class_docstr: Configuration class to store `Fixed` sparsity configuration.
  For more details about this sparsity config, please see `Generative Modeling with
  Sparse Transformers`: https://arxiv.org/abs/1904.10509; this has been customized.
  This class extends parent class of `SparsityConfig` and customizes it for `Fixed` sparsity.
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docstr: Generates `Fixed` sparsity layout used by each head in the sparse attention.
  Arguments:
       seq_len: required: an integer determining number of attention heads of the layer.
  Return:
       layout: a tensor of dimension (num_heads, num_blocks, num_blocks) containing `Fixed`
          sparsity layout of all head
docstr_tok: ["Generates","`","Fixed","`","sparsity","layout","used","by","each","head","in","the","sparse","attention",".","Arguments",":","seq_len",":","required",":","an","integer","determining","number","of","attention","heads","of","the","layer",".","Return",":","layout",":","a","tensor","of","dimension","(","num_heads",",","num_blocks",",","num_blocks",")","containing","`","Fixed","`","sparsity","layout","of","all","head"]
code: def make_layout(self, seq_len):
      """Generates `Fixed` sparsity layout used by each head in the sparse attention.
      Arguments:
           seq_len: required: an integer determining number of attention heads of the layer.
      Return:
           layout: a tensor of dimension (num_heads, num_blocks, num_blocks) containing `Fixed`
              sparsity layout of all head
      """
  
      layout = self.setup_layout(seq_len)
      for h in range(0, self.num_layout_heads):
          layout = self.set_local_layout(h, layout)
          layout = self.set_global_layout(h, layout)
  
      layout = self.check_and_propagate_first_head_layout(layout)
      return layout
  
imports: import random
  import torch
label_desc: Use image node_id 4 for calling the FixedSparsityConfig obj's underlying member method code with example usage: obj.make_layout(seq_len) and returns: layout