code: def _x_and_linear(draw, dtypes):
      dtype = draw(dtypes)
      in_features = draw(helpers.ints(min_value=1, max_value=2))
      out_features = draw(helpers.ints(min_value=1, max_value=2))
  
      x_shape = (
          1,
          1,
          in_features,
      )
      weight_shape = (out_features, in_features)
      bias_shape = (out_features,)
  
      x = draw(
          helpers.array_values(
              dtype=dtype[0], shape=x_shape, min_value=0, max_value=1
          )
      )
      weight = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=weight_shape,
              min_value=0,
              max_value=1,
          )
      )
      bias = draw(
          helpers.array_values(
              dtype=dtype[0], shape=bias_shape, min_value=0, max_value=1
          )
      )
      return dtype, x, weight, bias
  
imports: from hypothesis import strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
label_desc: Use image node_id 1 for calling a global function with example usage: _x_and_linear(draw, dtypes) and returns: dtype,  x,  weight,  bias