code: def _x_and_filters(
      draw,
      dim: int = 2,
      padding=None,
      transpose: bool = False,
      depthwise=False,
      general=False,
      bias=False,
  ):
      if not isinstance(dim, int):
          dim = draw(dim)
      batch_size = draw(st.integers(1, 5))
      filter_shape = draw(
          helpers.get_shape(
              min_num_dims=dim,
              max_num_dims=dim,
              min_dim_size=1,
              max_dim_size=5,
          )
      )
      dtype = draw(helpers.get_dtypes("float", full=False))
      input_channels = draw(st.integers(1, 3))
      output_channels = draw(st.integers(1, 3))
      group_list = [*range(1, 6)]
      if not transpose:
          group_list = list(
              filter(
                  lambda x: (
                      input_channels % x == 0 and x <= output_channels
                  ),
                  group_list,
              )
          )
      else:
          group_list = list(
              filter(lambda x: (output_channels % x == 0), group_list)
          )
      fc = draw(st.sampled_from(group_list))
      strides = draw(
          st.one_of(
              st.integers(1, 3),
              st.lists(st.integers(1, 3), min_size=dim, max_size=dim),
          )
          if dim > 1
          else st.integers(1, 3)
      )
      dilations = draw(
          st.one_of(
              st.integers(1, 3),
              st.lists(st.integers(1, 3), min_size=dim, max_size=dim),
          )
          if dim > 1
          else st.integers(1, 3)
      )
      if dim == 2:
          data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
      elif dim == 1:
          data_format = draw(st.sampled_from(["NWC", "NCW"]))
      else:
          data_format = draw(st.sampled_from(["NDHWC", "NCDHW"]))
      fdilations = (
          [dilations] * dim if isinstance(dilations, int) else dilations
      )
      if padding is None:
          padding = st.one_of(
              st.lists(
                  st.tuples(
                      st.integers(min_value=0, max_value=3),
                      st.integers(min_value=0, max_value=3),
                  ),
                  min_size=dim,
                  max_size=dim,
              ),
              st.sampled_from(["SAME", "VALID"]),
              st.integers(min_value=0, max_value=3),
          )
      padding = draw(padding)
      if transpose:
          fstrides = (
              [strides] * dim if isinstance(strides, int) else strides
          )
          if isinstance(padding, list):
              assume(
                  all(
                      max(pad) - min(pad) < min(stride, dilation)
                      for pad, stride, dilation in zip(
                          padding, fstrides, fdilations
                      )
                  )
              )
          x_dim = draw(
              helpers.get_shape(
                  min_num_dims=dim,
                  max_num_dims=dim,
                  min_dim_size=1,
                  max_dim_size=5,
              )
          )
          output_shape = _output_shape(
              dim, fdilations, fstrides, padding, x_dim, filter_shape
          )
          assume(all(s > 0 for s in output_shape))
          if draw(st.booleans()):
              output_shape = None
      else:
          x_dim = []
          for i in range(dim):
              min_x = filter_shape[i] + (filter_shape[i] - 1) * (
                  fdilations[i] - 1
              )
              x_dim.append(draw(st.integers(min_x, min_x + 1)))
          x_dim = tuple(x_dim)
      if not depthwise:
          if not transpose:
              output_channels = output_channels * fc
              filter_shape = filter_shape + (
                  input_channels // fc,
                  output_channels,
              )
          else:
              input_channels = input_channels * fc
              filter_shape = filter_shape + (
                  output_channels // fc,
                  input_channels,
              )
      else:
          filter_shape = filter_shape + (input_channels,)
      channel_first = True
      if data_format in ["NHWC", "NWC", "NDHWC"]:
          x_shape = (batch_size,) + x_dim + (input_channels,)
          channel_first = False
      else:
          x_shape = (batch_size, input_channels) + x_dim
      vals = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=x_shape,
              min_value=0.0,
              max_value=1.0,
          )
      )
      filters = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=filter_shape,
              min_value=0.0,
              max_value=1.0,
          )
      )
      if bias:
          bias_shape = (output_channels,)
          b = draw(
              helpers.array_values(
                  dtype=dtype[0],
                  shape=bias_shape,
                  min_value=0.0,
                  max_value=1.0,
              )
          )
      if general:
          data_format = (
              "channel_first" if channel_first else "channel_last"
          )
      if not transpose:
          x_dilation = draw(
              st.one_of(
                  st.integers(1, 3),
                  st.lists(
                      st.integers(1, 3), min_size=dim, max_size=dim
                  ),
              )
          )
          dilations = (dilations, x_dilation)
      if not depthwise:
          filter_format = draw(
              st.sampled_from(["channel_first", "channel_last"])
          )
          if filter_format == "channel_first":
              filters = np.transpose(filters, (-1, -2, *range(dim)))
      ret = (
          dtype,
          vals,
          filters,
          dilations,
          data_format,
          strides,
          padding,
      )
      ret = ret + (output_shape, fc) if transpose else ret + (fc,)
      if not depthwise:
          ret = ret + (filter_format,)
      if bias:
          return ret + (b,)
      return ret
  
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 9 for calling a global function with example usage: _x_and_filters(draw, dim, padding, transpose, depthwise, general, bias) and returns: ret, unknown