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: Set the model parameters for the embedding, transformer, and unembedding containers.
docstr_tok: ["Set","the","model","parameters","for","the","embedding",",","transformer",",","and","unembedding","containers","."]
code: def set_parameters(
      self,
      transformer: Iterable[LayerContainer],
      non_transformer: LayerContainer,
      flattened_param_buffer: torch.Tensor,
      flattened_param_metadata: ModelMetadata,
  ):
      """
      Set the model parameters for the embedding, transformer, and unembedding containers.
      """
      self._transformer = transformer
      self._non_transformer = non_transformer
      self._flattened_param_buffer = flattened_param_buffer
      self._flattened_param_metadata = flattened_param_metadata
  
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 3 for calling the DSInferenceModelBase obj's underlying member method code with example usage: obj.set_parameters(transformer, non_transformer, flattened_param_buffer, flattened_param_metadata) without return types