code: def _build_embedding(self, dim, max_steps):
      steps = torch.arange(max_steps).unsqueeze(1)  # [T,1]
      dims = torch.arange(dim).unsqueeze(0)  # [1,dim]
      table = steps * 10.0 ** (dims * 4.0 / dim)  # [T,dim]
      table = torch.cat([torch.sin(table), torch.cos(table)], dim=1)
      return table
  
imports: import math
  import torch
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the DiffusionEmbedding obj's underlying member method code with example usage: obj._build_embedding(dim, max_steps) and returns: table