Repository: pytorch
Issue: ExportedModule default print of graph signature is unreadable
Labels: oncall: pt2,oncall: export

### 🐛 Describe the bug

It looks like this:

```
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_pos_embed_height'), target='pos_embed_height',
 persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_pos_embed_width'), target='pos_embed_width', persistent=None), InputSpec(kind=
<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_attn_pool_queries'), target='attn_pool_queries', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=
TensorArgument(name='p_to_patch_embedding_0_gamma'), target='to_patch_embedding.0.gamma', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgume
nt(name='p_to_patch_embedding_1_weight'), target='to_patch_embedding.1.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p
_to_patch_embedding_1_bias'), target='to_patch_embedding.1.bias', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_to_patch_embe
dding_2_gamma'), target='to_patch_embedding.2.gamma', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_transformer_layers_0_0_no
rm_gamma'), target='transformer.layers.0.0.norm.gamma', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_transformer_layers_0_0_
q_norm_gamma'), target='transformer.layers.0.0.q_norm.gamma', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_transformer_layer
s_0_0_k_norm_gamma'), target='transformer.layers.0.0.k_norm.gamma', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_transformer
_layers_0_0_to_q_weight'), target='transformer.layers.0.0.to_q.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_transfo
rmer_layers_0_0_to_kv_weight'), target='transformer.layers.0.0.to_kv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_t
ransformer_layers_0_0_to_out_0_weight'), target='transformer.layers.0.0.to_out.0.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgume
nt(name='p_transformer_layers_0_1_0_gamma'), target='transformer.layers.0.1.0.gamma', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(n
ame='p_transformer_layers_0_1_1_weight'), target='transformer.layers.0.1.1.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(nam
e='p_transformer_layers_0_1_1_bias'), target='transformer.layers.0.1.1.bias', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_t
ransformer_layers_0_1_4_weight'), target='transformer.layers.0.1.4.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_tra
nsformer_layers_0_1_4_bias'), target='transformer.layers.0.1.4.bias', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_transform
er_layers_1_0_norm_gamma'), target='transformer.layers.1.0.norm.gamma', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_transfo
rmer_layers_1_0_q_norm_gamma'), target='transformer.layers.1.0.q_norm.gamma', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_t
ransformer_layers_1_0_k_norm_gamma'), target='transformer.layers.1.0.k_norm.gamma', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(nam
e='p_transformer_layers_1_0_to_q_weight'), target='transformer.layers.1.0.to_q.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>,
```

There's no way anyone is going to understand this. Remove it from the default print or design a properly indented / more compact representation for this.

As reference, I recently designed a compact format for automatic dynamic shapes decisions that I think is very readable:

```
  L['previous_seq_summarizations'][11]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][10]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][9]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][8]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][7]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][6]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][5]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][4]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][3]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][2]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][1]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['previous_seq_summarizations'][0]: tensor size=[128, 64, 192] stride=[S(1), S(2), 1]
  L['non_seq_embs']: tensor size=[128, 385, 192] stride=[S(1), S(2), 1]
```

### Versions

main

cc @chauhang @penguinwu @avikchaudhuri @gmagogsfm @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4