code: def linear_to_conv2d_map(
      state_dict,
      prefix,
      local_metadata,
      strict,
      missing_keys,
      unexpected_keys,
      error_msgs,
  ):
      """Unsqueeze twice to map nn.Linear weights to nn.Conv2d weights"""
      for k in state_dict:
          is_internal_proj = all(
              substr in k for substr in ["lin", ".weight"]
          )
          is_output_proj = all(
              substr in k for substr in ["classifier", ".weight"]
          )
          if is_internal_proj or is_output_proj:
              if len(state_dict[k].shape) == 2:
                  state_dict[k] = state_dict[k][:, :, None, None]
  
imports: from ane_transformers.reference.layer_norm import LayerNormANE
  import torch
  import torch.nn
  from transformers.models.distilbert import modeling_distilbert
label_desc: Use image node_id 2 for calling a global function with example usage: linear_to_conv2d_map(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) without return types