class_docstr: Applies a caching according to a policy function
code: def forward(
      ctx,
      inp,
      all_expert_count,
      all_global_expert_count,
      local_expert_count,
      global_expert_count,
      fwd_expert_count,
      policy_fn,
      experts,
      batch_size,
      d_model,
      topk,
      num_expert,
      world_size,
      fused,  # for optimization
  ):
      if world_size > 1:
          stored_models = policy_fn(
              all_expert_count,
              all_global_expert_count,
              num_expert,
              world_size,
              d_model,
              fused,
          )
  
          # sent_models is the information of which models to send and to where according to the previous selection. stored_models will contain the info of where to fetch models from
          local_params, all_params, models = _generate_model_parameters(
              stored_models, experts, fused
          )
  
          if not fused:
              fmoe_cuda.model_exchange(
                  stored_models,
                  local_params,
                  all_params,
                  num_expert,
                  world_size,
                  fused,
              )
          else:
              stored_models = stored_models.any(dim=1)
  
              i = fmoe_cuda.model_exchange(
                  stored_models, local_params, all_params, 1, world_size
              )
  
              # add self node's experts to the list without copying
              models[i] = [experts]
  
              # Because we are fusing, all other experts will be sent together
              stored_models = stored_models.view(world_size, 1).repeat(
                  1, num_expert
              )
  
          _update_fetched_model_params(models, all_params)
  
          # now we need to include information about all the local experts that will run
          # num_expert * world_size tensor
          fwd_expert_count = fmoe_cuda.generate_cached_count(
              local_expert_count.cuda(),
              global_expert_count.cuda(),
              stored_models.cuda(),
              num_expert,
              world_size,
          ).cpu()
      else:
          raise NotImplementedError
  
      variables = (stored_models,)
      ctx.save_for_backward(*variables)
      ctx.models = models, i, fused, num_expert, world_size
      return (
          inp,
          models,
          stored_models,
          fwd_expert_count,
          int(fwd_expert_count.sum().item()),
      )
  
imports: import torch
  from torch.autograd import Function
  import fmoe_cuda
  from .utils import get_torch_default_comm
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from copy import deepcopy
label_desc: Use image node_id 1 for calling the MOECache obj's underlying member method code with example usage: obj.forward(ctx, inp, all_expert_count, all_global_expert_count, local_expert_count, global_expert_count, fwd_expert_count, policy_fn, experts, batch_size, d_model, topk, num_expert, world_size, fused) and returns: inp,  models,  stored_models,  fwd_expert_count,  int