code: def forward(
      self,
      input,
      input_mask,
      head_mask=None,
      layer_past=None,
      get_present=False,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      output_attentions=False,
      norm_w=None,
      norm_b=None,
      alibi=None,
  ):
      output = DeepSpeedSelfAttentionFunction.apply(
          input,
          input_mask,
          head_mask,
          layer_past,
          get_present,
          encoder_hidden_states,
          encoder_attention_mask,
          output_attentions,
          norm_w,
          norm_b,
          self.config,
          self.attn_qkvw,
          self.attn_qkvb,
          self.num_attention_heads_per_partition,
          self.norm_factor,
          self.hidden_size_per_partition,
          self.attn_ow,
          self.attn_ob,
          self.mp_group,
          self.q_scales,
          self.q_groups,
          self.merge_count,
          self.qkv_merging,
          self.score_context_func,
          alibi,
      )
  
      return output
  
file_path: H2O/h2o_flexgen/benchmark/third_party/DeepSpeed/deepspeed/ops/transformer/inference/ds_attention.py
imports: import math
  import torch
  from torch.autograd import Function
  from ...None import op_builder
  import torch.nn
  from deepspeed import comm
label_desc: Use image node_id 2 for calling the DeepSpeedSelfAttention obj's underlying member method code with example usage: obj.forward(input, input_mask, head_mask, layer_past, get_present, encoder_hidden_states, encoder_attention_mask, output_attentions, norm_w, norm_b, alibi) and returns: output