code: def verify_batch_flatten_grad(d_shape, executor_kind):
      data = relay.var("data", relay.TensorType(d_shape, "float32"))
      fwd_func = relay.Function([data], relay.nn.batch_flatten(data))
      check_grad(fwd_func, executor_kind=executor_kind)
  
imports: import numpy
  import pytest
  from tvm import topi
  import tvm.topi.testing
  import tvm
  from tvm import te
  from tvm import relay
  from tvm.relay.testing import check_grad, run_infer_type, run_opt_pass
  from tvm.relay.transform import gradient
  import tvm.testing
label_desc: Use image node_id 13 for calling a global function with example usage: verify_batch_flatten_grad(d_shape, executor_kind) without return types