class_docstr: Implementation of a model for inference composable with ragged batching.
class_docstr_tok: ["Implementation","of","a","model","for","inference","composable","with","ragged","batching","."]
docstr: Complete a forward pass of the model. This interface should be graphable, so it
  should not rely on the ability to use python control flow.
docstr_tok: ["Complete","a","forward","pass","of","the","model",".","This","interface","should","be","graphable",",","so","it","should","not","rely","on","the","ability","to","use","python","control","flow","."]
code: def forward(wrapped_batch: RaggedBatchWrapper) -> torch.Tensor:
      """
      Complete a forward pass of the model. This interface should be graphable, so it
      should not rely on the ability to use python control flow.
      """
      raise NotImplementedError()
  
imports: from abc import ABC, abstractmethod
  from typing import Iterable, Optional, Tuple, Type
  import torch
  import deepspeed.comm
  from ..ragged import DSStateManager, RaggedBatchWrapper
  from ..ragged.manager_configs import KVCacheConfig
  from ..ragged import DSSequenceDescriptor
  from ..model_implementations.layer_container_base import LayerContainer
  from ..config_v2 import RaggedInferenceEngineConfig
  from .flat_model_helpers import ModelMetadata
label_desc: Use image node_id 17 for calling the DSInferenceModelBase obj's underlying member method code with example usage: obj.forward(wrapped_batch) without return types