ModelParallel(device_mesh=device_mesh,
                               layout_map=layout_map,
                               batch_dim_name='batch')
  # Set the global distribution, or via `with distribution.scope():`
  set_distribution(distribution)
  
  model = model_creation()
  model.compile()
  model.fit(data)
  ```
  
  You can quickly update the device mesh shape to change the sharding factor
  of the variables. E.g.
  ```
  # With only the shape change for the device mesh, the variables will be
  # sharded across 8 devices instead of 4, which further reduces the memory
  # footprint of variables on each of the device.
  device_mesh = DeviceMesh(shape=(1, 8), axis_names=('batch', 'model'),
                           devices=devices)
  ```
  
  To figure out a proper layout mapping rule for all the model variables, you
  can first list out all the model variable paths, which will be used as the
  key to map the variables to `TensorLayout`.
  
  e.g.
  ```
  model = create_model()
  for v in model.variables:
      print(v.path)
  ```
  
  Args:
      device_mesh: `DeviceMesh` instance for physical device and its
          logical mapping.
      layout_map: `LayoutMap` instance which map the variable path to the
          corresponding `TensorLayout`. The axis names of the
          `TensorLayout`s should match to the axis names in the
          device_mesh, or exception will be raised.
      batch_dim_name: optional string, the axis name in the `device_mesh`
          that will be used to distribute data. If unspecified, the
          first axis from the `device_mesh` will be used.
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code: def get_tensor_layout(self, path):
      return self._layout_map[path]
  
imports: import collections
  import contextlib
  import os
  import re
  import warnings
  import numpy
  from keras.api_export import keras_export
  from keras.backend import KerasTensor
  from keras.backend import distribution_lib
  from keras.backend.common import global_state
label_desc: Use image node_id 4 for calling the ModelParallel obj's underlying member method code with example usage: obj.get_tensor_layout(path) and returns: self