Add SuperSimpleNorm and update synthetic env

lib/xlayers/super_norm.py

@@ -82,3 +82,43 @@ class SuperLayerNorm1D(SuperModule):
                 elementwise_affine=self._elementwise_affine,
             )
         )
+
+
+class SuperSimpleNorm(SuperModule):
+    """Super simple normalization."""
+
+    def __init__(self, mean, std, inplace=False) -> None:
+        super(SuperSimpleNorm, self).__init__()
+        self._mean = mean
+        self._std = std
+        self._inplace = inplace
+
+    @property
+    def abstract_search_space(self):
+        return spaces.VirtualNode(id(self))
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        # check inputs ->
+        return self.forward_raw(input)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        if not self._inplace:
+            tensor = input.clone()
+        else:
+            tensor = input
+        mean = torch.as_tensor(self._mean, dtype=tensor.dtype, device=tensor.device)
+        std = torch.as_tensor(self._std, dtype=tensor.dtype, device=tensor.device)
+        if (std == 0).any():
+            raise ValueError(
+                "std evaluated to zero after conversion to {}, leading to division by zero.".format(
+                    dtype
+                )
+            )
+        while mean.ndim < tensor.ndim:
+            mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
+        return tensor.sub_(mean).div_(std)
+
+    def extra_repr(self) -> str:
+        return "mean={mean}, std={mean}, inplace={inplace}".format(
+            mean=self._mean, std=self._std, inplace=self._inplace
+        )