code: def aggregate_var(h, **kwargs):
      """
      Compute the variance of the input tensor along the second to last dimension.
  
      Parameters
      ----------
      h : torch.Tensor
          Input tensor.
  
      Returns
      -------
      torch.Tensor
          Variance of the input tensor along the second to last dimension.
      """
      h_mean_squares = torch.mean(h * h, dim=-2)
      h_mean = torch.mean(h, dim=-2)
      var = torch.relu(h_mean_squares - h_mean * h_mean)
      return var
  
file_path: deepchem/deepchem/utils/graph_utils.py
imports: import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: aggregate_var(h) and returns: var