class_docstr: Compute "energy" concentration level for a tensor
  
  Args:
      dim (int):
          The dimension to measure.
      top_k_percents (List[float]):
          List of percentage values. For each value, the `measure`
          function will compute and return the percentage of "energy"
          concentrated on that top-K percent of values in the dimension
          to measure. Note, this is the opposite of the sparsity percentage.
code: def __init__(self, dim: int, top_k_percents: List[float]) -> None:
      assert isinstance(dim, int)
      self.dim = dim
      self.percents = []
      last_p = 0.0
      for p in top_k_percents:
          assert isinstance(p, (int, float))
          assert p > 0, p
          assert p <= 100, p
          assert (
              p > last_p
          ), f"p {p} should be larger than last_p {last_p}"
          self.percents.append(float(p))
          last_p = p
  
imports: from typing import List
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 to create a new EnergyConcentrationProfile object  with example: obj = EnergyConcentrationProfile(dim, top_k_percents)