code: def initialize(self, input, eps=1e-6):
      # data-aware initializer
      assert len(input.shape) == 2
      if input.shape[0] < 1000:
          warn(
              "Data-aware initialization is performed on less than 1000 data points. This may cause instability."
              "To avoid potential problems, run this model on a data batch with at least 1000 data samples."
              "You can do so manually before training. Use with torch.no_grad() for memory efficiency."
          )
      with torch.no_grad():
          feature_selectors = self.choice_function(
              self.feature_selection_logits, dim=0
          )
          # ^--[in_features, num_trees, depth]
  
          feature_values = torch.einsum(
              "bi,ind->bnd", input, feature_selectors
          )
          # ^--[batch_size, num_trees, depth]
  
          # initialize thresholds: sample random percentiles of data
          percentiles_q = 100 * np.random.beta(
              self.threshold_init_beta,
              self.threshold_init_beta,
              size=[self.num_trees, self.depth],
          )
          self.feature_thresholds.data[...] = torch.as_tensor(
              list(
                  map(
                      np.percentile,
                      check_numpy(feature_values.flatten(1, 2).t()),
                      percentiles_q.flatten(),
                  )
              ),
              dtype=feature_values.dtype,
              device=feature_values.device,
          ).view(self.num_trees, self.depth)
  
          # init temperatures: make sure enough data points are in the linear region of sparse-sigmoid
          temperatures = np.percentile(
              check_numpy(
                  abs(feature_values - self.feature_thresholds)
              ),
              q=100 * min(1.0, self.threshold_init_cutoff),
              axis=0,
          )
  
          # if threshold_init_cutoff > 1, scale everything down by it
          temperatures /= max(1.0, self.threshold_init_cutoff)
          self.log_temperatures.data[...] = torch.log(
              torch.as_tensor(temperatures) + eps
          )
  
imports: import random
  from typing import Callable
  from warnings import warn
  import numpy
  import torch
  import torch.nn
  from pytorch_tabular.utils import check_numpy
  from ..layers import ModuleWithInit
  from .activations import RSoftmax, entmax15, sparsemax, sparsemoid
label_desc: Use image node_id 3 for calling the ODST obj's underlying member method code with example usage: obj.initialize(input, eps) without return types