class_docstr: Initialize the DeepSpeed OPT Transformer Layer.
      
code: def forward(self, *args, **kwargs):
      input = args[0]
      input_mask = None
      # Allocate memory only on first layer forward
      if self.config.layer_id == 0 and self._alloc_workspace:
          self.allocate_workspace(
              self.config.hidden_size,
              self.config.heads,
              input.size()[1],
              input.size()[0],
              DeepSpeedTransformerInference.layer_id,
              self.config.mp_size,
              self.config.bigscience_bloom,
              dist.get_rank() if dist.is_initialized() else 0,
              self.config.max_out_tokens,
              self.config.min_out_tokens,
          )
          self._alloc_workspace = False
  
      get_present = True
  
      # We set the prev key/value to None when there is a prompt
      if input.shape[1] > 1:
          self.layer_past = None
      layer_past = self.layer_past
  
      input_type = input.dtype
  
      if (
          self.config.dtype
          in [torch.float16, torch.bfloat16, torch.int8]
      ) and input.dtype == torch.float:
          target_dtype = (
              torch.half if self.dtype == torch.int8 else self.dtype
          )
          input = input.to(target_dtype)
  
      with torch.no_grad():
          (
              attention_output,
              key,
              value,
              context_outputtn_ctx,
              inp_norm,
          ) = self.attention(
              input,
              input_mask,
              None,
              layer_past,
              get_present,
              None,
              None,
              None,
              self.norm_w,
              self.norm_b,
              None,
          )
          self.layer_past = (key, value)
          output = self.mlp(
              attention_output, input, inp_norm, self.attention.attn_ob
          )
  
          output = output.to(input_type)
      return output
  
imports: import torch
  from deepspeed import comm
  from deepspeed.model_implementations.transformers.ds_transformer import DeepSpeedTransformerInference
label_desc: Use image node_id 2 for calling the DeepSpeedLlama2Inference obj's underlying member method code with example usage: obj.forward() and returns: output