code: def parse_linear_layer(
      operation,
      layer_name,
      input_names,
      input_shapes,
      node,
      class_object,
      data_reader,
      config,
  ):
      assert "Linear" in operation
  
      layer = {}
  
      layer["class_name"] = "Dense"
      layer["name"] = layer_name
      layer["inputs"] = input_names
  
      layer["weight_data"] = class_object.weight.data.numpy()
      if class_object.bias is not None:
          layer["bias_data"] = class_object.bias.data.numpy()
      else:
          layer["bias_data"] = None
  
      if class_object is not None:
          layer["n_in"] = class_object.in_features
          layer["n_out"] = class_object.out_features
      else:
          raise Exception(
              "parsing of torch.nn.functional.linear not supported yet, please use torch.nn.Linear class"
          )
  
      # Handling whether bias is used or not
      if class_object.bias is None:
          layer["use_bias"] = False
      else:
          layer["use_bias"] = True
  
      output_shape = input_shapes[0][:]
      output_shape[-1] = layer["n_out"]
  
      return layer, output_shape
  
label_desc: Use image node_id 1 for calling a global function with example usage: parse_linear_layer(operation, layer_name, input_names, input_shapes, node, class_object, data_reader, config) and returns: layer,  output_shape