code: def __init__(
      self,
      hidden_size: int,
      num_attention_heads: int,
      attention_dropout_prob: float,
      hidden_dropout_prob: float,
      dtype=None,
      checkpoint: bool = False,
      max_position_embeddings=1024,
  ):
      super().__init__()
      self.hidden_size = hidden_size
      self.attention_head_size = divide(
          hidden_size, num_attention_heads
      )
      self.num_attention_heads_per_partition = divide(
          num_attention_heads, gpc.tensor_parallel_size
      )
      self.hidden_size_per_partition = divide(
          hidden_size, gpc.tensor_parallel_size
      )
      self.checkpoint = checkpoint
      self.query_key_value = Linear1D_Col(
          hidden_size,
          3 * hidden_size,
          dtype=dtype,
      )
      self.attention_dropout = col_nn.Dropout(attention_dropout_prob)
      self.dense = Linear1D_Row(
          hidden_size,
          hidden_size,
          dtype=dtype,
          parallel_input=True,
      )
      self.dropout = col_nn.Dropout(hidden_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 GenericGPTSelfAttention1D object from inherited base classes: ParallelLayer with example: obj = GenericGPTSelfAttention1D(hidden_size, num_attention_heads, attention_dropout_prob, hidden_dropout_prob, dtype, checkpoint, max_position_embeddings)