code: def __init__(
      self,
      in_features: int,
      mlp_ratio: int,
      act_func: str = "gelu",
      dropout_prob: float = 0.0,
      dtype=None,
      checkpoint: bool = False,
      skip_bias_add: bool = False,
  ):
      super().__init__()
  
      self.in_features = in_features
      self.mlp_ratio = mlp_ratio
      self.checkpoint = checkpoint
      self.skip_bias_add = skip_bias_add
  
      self.act = ACT2FN[act_func]
      skip_dense_1_add_bias = False
  
      # Project to mlp_ratio * h.
      self.dense_1 = Linear1D_Col(
          self.in_features,
          int(self.mlp_ratio * self.in_features),
          dtype=dtype,
          gather_output=False,
          skip_bias_add=skip_dense_1_add_bias,
      )
  
      # Project back to h.
      self.dense_2 = Linear1D_Row(
          int(self.mlp_ratio * self.in_features),
          self.in_features,
          dtype=dtype,
          parallel_input=True,
      )
  
      self.dropout = col_nn.Dropout(dropout_prob)
  
imports: import math
  import torch
  from torch import Tensor
  from torch import nn
  from colossalai import kernel
  from colossalai import nn
  from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType
  from colossalai.legacy.core import global_context
  from colossalai.legacy.nn.layer import Linear1D_Col, Linear1D_Row
  from colossalai.legacy.nn.layer.base_layer import ParallelLayer
  from colossalai.legacy.nn.layer.utils import ACT2FN, divide
  from colossalai.legacy.utils.activation_checkpoint import checkpoint
  from colossalai.utils import checkpoint
label_desc: Use image node_id 1 to create a new GPTMLP1D object from inherited base classes: ParallelLayer with example: obj = GPTMLP1D(in_features, mlp_ratio, act_func, dropout_prob, dtype, checkpoint, skip_bias_add)