code: def get_model_config():
      return {
          "vocab_size": 10000,
          "ninp": 2048,  # embedding dimension
          "nhid": 2048,  # the dimension of the feedforward network model in nn.TransformerEncoder
          "nhead": 32,  # the number of heads in the multiheadattention models
          "dropout": 0,
          "initrange": 0.1,
          "scaler": GradScaler(),
          "clip_value": 0.05,
          "num_decoder_layers": 10,
          "seq_len": 32,
      }
  
imports: import torch.nn
  from fairscale.optim import GradScaler
label_desc: Use image node_id 1 for calling the FSDP obj's underlying member method code with example usage: obj.get_model_config() and returns: dict