code: def matmul_add(N, L, M, dtype):
      A = te.placeholder((N, L), name="A", dtype=dtype)
      B = te.placeholder((L, M), name="B", dtype=dtype)
      C = te.placeholder((N, M), name="C", dtype=dtype)
  
      k = te.reduce_axis((0, L), name="k")
      matmul = te.compute(
          (N, M),
          lambda i, j: te.sum(A[i, k] * B[k, j], axis=k),
          name="matmul",
          attrs={
              "layout_free_placeholders": [B]
          },  # enable automatic layout transform for tensor B
      )
      out = te.compute(
          (N, M), lambda i, j: matmul[i, j] + C[i, j], name="out"
      )
  
      return [A, B, C, out]
  
imports: import numpy
  import tvm
  from tvm import te, auto_scheduler
label_desc: Use image node_id 1 for calling a global function with example usage: matmul_add(N, L, M, dtype) and returns: list