code: def __init__(
      self,
      dim_features: int,
      iter_before_redraw: Optional[int],
      normalize_inputs: bool = False,
      epsilon: float = 1e-6,
      softmax_temp: float = -1,
  ):
      super().__init__(
          dim_features, iter_before_redraw, normalize_inputs, epsilon
      )
      self.softmax_temp = softmax_temp
  
      # Handle the scaling from all kernels by √m.
      # This normalizes for all the feature maps involved
      self.h_scale = math.log(math.sqrt(self.dim_features))
  
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 1 to create a new SoftMaxPositiveEstimators object from inherited base classes: FeatureMap with example: obj = SoftMaxPositiveEstimators(dim_features, iter_before_redraw, normalize_inputs, epsilon, softmax_temp)