code: def mesh_split(
      tensor,
      device_mesh,
      tensor_split_dims_mapping,
      use_sharding_op=False,
      manual_mesh_dims=None,
      unspecified_dims=None,
  ):
      """Returns a tensor that is split along multiple dimensions in a device mesh.
  
      Args:
        tensor: A tf.Tensor to split.
        device_mesh: An np.ndarray describing the topology of the device mesh and
          each element is the ID of the device in the topology.
        tensor_split_dims_mapping: A list of integers that map each tensor axis to
          the device mesh axis along which it is sharded. Its length is the tensor
          rank, and tensor_split_dims_mapping[i] is device mesh axis for tensor
          dimension i. Use -1 for tensor dimensions that are not sharded.
        use_sharding_op: If true, adds a sharding op to set the sharding.
        manual_mesh_dims: An optional list of mesh dims for manual subgroups.
        unspecified_dims: An optional list of dimensions unspecified.
  
      Raises:
        ValueError: The number of tensor split dimensions is larger than device mesh
          rank.
      """
      sharding = mesh_split_sharding(
          device_mesh, tensor_split_dims_mapping, manual_mesh_dims
      )
      return sharding.apply_to_tensor(
          tensor,
          use_sharding_op=use_sharding_op,
          unspecified_dims=unspecified_dims or [],
      )
  
imports: import numpy
  from tensorflow.compiler.tf2xla.python import xla
  from local_xla.xla import xla_data_pb2
  from tensorflow.core.framework import attr_value_pb2
label_desc: Use image node_id 13 for calling a global function with example usage: mesh_split(tensor, device_mesh, tensor_split_dims_mapping, use_sharding_op, manual_mesh_dims, unspecified_dims) and returns: sharding