code: def test_torch_binary_cross_entropy_with_logits(
      *,
      dtype_and_true,
      dtype_and_pred,
      dtype_and_weight,
      size_average,
      reduce,
      reduction,
      dtype_and_pos_weight,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):
      pred_dtype, pred = dtype_and_pred
      true_dtype, true = dtype_and_true
      weight_dtype, weight = dtype_and_weight
      pos_weight_dtype, pos_weight = dtype_and_pos_weight
      helpers.test_frontend_function(
          input_dtypes=[
              pred_dtype[0],
              true_dtype[0],
              weight_dtype[0],
              pos_weight_dtype[0],
          ],
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          input=pred[0],
          target=true[0],
          weight=weight[0],
          size_average=size_average,
          reduce=reduce,
          reduction=reduction,
          pos_weight=pos_weight[0],
      )
  
imports: import numpy
  from hypothesis import strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  import ivy
  from ivy.functional.frontends.torch.nn.functional.loss_functions import cosine_similarity
label_desc: Use image node_id 2 for calling a global function with example usage: test_torch_binary_cross_entropy_with_logits() without return types