class_docstr: ## Zero3 Layer
  
  Each layer of the model (or a combination of a few consecutive layers) should be wrapped in
  this module.
class_docstr_tok: ["#","#","Zero3","Layer","Each","layer","of","the","model","(","or","a","combination","of","a","few","consecutive","layers",")","should","be","wrapped","in","this","module","."]
docstr: #### Merge all the parameters and pad it so that it's divisible by `world_size`.
docstr_tok: ["#","#","#","#","Merge","all","the","parameters","and","pad","it","so","that","it","'s","divisible","by","`","world_size","`","."]
code: def _merge_and_pad_params(
      self, params: List[nn.Parameter]
  ) -> torch.Tensor:
      """
      #### Merge all the parameters and pad it so that it's divisible by `world_size`.
      """
      # Total number of parameters
      size = sum(p.shape.numel() for p in params)
  
      # If it is not divisible by `world_size`, pad it
      if size % self.world_size != 0:
          padding_fixed = self.world_size - (size % self.world_size)
      # Otherwise, no need to pad
      else:
          padding_fixed = 0
      # Create an empty padding tensor
      padding = self._empty((padding_fixed,))
      # Concatenate all the parameters and pad it
      return torch.cat([p.view(-1) for p in params] + [padding], dim=0)
  
imports: import functools
  from typing import List, Optional, Tuple
  import torch
  import torch.distributed
  from torch import nn
label_desc: Use image node_id 2 for calling the Zero3Layer obj's underlying member method code with example usage: obj._merge_and_pad_params(params) and returns: torch