code: def _generate_batchnorm_data(draw):
      batch_size = draw(st.integers(min_value=2, max_value=5))
      num_features = draw(st.integers(min_value=2, max_value=3))
      num_dims = draw(st.integers(min_value=1, max_value=3))
      dims = [draw(st.integers(1, 5)) for i in range(num_dims)]
      x_shape = [batch_size] + [*dims] + [num_features]
      dtype, inputs = draw(
          helpers.dtype_and_values(
              available_dtypes=helpers.get_dtypes("float", full=True),
              shape=x_shape,
              min_value=0,
              max_value=1,
          ).filter(lambda x: x[0][0] not in ["float64"])
      )
      return dtype, inputs, num_features
  
imports: from hypothesis import strategies
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_method
label_desc: Use image node_id 1 for calling a global function with example usage: _generate_batchnorm_data(draw) and returns: dtype,  inputs,  num_features