code: def _block_layout(
      self,
      blk_shape: Any,
      head_idx: int,
      query_idx: int,
      key_idx: int,
      blk_idx: int,
  ) -> np.ndarray:
      if query_idx > key_idx:
          return np.ones(2 * [self.block_size], dtype=np.bool)
      elif query_idx < key_idx:
          return np.zeros(2 * [self.block_size], dtype=np.bool)
      else:
          return np.tril(np.ones(2 * [self.block_size], dtype=np.bool))
  
imports: import math
  from abc import ABC, abstractmethod
  from itertools import product
  from typing import Any, Optional
  import attr
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the DenseCausalAttentionMask obj's underlying member method code with example usage: obj._block_layout(blk_shape, head_idx, query_idx, key_idx, blk_idx) and returns: np, np, np