docstr: Generate the representation given the inputs.
  
  This is used in training or fine-tuning a bert model.
  
  Parameters
  ----------
  F
  data
      - layout = 'NT'
          Shape (batch_size, seq_length, C)
      - layout = 'TN'
          Shape (seq_length, batch_size, C)
  valid_length
      Shape (batch_size,)
  
  Returns
  -------
  out
      - layout = 'NT'
          Shape (batch_size, seq_length, C_out)
      - layout = 'TN'
          Shape (seq_length, batch_size, C_out)
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code: def forward(self, data, valid_length):
      """
      Generate the representation given the inputs.
  
      This is used in training or fine-tuning a bert model.
  
      Parameters
      ----------
      F
      data
          - layout = 'NT'
              Shape (batch_size, seq_length, C)
          - layout = 'TN'
              Shape (seq_length, batch_size, C)
      valid_length
          Shape (batch_size,)
  
      Returns
      -------
      out
          - layout = 'NT'
              Shape (batch_size, seq_length, C_out)
          - layout = 'TN'
              Shape (seq_length, batch_size, C_out)
      """
      if self.layout == "NT":
          time_axis, batch_axis = 1, 0
      else:
          time_axis, batch_axis = 0, 1
      # 1. Embed the data
      attn_mask = gen_self_attn_mask(
          data, valid_length, attn_type="full", layout=self.layout
      )
      out = data
      all_encodings_outputs = []
      additional_outputs = []
      for layer_idx in range(self._num_layers):
          layer = self.all_layers[layer_idx]
          out, attention_weights = layer(out, attn_mask)
          # out : [batch_size, seq_len, units] or [seq_len, batch_size, units]
          # attention_weights : [batch_size, num_heads, seq_len, seq_len]
          if self._output_all_encodings:
              out = sequence_mask(
                  out, valid_len=valid_length, axis=time_axis
              )
              all_encodings_outputs.append(out)
  
          if self._output_attention:
              additional_outputs.append(attention_weights)
  
      if not self._output_all_encodings:
          # if self._output_all_encodings, SequenceMask is already applied above
          out = sequence_mask(
              out, valid_len=valid_length, axis=time_axis
          )
          return out, additional_outputs
      else:
          return all_encodings_outputs, additional_outputs
  
imports: import logging
  import torch
  from ..attention_cell import gen_self_attn_mask
  from .transformer import TransformerEncoderLayer
  from ..layers import get_activation, sequence_mask
  from ...utils.registry import Registry
  from ...models.bert import bert_cfg_reg
label_desc: Use image node_id 3 for calling the BertTransformer obj's underlying member method code with example usage: obj.forward(data, valid_length) and returns: out,  additional_outputs, all_encodings_outputs,  additional_outputs