class_docstr: A basic focal modulation layer for one stage.
  Args:
      dim (int): Number of feature channels
      depth (int): Depths of this stage.
      mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
      drop (float, optional): Dropout rate. Default: 0.0
      drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
      norm_layer (nn.Layer, optional): Normalization layer. Default: nn.LayerNorm
      downsample (nn.Layer | None, optional): Downsample layer at the end of the layer. Default: None
      focal_level (int): Number of focal levels
      focal_window (int): Focal window size at focal level 1
      use_conv_embed (bool): Whether use overlapped convolution for patch embedding
      use_layerscale (bool): Whether use layerscale proposed in CaiT. Default: False 
      layerscale_value (float): Value of layerscale
      use_postln (bool): Whether use layernorm after modulation. Default: False.
      use_postln_in_modulation (bool): Whether use post-modulation layernorm. Default: False.
      normalize_modulator (bool): Whether use normalize in modulator
      use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
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docstr: Args:
      x: Input feature, tensor size (B, H*W, C).
docstr_tok: ["Args",":","x",":","Input","feature",",","tensor","size","(","B",",","H","*","W",",","C",")","."]
code: def forward(self, x, H, W):
      """
      Args:
          x: Input feature, tensor size (B, H*W, C).
      """
      for blk in self.blocks:
          blk.H, blk.W = H, W
          x = blk(x)
  
      if self.downsample is not None:
          x_reshaped = x.transpose([0, 2, 1]).reshape(
              [x.shape[0], x.shape[-1], H, W]
          )
          x_down = self.downsample(x_reshaped)
          x_down = x_down.flatten(2).transpose([0, 2, 1])
          Wh, Ww = (H + 1) // 2, (W + 1) // 2
          return x, H, W, x_down, Wh, Ww
      else:
          return x, H, W, x, H, W
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.core.workspace import register, serializable
  from .transformer_utils import DropPath, Identity
  from .transformer_utils import add_parameter, to_2tuple
  from .transformer_utils import ones_, zeros_, trunc_normal_
  from .swin_transformer import Mlp
label_desc: Use image node_id 2 for calling the BasicLayer obj's underlying member method code with example usage: obj.forward(x, H, W) and returns: x,  H,  W,  x_down,  Wh,  Ww, x,  H,  W,  x,  H,  W