code: def __init__(
      self,
      in_features,
      hidden_features=None,
      out_features=None,
      activation=F.gelu,
      process_group: ProcessGroup = None,
      sequence_parallel=True,
      bias1=True,
      bias2=True,
      device=None,
      dtype=None,
  ):
      factory_kwargs = {"device": device, "dtype": dtype}
      super().__init__()
      assert (
          ColumnParallelLinear is not None
      ), "Need to install fused_dense"
      assert (
          RowParallelLinear is not None
      ), "Need to install fused_dense"
      out_features = (
          out_features if out_features is not None else in_features
      )
      hidden_features = (
          hidden_features
          if hidden_features is not None
          else in_features * 4
      )
      self.fc1 = ColumnParallelLinear(
          in_features,
          hidden_features,
          process_group,
          bias=bias1,
          sequence_parallel=sequence_parallel,
          **factory_kwargs,
      )
      self.activation = activation
      self.fc2 = RowParallelLinear(
          hidden_features,
          out_features,
          process_group,
          bias=bias2,
          sequence_parallel=sequence_parallel,
          **factory_kwargs,
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributed import ProcessGroup
label_desc: Use image node_id 1 to create a new ParallelMLP object from inherited base classes: nn with example: obj = ParallelMLP(in_features, hidden_features, out_features, activation, process_group, sequence_parallel, bias1, bias2, device, dtype)