code: def __init__(
      self,
      dim,
      n_heads,
      d_head,
      dropout=0.0,
      context_dim=None,
      gated_ff=True,
      checkpoint=True,
  ):
      super().__init__()
      self.attn1 = CrossAttention(
          query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
      )  # is a self-attention
      self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
      self.attn2 = CrossAttention(
          query_dim=dim,
          context_dim=context_dim,
          heads=n_heads,
          dim_head=d_head,
          dropout=dropout,
      )  # is self-attn if context is none
      self.norm1 = nn.LayerNorm(dim)
      self.norm2 = nn.LayerNorm(dim)
      self.norm3 = nn.LayerNorm(dim)
      self.checkpoint = checkpoint
  
imports: import math
  from inspect import isfunction
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new BasicTransformerBlock object from inherited base classes: nn with example: obj = BasicTransformerBlock(dim, n_heads, d_head, dropout, context_dim, gated_ff, checkpoint)