code: def x_and_filters(
      draw,
      dim: int = 2,
      transpose: bool = False,
      depthwise=False,
      mixed_fn_compos=True,
  ):
      """Draws a random x and filters for a convolution.
  
      Parameters
      ----------
      draw
          special function that draws data randomly (but is reproducible) from a given
          data-set (ex. list).
      dim
          the dimension of the convolution
      transpose
          if True, draw a transpose convolution
      depthwise
          if True, draw a depthwise convolution
  
      Returns
      -------
          A strategy that draws a random x and filters for a convolution.
      """
      strides = draw(st.integers(min_value=1, max_value=2))
      padding = draw(st.sampled_from(["SAME", "VALID"]))
      batch_size = draw(st.integers(1, 5))
      filter_shape = draw(
          get_shape(
              min_num_dims=dim,
              max_num_dims=dim,
              min_dim_size=1,
              max_dim_size=5,
          )
      )
      input_channels = draw(st.integers(1, 5))
      output_channels = draw(st.integers(1, 5))
      dilations = draw(st.integers(1, 2))
      dtype = draw(
          dtype_helpers.get_dtypes(
              "float", mixed_fn_compos=mixed_fn_compos, full=False
          )
      )
      if dim == 2:
          data_format = draw(st.sampled_from(["NCHW"]))
      elif dim == 1:
          data_format = draw(st.sampled_from(["NWC", "NCW"]))
      else:
          data_format = draw(st.sampled_from(["NDHWC", "NCDHW"]))
  
      x_dim = []
      if transpose:
          output_shape = []
          x_dim = draw(
              get_shape(
                  min_num_dims=dim,
                  max_num_dims=dim,
                  min_dim_size=1,
                  max_dim_size=20,
              )
          )
          for i in range(dim):
              output_shape.append(
                  _deconv_length(
                      x_dim[i],
                      strides,
                      filter_shape[i],
                      padding,
                      dilations,
                  )
              )
      else:
          for i in range(dim):
              min_x = filter_shape[i] + (filter_shape[i] - 1) * (
                  dilations - 1
              )
              x_dim.append(draw(st.integers(min_x, 100)))
          x_dim = tuple(x_dim)
      if not depthwise:
          filter_shape = filter_shape + (
              input_channels,
              output_channels,
          )
      else:
          filter_shape = filter_shape + (input_channels,)
      if data_format in ["NHWC", "NWC", "NDHWC"]:
          x_shape = (batch_size,) + x_dim + (input_channels,)
      else:
          x_shape = (batch_size, input_channels) + x_dim
      vals = draw(
          array_helpers.array_values(
              shape=x_shape,
              dtype=dtype[0],
              large_abs_safety_factor=3,
              small_abs_safety_factor=4,
              safety_factor_scale="log",
          )
      )
      filters = draw(
          array_helpers.array_values(
              shape=filter_shape,
              dtype=dtype[0],
              large_abs_safety_factor=3,
              small_abs_safety_factor=4,
              safety_factor_scale="log",
          )
      )
      if transpose:
          return (
              dtype,
              vals,
              filters,
              dilations,
              data_format,
              strides,
              padding,
              output_shape,
          )
      return (
          dtype,
          vals,
          filters,
          dilations,
          data_format,
          strides,
          padding,
      )
  
imports: from hypothesis import assume, strategies
  from typing import Tuple
  from functools import lru_cache
  import math
  import numpy
  import ivy
  from .None import array_helpers, number_helpers, dtype_helpers
  from ..pipeline_helper import WithBackendContext
  from ivy.functional.ivy.layers import _deconv_length
  from ..globals import mod_backend
label_desc: Use image node_id 13 for calling a global function with example usage: x_and_filters(draw, dim, transpose, depthwise, mixed_fn_compos) and returns: dtype,  vals,  filters,  dilations,  data_format,  strides,  padding, dtype,  vals,  filters,  dilations,  data_format,  strides,  padding,  output_shape