class_docstr: Configuration class to store `Dense` configuration.
  In reality, this is not sparse and all blocks are used. We keep it for the sake of comparison and comprehension.
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docstr: Set 1 to all blocks of the layout meaning the pattern is dense; not sparse.
  
  Arguments:
       seq_len: required: an integer determining the underling sequence length; must be <= max sequence length
  
  Return:
       layout: a tensor of dimension (num_heads, num_blocks, num_blocks) containing sparsity layout of all head; for dense everything is 1
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code: def make_layout(self, seq_len):
      """Set 1 to all blocks of the layout meaning the pattern is dense; not sparse.
  
      Arguments:
           seq_len: required: an integer determining the underling sequence length; must be <= max sequence length
  
      Return:
           layout: a tensor of dimension (num_heads, num_blocks, num_blocks) containing sparsity layout of all head; for dense everything is 1
      """
  
      layout = self.setup_layout(seq_len)
      layout[:, :, :] = 1
      return layout
  
imports: import torch
  import random
label_desc: Use image node_id 2 for calling the DenseSparsityConfig obj's underlying member method code with example usage: obj.make_layout(seq_len) and returns: layout