code: def forward(
      self,
      hidden_states,
      attention_mask,
      encoder_output=None,
      enc_dec_attn_mask=None,
      rotary_pos_emb=None,
      layer_past=None,
      get_key_value=False,
      set_inference_key_value_memory=False,
      inference_max_sequence_len=None,
      self_attention_relative_position_bias=None,
      cross_attention_relative_position_bias=None,
      checkpoint_core_attention=False,
  ):
      kwargs = locals()
      for key in ["self", "__class__"]:
          kwargs.pop(key)
      if self.dtype == torch.float32:
          return super().forward(**kwargs)
      with torch.autocast(device_type="cuda", dtype=self.dtype):
          return super().forward(**kwargs)
  
file_path: NeMo/nemo/collections/vision/modules/common/megatron/vision_transformer.py
imports: import torch
  from nemo.collections.nlp.modules.common.megatron.layer_type import LayerType
  from nemo.collections.nlp.modules.common.megatron.module import MegatronModule
  from nemo.collections.nlp.modules.common.megatron.transformer import ParallelTransformer, ParallelTransformerLayer_
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults
label_desc: Use image node_id 2 for calling the ParallelVisionTransformerLayer obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, encoder_output, enc_dec_attn_mask, rotary_pos_emb, layer_past, get_key_value, set_inference_key_value_memory, inference_max_sequence_len, _attention_relative_position_bias, cross_attention_relative_position_bias, checkpoint_core_attention) and returns: super, super