code: def parallel_self_attention(
      tensor_model_parallel_size,
      num_att_heads_per_partition,
      hidden_size_per_att_head,
      dropout_prob,
      batch_size,
      sequence_length,
  ):
      mpu.initialize_model_parallel(tensor_model_parallel_size)
      tensor_model_parallel_size = (
          mpu.get_tensor_model_parallel_world_size()
      )
  
      seed = 12345
      set_random_seed(seed)
  
      num_att_heads = (
          num_att_heads_per_partition
          * torch.distributed.get_world_size()
      )
      hidden_size = hidden_size_per_att_head * num_att_heads
  
      # Network
      identity_layer = IdentityLayer3D(
          batch_size, sequence_length, hidden_size
      ).cuda()
      attention_layer = mpu.BertParallelSelfAttention(
          hidden_size, num_att_heads, dropout_prob
      ).cuda()
      loss_weight = torch.randn(
          [batch_size, sequence_length, hidden_size]
      ).cuda()
      attention_mask = torch.randn(
          [batch_size, 1, 1, sequence_length]
      ).cuda()
      # Forward
      input_ = identity_layer()
      output = attention_layer(input_, attention_mask)
      loss = torch.mul(output, loss_weight).sum()
      # Backward
      loss.backward()
  
      rank = mpu.get_tensor_model_parallel_rank()
      mpu.destroy_model_parallel()
      return (
          rank,
          hidden_size,
          tensor_model_parallel_size,
          loss,
          attention_layer,
          identity_layer,
      )
  
imports: from mpu import layers
  from commons import set_random_seed
  from commons import print_separator
  from commons import initialize_distributed
  import mpu
  from torch.nn.parameter import Parameter
  import torch.nn.init
  import torch
  import random
  import sys
label_desc: Use image node_id 5 for calling a global function with example usage: parallel_self_attention(tensor_model_parallel_size, num_att_heads_per_partition, hidden_size_per_att_head, dropout_prob, batch_size, sequence_length) and returns: rank,  hidden_size,  tensor_model_parallel_size,  loss,  attention_layer,  identity_layer