class_docstr: ### Embeddings for $t$
class_docstr_tok: ["#","#","#","Embeddings","for","$","t","$"]
docstr: * `n_channels` is the number of dimensions in the embedding
code: def __init__(self, n_channels: int):
      """
      * `n_channels` is the number of dimensions in the embedding
      """
      super().__init__()
      self.n_channels = n_channels
      # First linear layer
      self.lin1 = nn.Linear(self.n_channels // 4, self.n_channels)
      # Activation
      self.act = Swish()
      # Second linear layer
      self.lin2 = nn.Linear(self.n_channels, self.n_channels)
  
imports: import math
  from typing import Optional, Tuple, Union, List
  import torch
  from torch import nn
  from labml_helpers.module import Module
label_desc: Use image node_id 1 to create a new TimeEmbedding object from inherited base classes: nn with example: obj = TimeEmbedding(n_channels)