class_docstr: An aggregation operator that returns the feature-wise median of a set.
  
  That is, for every feature :math:`d`, it computes
  
  .. math::
      {\mathrm{median}(\mathcal{X})}_d = x_{\pi_i,d}
  
  where :math:`x_{\pi_1,d} \le x_{\pi_2,d} \le \dots \le
  x_{\pi_n,d}` and :math:`i = \lfloor \frac{n}{2} \rfloor`.
  
  .. note::
      If the median lies between two values, the lowest one is returned.
      To compute the midpoint (or other kind of interpolation) of the two
      values, use :class:`QuantileAggregation` instead.
  
  Args:
      fill_value (float, optional): The default value in the case no entry is
          found for a given index (default: :obj:`0.0`).
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code: def __init__(self, fill_value: float = 0.0):
      super().__init__(0.5, "lower", fill_value)
  
file_path: pytorch_geometric/torch_geometric/nn/aggr/quantile.py
imports: from typing import List, Optional, Union
  import torch
  from torch import Tensor
  from torch_geometric.nn.aggr import Aggregation
  from torch_geometric.utils import cumsum
label_desc: Use image node_id 1 to create a new MedianAggregation object from inherited base classes: QuantileAggregation with example: obj = MedianAggregation(fill_value)