code: def create_blocked_tensor(
      B, M, N, blocksize, sparsity, dtype, device
  ):
      assert (
          sparsity <= 1.0 and sparsity >= 0.0
      ), "sparsity should be a value between 0 and 1"
      assert M % blocksize[0] == 0
      assert N % blocksize[1] == 0
      shape = (B, M // blocksize[0], N // blocksize[1])[int(B == 0) :]
      A = torch.bernoulli(
          torch.full(shape, 1 - sparsity, dtype=dtype, device=device)
      )
      expected_nnz = int(
          (1 - sparsity) * M * N / (blocksize[0] * blocksize[1])
      )
      nonzero_indices = A.flatten().nonzero()
      actual_nnz = nonzero_indices.shape[0]
      if actual_nnz > expected_nnz:
          selected_nonzeros = torch.randperm(actual_nnz)[
              : actual_nnz - expected_nnz
          ]
          A.flatten()[nonzero_indices[selected_nonzeros]] = 0
      elif actual_nnz < expected_nnz:
          zero_indices = (A == 0).flatten().nonzero()
          selected_zeros = torch.randperm(zero_indices.shape[0])[
              : expected_nnz - actual_nnz
          ]
          A.flatten()[zero_indices[selected_zeros]] = 1
      A = torch.repeat_interleave(A, blocksize[0], dim=-2)
      A = torch.repeat_interleave(A, blocksize[1], dim=-1)
      return A
  
file_path: pytorch/torch/sparse/_triton_ops_meta.py
imports: import inspect
  import itertools
  import re
  import warnings
  from typing import Any, Dict
  import torch
  from torch.hub import tqdm
  from torch.testing import make_tensor
label_desc: Use image node_id 5 for calling a global function with example usage: create_blocked_tensor(B, M, N, blocksize, sparsity, dtype, device) and returns: A