code: def __init__(
      self,
      word_vocab,
      embedding_styles,
      input_size,
      hidden_size,
      output_size,
      gl_type,
      gl_metric_type="weighted_cosine",
      gl_num_heads=1,
      gl_top_k=None,
      gl_epsilon=None,
      gl_smoothness_ratio=None,
      gl_connectivity_ratio=None,
      gl_sparsity_ratio=None,
      gl_input_size=None,
      gl_hidden_size=None,
      init_adj_alpha=None,
      feat_drop=0,
      layer_drop=0.5,
      activation=F.relu,
      device=None,
  ):
      super(DynamicGNNClassifier, self).__init__()
      self.gl_type = gl_type
      if self.gl_type == "node_emb":
          self.graph_learner = NodeEmbeddingBasedGraphConstruction(
              word_vocab,
              embedding_styles,
              sim_metric_type=gl_metric_type,
              num_heads=gl_num_heads,
              top_k_neigh=gl_top_k,
              epsilon_neigh=gl_epsilon,
              smoothness_ratio=gl_smoothness_ratio,
              connectivity_ratio=gl_connectivity_ratio,
              sparsity_ratio=gl_sparsity_ratio,
              input_size=gl_input_size,
              hidden_size=gl_hidden_size,
              fix_word_emb=True,
              dropout=None,
              device=device,
          )
  
      elif self.gl_type == "node_edge_emb":
          raise NotImplementedError()
      elif self.gl_type == "node_emb_refined":
          self.graph_learner = (
              NodeEmbeddingBasedRefinedGraphConstruction(
                  word_vocab,
                  embedding_styles,
                  init_adj_alpha,
                  sim_metric_type=gl_metric_type,
                  num_heads=gl_num_heads,
                  top_k_neigh=gl_top_k,
                  epsilon_neigh=gl_epsilon,
                  smoothness_ratio=gl_smoothness_ratio,
                  connectivity_ratio=gl_connectivity_ratio,
                  sparsity_ratio=gl_sparsity_ratio,
                  input_size=gl_input_size,
                  hidden_size=gl_hidden_size,
                  fix_word_emb=True,
                  dropout=None,
                  device=device,
              )
          )
      else:
          raise RuntimeError("Unknown gl_type: {}".format(self.gl_type))
  
      self.gnn_clf = GNNClassifier(
          input_size,
          hidden_size,
          output_size,
          feat_drop=feat_drop,
          layer_drop=layer_drop,
          activation=activation,
      )
  
imports: import argparse
  import os
  import time
  import dgl
  import dgl.function
  import networkx
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.nn.functional
  from dgl import DGLGraph
  from dgl.data import load_data, register_data_args
  from scipy import sparse
  from ...modules.graph_construction import NodeEmbeddingBasedGraphConstruction, NodeEmbeddingBasedRefinedGraphConstruction
  from ...modules.utils.vocab_utils import VocabModel
  from ..utils import EarlyStopping
label_desc: Use image node_id 1 to create a new DynamicGNNClassifier object from inherited base classes: nn with example: obj = DynamicGNNClassifier(word_vocab, embedding_styles, input_size, hidden_size, output_size, gl_type, gl_metric_type, gl_num_heads, gl_top_k, gl_epsilon, gl_smoothness_ratio, gl_connectivity_ratio, gl_sparsity_ratio, gl_input_size, gl_hidden_size, init_adj_alpha, feat_drop, layer_drop, activation, device)