class_docstr: A basic TinyViT layer for one stage.
  
  Args:
      dim (int): Number of input channels.
      input_resolution (tuple[int]): Input resolution.
      depth (int): Number of blocks.
      num_heads (int): Number of attention heads.
      window_size (int): Local window size.
      mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
      drop (float, optional): Dropout rate. Default: 0.0
      drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
      downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
      local_conv_size: the kernel size of the depthwise convolution between attention and MLP. Default: 3
      activation: the activation function. Default: nn.GELU
      out_dim: the output dimension of the layer. Default: dim
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code: def forward(self, x):
      for blk in self.blocks:
          x = blk(x)
      if self.downsample is not None:
          x = self.downsample(x)
      return x
  
imports: import itertools
  import paddle
  from paddle import nn
  from paddle.nn import functional
  from .common import LayerNorm2d
label_desc: Use image node_id 2 for calling the BasicLayer obj's underlying member method code with example usage: obj.forward(x) and returns: x