class_docstr: AnyNet model. See :paper:`dds`.
class_docstr_tok: ["AnyNet","model",".","See",":","paper",":","`","dds","`","."]
docstr: Args:
      x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
  
  Returns:
      dict[str->Tensor]: names and the corresponding features
code: def forward(self, x):
      """
      Args:
          x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
  
      Returns:
          dict[str->Tensor]: names and the corresponding features
      """
      assert (
          x.dim() == 4
      ), f"Model takes an input of shape (N, C, H, W). Got {x.shape} instead!"
      outputs = {}
      x = self.stem(x)
      if "stem" in self._out_features:
          outputs["stem"] = x
      for stage, name in self.stages_and_names:
          x = stage(x)
          if name in self._out_features:
              outputs[name] = x
      return outputs
  
imports: import numpy
  from torch import nn
  from detectron2.layers import CNNBlockBase, ShapeSpec, get_norm
  from .backbone import Backbone
label_desc: Use image node_id 2 for calling the AnyNet obj's underlying member method code with example usage: obj.forward(x) and returns: outputs