code: def __init__(
      self, layers_count, model_dim, heads_count, fc_dim, dropout_prob
  ):
      super(TransformerEncoder, self).__init__()
      self.model_dim = model_dim
      assert layers_count > 0
      self.encoder_layers = nn.ModuleList(
          [
              TransformerEncoderLayer(
                  model_dim, heads_count, fc_dim, dropout_prob
              )
              for _ in range(layers_count)
          ]
      )
  
imports: from collections import OrderedDict
  import os
  import numpy
  import torch
  from torch import nn
  from gluoncv.torch.utils.coot_utils import truncated_normal_fill
label_desc: Use image node_id 1 to create a new TransformerEncoder object from inherited base classes: nn with example: obj = TransformerEncoder(layers_count, model_dim, heads_count, fc_dim, dropout_prob)