code: def _x_and_filters_and_transpose(
      draw,
      dim: int = 2,
      general=False,
      bias=False,
  ):
      transpose = draw(st.booleans())
      all_args = draw(
          _x_and_filters(
              dim=dim,
              general=general,
              bias=bias,
              transpose=transpose,
          )
      )
      output_shape = None
      if transpose:
          (
              dtype,
              x,
              filters,
              dilations,
              data_format,
              stride,
              pad,
              output_shape,
              fc,
              filter_format,
              bias,
          ) = all_args
      else:
          (
              dtype,
              x,
              filters,
              dilations,
              data_format,
              stride,
              pad,
              fc,
              filter_format,
              bias,
          ) = all_args
      return (
          dtype,
          x,
          filters,
          stride,
          pad,
          transpose,
          output_shape,
          data_format,
          filter_format,
          fc,
          dilations,
          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 10 for calling a global function with example usage: _x_and_filters_and_transpose(draw, dim, general, bias) and returns: dtype,  x,  filters,  stride,  pad,  transpose,  output_shape,  data_format,  filter_format,  fc,  dilations,  bias