class_docstr: Abstract Configuration class to store `sparsity configuration of a self attention layer`.
  It contains shared property of different block-sparse sparsity patterns. However, each class needs to extend it based on required property and functionality.
docstr: If all heads require same sparsity layout, it propagate first head layout to all heads
  
  Arguments:
       layout: required: a tensor of dimension (num_heads, num_blocks, num_blocks) containing sparsity layout of all head; may not be completely set at this step
  
  Return:
       layout: a tensor of dimension (num_heads, num_blocks, num_blocks) containing sparsity layout of all head
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code: def check_and_propagate_first_head_layout(self, layout):
      """If all heads require same sparsity layout, it propagate first head layout to all heads
  
      Arguments:
           layout: required: a tensor of dimension (num_heads, num_blocks, num_blocks) containing sparsity layout of all head; may not be completely set at this step
  
      Return:
           layout: a tensor of dimension (num_heads, num_blocks, num_blocks) containing sparsity layout of all head
      """
  
      if not self.different_layout_per_head:
          layout[1 : self.num_heads, :, :] = layout[0, :, :]
      return layout
  
imports: import torch
  import random
label_desc: Use image node_id 3 for calling the SparsityConfig obj's underlying member method code with example usage: obj.check_and_propagate_first_head_layout(layout) and returns: layout