code: def _x_and_filters(
      draw,
      dtypes,
      data_format,
      padding=None,
      stride_min=1,
      stride_max=4,
      dilation_min=1,
      dilation_max=4,
      type: str = "2d",
      transpose=False,
      atrous=False,
  ):
      data_format = draw(data_format)
      dtype = draw(dtypes)
      if type is not None:
          if "1" in type:
              dim = 1
          elif "2" in type:
              dim = 2
          elif "3" in type:
              dim = 3
          elif type in ["depthwise", "separable"]:
              dim = 2
      else:
          dim = len(data_format) - 2
      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"]),
          )
      padding = draw(padding)
      if atrous:
          dilations = draw(st.integers(dilation_min, dilation_max))
      else:
          dilations = draw(
              st.one_of(
                  st.integers(dilation_min, dilation_max),
                  st.lists(
                      st.integers(dilation_min, dilation_max),
                      min_size=dim,
                      max_size=dim,
                  ),
              )
          )
      fdilations = (
          [dilations] * dim if isinstance(dilations, int) else dilations
      )
      if atrous:
          stride = 1
      elif type in ["depthwise", "separable"]:
          # if any value in dilations is greater than 1, tensorflow implements
          # depthwise_covn2d as an atrous depthwise convolution, in which case all values
          # in strides must be equal to 1.
          if any(x > 1 for x in fdilations):
              stride = 1
          else:
              stride = draw(st.integers(stride_min, stride_max))
      else:
          stride = draw(
              st.one_of(
                  st.integers(stride_min, stride_max),
                  st.lists(
                      st.integers(stride_min, stride_max),
                      min_size=dim,
                      max_size=dim,
                  ),
              )
          )
      fstride = [stride] * dim if isinstance(stride, int) else stride
      if dim == 1:
          if not transpose:
              filter_shape = draw(
                  st.tuples(
                      helpers.ints(min_value=3, max_value=5),
                      helpers.ints(min_value=1, max_value=3),
                      helpers.ints(min_value=1, max_value=3),
                  )
              )
              min_x_width = filter_shape[0] + (filter_shape[0] - 1) * (
                  fdilations[0] - 1
              )
          else:
              filter_shape = draw(
                  st.tuples(
                      helpers.ints(min_value=3, max_value=5),
                      helpers.ints(min_value=1, max_value=3),
                      helpers.ints(min_value=1, max_value=3),
                  )
              )
              min_x_width = 1
          if transpose:
              d_in = filter_shape[2]
          else:
              d_in = filter_shape[1]
          if data_format == "NWC":
              x_shape = draw(
                  st.tuples(
                      helpers.ints(min_value=1, max_value=5),
                      helpers.ints(
                          min_value=min_x_width, max_value=100
                      ),
                      helpers.ints(min_value=d_in, max_value=d_in),
                  )
              )
          else:
              x_shape = draw(
                  st.tuples(
                      helpers.ints(min_value=1, max_value=5),
                      helpers.ints(min_value=d_in, max_value=d_in),
                      helpers.ints(
                          min_value=min_x_width, max_value=100
                      ),
                  )
              )
      elif dim == 2:
          min_x_height = 1
          min_x_width = 1
          filter_shape = draw(
              st.tuples(
                  helpers.ints(min_value=3, max_value=5),
                  helpers.ints(min_value=3, max_value=5),
                  helpers.ints(min_value=1, max_value=3),
                  helpers.ints(min_value=1, max_value=3),
              )
          )
          if not transpose:
              min_x_height = filter_shape[0] + (filter_shape[0] - 1) * (
                  fdilations[0] - 1
              )
              min_x_width = filter_shape[1] + (filter_shape[1] - 1) * (
                  fdilations[1] - 1
              )
          if transpose:
              d_in = filter_shape[3]
          else:
              d_in = filter_shape[2]
          if data_format == "NHWC":
              x_shape = draw(
                  st.tuples(
                      helpers.ints(min_value=1, max_value=5),
                      helpers.ints(
                          min_value=min_x_height, max_value=100
                      ),
                      helpers.ints(
                          min_value=min_x_width, max_value=100
                      ),
                      helpers.ints(min_value=d_in, max_value=d_in),
                  )
              )
          else:
              x_shape = draw(
                  st.tuples(
                      helpers.ints(min_value=1, max_value=5),
                      helpers.ints(min_value=d_in, max_value=d_in),
                      helpers.ints(
                          min_value=min_x_height, max_value=100
                      ),
                      helpers.ints(
                          min_value=min_x_width, max_value=100
                      ),
                  )
              )
      elif dim == 3:
          filter_shape = draw(
              st.tuples(
                  helpers.ints(min_value=3, max_value=5),
                  helpers.ints(min_value=3, max_value=5),
                  helpers.ints(min_value=3, max_value=5),
                  helpers.ints(min_value=1, max_value=3),
                  helpers.ints(min_value=1, max_value=3),
              )
          )
          if not transpose:
              min_x_depth = filter_shape[0] + (filter_shape[0] - 1) * (
                  fdilations[0] - 1
              )
              min_x_height = filter_shape[1] + (filter_shape[1] - 1) * (
                  fdilations[1] - 1
              )
              min_x_width = filter_shape[2] + (filter_shape[2] - 1) * (
                  fdilations[2] - 1
              )
          else:
              min_x_depth = 1
              min_x_height = 1
              min_x_width = 1
          if transpose:
              d_in = filter_shape[4]
          else:
              d_in = filter_shape[3]
          if data_format == "NDHWC":
              x_shape = draw(
                  st.tuples(
                      helpers.ints(min_value=1, max_value=5),
                      helpers.ints(
                          min_value=min_x_depth, max_value=100
                      ),
                      helpers.ints(
                          min_value=min_x_height, max_value=100
                      ),
                      helpers.ints(
                          min_value=min_x_width, max_value=100
                      ),
                      helpers.ints(min_value=d_in, max_value=d_in),
                  )
              )
          else:
              x_shape = draw(
                  st.tuples(
                      helpers.ints(min_value=1, max_value=5),
                      helpers.ints(min_value=d_in, max_value=d_in),
                      helpers.ints(
                          min_value=min_x_depth, max_value=100
                      ),
                      helpers.ints(
                          min_value=min_x_height, max_value=100
                      ),
                      helpers.ints(
                          min_value=min_x_width, max_value=100
                      ),
                  )
              )
      if data_format[-1] == "C":
          x_dims = x_shape[1:-1]
      else:
          x_dims = x_shape[2:]
      if transpose:
          output_shape = _output_shape(
              dim, fdilations, fstride, padding, x_dims, filter_shape
          )
          assume(all(s > 0 for s in output_shape))
          if data_format[1] == "C":
              output_shape = [
                  x_shape[0],
                  filter_shape[dim],
                  *output_shape,
              ]
          else:
              output_shape = [
                  x_shape[0],
                  *output_shape,
                  filter_shape[dim],
              ]
      if not isinstance(padding, str):
          assume(
              all(
                  max(pad) - min(pad) < min(stride, dilation)
                  for pad, stride, dilation in zip(
                      padding, fstride, fdilations
                  )
              )
          )
          if data_format[1] == "C":
              padding = [(0, 0), (0, 0), *padding]
          else:
              padding = [(0, 0), *padding, (0, 0)]
      x = draw(
          helpers.array_values(
              dtype=dtype[0], shape=x_shape, min_value=0, max_value=1
          )
      )
      filters = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=filter_shape,
              min_value=0,
              max_value=1,
          )
      )
      if type == "separable":
          p_filter_shape = (
              1,
              1,
              filter_shape[-1] * filter_shape[-2],
              draw(helpers.ints(min_value=1, max_value=3)),
          )
          p_filters = draw(
              helpers.array_values(
                  dtype=dtype[0],
                  shape=p_filter_shape,
                  min_value=0,
                  max_value=1,
              )
          )
          filters = [filters, p_filters]
      if type in ["depthwise", "separable"]:
          stride = [1, stride, stride, 1]
          if isinstance(dilations, int):
              dilations = [dilations] * dim
      elif not atrous and type is not None:
          if transpose:
              if isinstance(stride, int):
                  stride = [stride]
              else:
                  if draw(st.booleans()):
                      stride = [1, *stride, 1]
              if isinstance(dilations, int):
                  dilations = [dilations]
              else:
                  if draw(st.booleans()):
                      dilations = [1, *dilations, 1]
          else:
              if dim != 3:
                  if isinstance(stride, int):
                      stride = [stride]
                  else:
                      if draw(st.booleans()):
                          stride = [1, *stride, 1]
                  if isinstance(dilations, int):
                      dilations = [dilations]
                  else:
                      if draw(st.booleans()):
                          dilations = [1, *dilations, 1]
              else:
                  if isinstance(stride, int):
                      stride = [stride] * dim
                  stride = [1, *stride, 1]
                  if isinstance(dilations, int):
                      dilations = [dilations] * dim
                  dilations = [1, *dilations, 1]
      if not transpose:
          return (
              dtype,
              x,
              filters,
              dilations,
              data_format,
              stride,
              padding,
          )
      return (
          dtype,
          x,
          filters,
          dilations,
          data_format,
          stride,
          padding,
          output_shape,
      )
  
imports: from hypothesis import assume, strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_functional.test_core.test_statistical import _statistical_dtype_values
  from ivy_tests.test_ivy.test_functional.test_nn.test_layers import _assume_tf_dilation_gt_1, _output_shape
label_desc: Use image node_id 8 for calling a global function with example usage: _x_and_filters(draw, dtypes, data_format, padding, stride_min, stride_max, dilation_min, dilation_max, type, transpose, atrous) and returns: dtype,  x,  filters,  dilations,  data_format,  stride,  padding,  output_shape, dtype,  x,  filters,  dilations,  data_format,  stride,  padding