class_docstr: Helper to learn a "zero" symbol, i.e., the first symbol to feed.
class_docstr_tok: ["Helper","to","learn","a","``","zero","''","symbol",",","i.e.",",","the","first","symbol","to","feed","."]
docstr: Learn a zero-th symbol of dim `hidden_dim` (ie channels).
  
  Args:
      hidden_dim: number of input channels / hidden dimension
docstr_tok: ["Learn","a","zero-th","symbol","of","dim","`","hidden_dim","`","(","ie","channels",")",".","Args",":","hidden_dim",":","number","of","input","channels","\/","hidden","dimension"]
code: def __init__(self, hidden_dim: int) -> None:
      """
      Learn a zero-th symbol of dim `hidden_dim` (ie channels).
  
      Args:
          hidden_dim: number of input channels / hidden dimension
      """
      super().__init__()
  
      def initializer(param):
          # for reporoducibility we fix the seeed
          generator = torch.Generator().manual_seed(42)
          return param.uniform_(-3.0, 3.0, generator=generator)
  
      self.sym = nn.parameter.Parameter(
          torch.empty(hidden_dim)
      )  # [hidden_dim]
      self.sym.data = initializer(self.sym.data)
  
imports: from functools import partial
  from typing import Callable, Optional
  import torch
  import torch.nn
  from torch import Tensor
  from .layers_utils import MLP, StochasticDepth, WindowMultiHeadAttention
label_desc: Use image node_id 1 to create a new StartSym object from inherited base classes: nn with example: obj = StartSym(hidden_dim)