code: def _x_and_lstm(draw, dtypes):
      dtype = draw(dtypes)
      batch_shape = (1,)
  
      t = draw(helpers.ints(min_value=1, max_value=2))
      _in_ = draw(helpers.ints(min_value=1, max_value=2))
      _out_ = draw(helpers.ints(min_value=1, max_value=2))
  
      x_lstm_shape = batch_shape + (t,) + (_in_,)
      init_h_shape = batch_shape + (_out_,)
      init_c_shape = init_h_shape
      kernel_shape = (_in_,) + (4 * _out_,)
      recurrent_kernel_shape = (_out_,) + (4 * _out_,)
      bias_shape = (4 * _out_,)
      recurrent_bias_shape = bias_shape
  
      x_lstm = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=x_lstm_shape,
              min_value=0,
              max_value=1,
          )
      )
      init_h = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=init_h_shape,
              min_value=0,
              max_value=1,
          )
      )
      init_c = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=init_c_shape,
              min_value=0,
              max_value=1,
          )
      )
      kernel = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=kernel_shape,
              min_value=0,
              max_value=1,
          )
      )
      recurrent_kernel = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=recurrent_kernel_shape,
              min_value=0,
              max_value=1,
          )
      )
      lstm_bias = draw(
          helpers.array_values(
              dtype=dtype[0], shape=bias_shape, min_value=0, max_value=1
          )
      )
      recurrent_bias = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=recurrent_bias_shape,
              min_value=0,
              max_value=1,
          )
      )
      return (
          dtype,
          x_lstm,
          init_h,
          init_c,
          kernel,
          recurrent_kernel,
          lstm_bias,
          recurrent_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 12 for calling a global function with example usage: _x_and_lstm(draw, dtypes) and returns: dtype,  x_lstm,  init_h,  init_c,  kernel,  recurrent_kernel,  lstm_bias,  recurrent_bias