code: def distribute_module(
      module: nn.Module,
      device_mesh: Optional[DeviceMesh] = None,
      partition_fn: Optional[
          Callable[[str, nn.Module, DeviceMesh], None]
      ] = None,
      input_fn: Optional[Callable[..., None]] = None,
      output_fn: Optional[Callable[..., None]] = None,
  ) -> nn.Module:
      """
      This function converts all module parameters to :class:`DTensor` parameters
      according to the `partition_fn` specified. It could also control the input or
      output of the module by specifying the `input_fn` and `output_fn`. (i.e. convert
      the input to :class:`DTensor`, convert the output back to torch.Tensor)
      Args:
          module (:class:`nn.Module`): user module to be partitioned.
          device_mesh (:class:`DeviceMesh`): the device mesh to place the module.
          partition_fn (Callable): the function to partition parameters (i.e. shard certain
              parameters across the `device_mesh`). If `partition_fn` is not specified,
              by default we replicate all module parameters of `module` across the mesh.
          input_fn (Callable): specify the input distribution, i.e. could control how the
              input of the module is sharded. `input_fn` will be installed as a module
              `forward_pre_hook` (pre forward hook).
          output_fn (Callable): specify the output distribution, i.e. could control how the
              output is sharded, or convert it back to torch.Tensor. output_fn will be
              installed as a module `forward_hook` (post forward hook).
  
      Returns:
          A module that contains parameters/buffers that are all `DTensor`s.
      """
  
      torch._C._log_api_usage_once("torch.dtensor.distribute_module")
  
      device_mesh = device_mesh or _mesh_resources.get_current_mesh()
  
      def replicate_module_params_buffers(
          m: nn.Module, mesh: DeviceMesh
      ) -> None:
          # This function loop over the immediate module parameters and
          # buffers, replicate all non DTensor params/buffers to DTensor
          # parameters/buffers, if they have not been partitioned in the
          # partition_fn, we can't easily use `module._apply` here
          # because we don't know what happened inside partition_fn as
          # user could do anything, i.e. install hooks, and we want to
          # preserve those.
          full_replicate = [Replicate()] * mesh.ndim
          for key, param in m._parameters.items():
              if param is not None and not isinstance(param, DTensor):
                  m.register_parameter(
                      key,
                      nn.Parameter(
                          distribute_tensor(
                              param.data, mesh, full_replicate
                          )
                      ),
                  )
          for key, buffer in m._buffers.items():
              if buffer is not None and not isinstance(buffer, DTensor):
                  m._buffers[key] = distribute_tensor(
                      buffer, mesh, full_replicate
                  )
  
      if partition_fn is None:
          # if partition_fn not specified, we by default replicate
          # all module params/buffers
          for name, submod in module.named_modules():
              replicate_module_params_buffers(submod, device_mesh)
      else:
          # apply partition_fun to submodules
          for name, submod in module.named_modules():
              partition_fn(name, submod, device_mesh)
              replicate_module_params_buffers(submod, device_mesh)
  
      # register input_fn as module forward pre hook
      if input_fn is not None:
          module.register_forward_pre_hook(lambda _, inputs: input_fn(inputs, device_mesh))  # type: ignore[misc]
      # register input_fn as module forward hook
      if output_fn is not None:
          module.register_forward_hook(
              lambda mod, inputs, outputs: output_fn(outputs, device_mesh)  # type: ignore[misc]
          )
  
      return module
  
imports: import warnings
  from typing import Callable, cast, Optional, Sequence, Tuple
  import torch
  import torch.distributed._functional_collectives
  import torch.distributed._tensor.dispatch
  import torch.distributed._tensor.random
  import torch.nn
  from torch.distributed._tensor._collective_utils import mesh_broadcast
  from torch.distributed._tensor._utils import compute_global_tensor_info
  from torch.distributed._tensor.placement_types import DTensorSpec, Placement, Replicate, Shard, TensorMeta
  from torch.distributed._tensor.random import is_rng_supported_mesh, OffsetBasedRNGTracker
  from torch.distributed._tensor.redistribute import Redistribute, redistribute_local_tensor
  from torch.distributed.device_mesh import _mesh_resources, DeviceMesh
label_desc: Use image node_id 2 for calling a global function with example usage: distribute_module(module, device_mesh, partition_fn, input_fn, output_fn) and returns: module