code: def _generate_bias_data(draw, keras_backend_fn=False):
      data_format = draw(st.sampled_from(["NC...", "N...C", None]))
      channel_dim = 1 if data_format == "NC..." else -1
      if keras_backend_fn:
          data_format = {
              "NC...": "channels_first",
              "N...C": "channels_last",
              None: None,
          }[data_format]
      dtype, value, shape = draw(
          helpers.dtype_and_values(
              available_dtypes=helpers.get_dtypes("numeric"),
              min_num_dims=3,
              ret_shape=True,
          )
      )
      channel_size = shape[channel_dim]
      bias = draw(
          helpers.array_values(dtype=dtype[0], shape=(channel_size,))
      )
      return data_format, dtype, value, bias
  
imports: from hypothesis import assume, strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_functional.test_core.test_statistical import _statistical_dtype_values
  from ivy_tests.test_ivy.test_functional.test_nn.test_layers import _assume_tf_dilation_gt_1, _output_shape
label_desc: Use image node_id 5 for calling a global function with example usage: _generate_bias_data(draw, keras_backend_fn) and returns: data_format,  dtype,  value,  bias