code: def __init__(
      self,
      emb_size,
      num_radial,
      bessel_funcs,
      cutoff,
      envelope_exponent,
      num_atom_types=95,
      activation=None,
  ):
      super(EmbeddingBlock, self).__init__()
  
      self.bessel_funcs = bessel_funcs
      self.cutoff = cutoff
      self.activation = activation
      self.envelope = Envelope(envelope_exponent)
      self.embedding = nn.Embedding(num_atom_types, emb_size)
      self.dense_rbf = nn.Linear(num_radial, emb_size)
      self.dense = nn.Linear(emb_size * 3, emb_size)
      self.reset_params()
  
file_path: dgl/examples/pytorch/dimenet/modules/embedding_block.py
imports: import numpy
  import torch
  import torch.nn
  from modules.envelope import Envelope
  from modules.initializers import GlorotOrthogonal
label_desc: Use image node_id 1 to create a new EmbeddingBlock object from inherited base classes: nn with example: obj = EmbeddingBlock(emb_size, num_radial, bessel_funcs, cutoff, envelope_exponent, num_atom_types, activation)