code: def pre_scale(self, x: torch.Tensor) -> torch.Tensor:
      with record_function("feature_map::pre_scale"):
          # Re-draw counting logic
          if (
              (
                  self.iter_before_redraw is not None
                  and self._iter_counter > self.iter_before_redraw
              )
              or self.features is None
              or self.features.device != x.device
          ):
              # The feature map is actually using half the dimension, we'll concatenate + and - features
              self._iter_counter = 1
              self.features = self._get_feature_map(
                  x.shape[-1], self.dim_feature_map, x.device
              )
  
          features = self.features
          assert features is not None
  
          if features.dtype != x.dtype:
              self.features = features.to(x.dtype)
  
          self._iter_counter += 1
  
          # Normalization / softmax
          if self.softmax_temp < 0:
              # A = exp(QK.t/√d), so each input will be scaled by √√d
              self.softmax_temp = x.shape[-1] ** -0.25
  
          x_scaled = x * self.softmax_temp
  
          # Compute the scaling factors in logspace, applied from within the exponential
          # - dimnish possible exponential overflow
          # - remove a multiply across the batch, replace by an addition
          norm_x_2 = torch.einsum(
              "...d,...d->...", x_scaled, x_scaled
          ).unsqueeze(-1)
          self.offset = -0.5 * norm_x_2 - self.h_scale + self.epsilon
  
          if self.normalize_inputs:
              # L0 normalize the exponential term, can be useful for numerical stability
              # This ensures that features +- offset is below 1
              self.offset -= norm_x_2.max(1, keepdim=True)[0]
  
      # Return the scaled inputs, the rest depends on the kernel being used
      return x_scaled
  
imports: import math
  from enum import Enum, auto
  from typing import Optional
  import torch
  from torch.autograd.profiler import record_function
  from .base import FeatureMap
label_desc: Use image node_id 2 for calling the SoftMaxPositiveEstimators obj's underlying member method code with example usage: obj.pre_scale(x) and returns: x_scaled