code: def _x_and_linear(draw):
      mixed_fn_compos = draw(st.booleans())
      is_torch_backend = ivy.current_backend_str() == "torch"
      dtype = draw(
          # should sample from "valid" but with_supported_dtypes was not working
          helpers.get_dtypes(
              "float", full=False, mixed_fn_compos=mixed_fn_compos
          )
      )
      in_features = draw(
          helpers.ints(
              min_value=1, max_value=2, mixed_fn_compos=mixed_fn_compos
          )
      )
      out_features = draw(
          helpers.ints(
              min_value=1, max_value=2, mixed_fn_compos=mixed_fn_compos
          )
      )
  
      x_shape = (
          1,
          1,
          in_features,
      )
  
      weight_shape = (1,) + (out_features,) + (in_features,)
      # if backend is torch and we're testing the primary implementation
      # weight.ndim should be equal to 2
      if is_torch_backend and not mixed_fn_compos:
          weight_shape = (out_features,) + (in_features,)
  
      bias_shape = (
          1,
          out_features,
      )
  
      x = draw(
          helpers.array_values(
              dtype=dtype[0], shape=x_shape, min_value=0, max_value=10
          )
      )
      weight = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=weight_shape,
              min_value=0,
              max_value=10,
          )
      )
      bias = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=bias_shape,
              min_value=0,
              max_value=10,
          )
      )
      return dtype, x, weight, bias
  
imports: from hypothesis import strategies, assume
  import ivy
  import numpy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test
  from ivy.functional.ivy.layers import _deconv_length, _pack_padded_sequence
label_desc: Use image node_id 11 for calling a global function with example usage: _x_and_linear(draw) and returns: dtype,  x,  weight,  bias