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.
docstr: Initialize the Dense Sparsity Pattern Config.
  In reality, this is not sparse and all blocks are used. We keep it for the sake of comparison and comprehension.
  
  Arguments:
       num_heads: required: an integer determining number of attention heads of the layer.
       seq_len: required: an integer determining number of attention heads of the layer.
       different_layout_per_head: optional: this is just for the sake of consistency with other sparsity formats; can ignore it for DenseSparsityConfig
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code: def __init__(
      self, num_heads, block=16, different_layout_per_head=False
  ):
      """Initialize the Dense Sparsity Pattern Config.
      In reality, this is not sparse and all blocks are used. We keep it for the sake of comparison and comprehension.
  
      Arguments:
           num_heads: required: an integer determining number of attention heads of the layer.
           seq_len: required: an integer determining number of attention heads of the layer.
           different_layout_per_head: optional: this is just for the sake of consistency with other sparsity formats; can ignore it for DenseSparsityConfig
      """
  
      super().__init__(num_heads, block, different_layout_per_head)
  
imports: import torch
  import random
label_desc: Use image node_id 1 to create a new DenseSparsityConfig object from inherited base classes: SparsityConfig with example: obj = DenseSparsityConfig(num_heads, block, different_layout_per_head)