code: def _x_ic_oc_f_d_df(
      draw, dim: int = 2, transpose: bool = False, depthwise=False
  ):
      strides = draw(st.integers(min_value=1, max_value=3))
      padding = draw(st.sampled_from(["SAME", "VALID"]))
      batch_size = draw(st.integers(1, 1))
      filter_shape = draw(
          helpers.get_shape(
              min_num_dims=dim,
              max_num_dims=dim,
              min_dim_size=1,
              max_dim_size=5,
          )
      )
      input_channels = draw(st.integers(1, 3))
      output_channels = draw(st.integers(1, 3))
      dilations = 1
      x_dim = []
      for i in range(dim):
          min_x = filter_shape[i] + (filter_shape[i] - 1) * (
              dilations - 1
          )
          x_dim.append(draw(st.integers(min_x, 20)))
      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"]))
      if data_format in ["NHWC", "NWC", "NDHWC"]:
          x_shape = [batch_size] + x_dim + [input_channels]
      else:
          x_shape = [batch_size] + [input_channels] + x_dim
  
      if transpose:
          output_shape = []
          for i in range(dim):
              output_shape.append(
                  _deconv_length(
                      x_dim[i],
                      strides,
                      filter_shape[i],
                      padding,
                      dilations,
                  )
              )
      filter_shape = list(filter_shape)
      if dim == 1:
          filter_shape = filter_shape[0]
      dtype, vals = draw(
          helpers.dtype_and_values(
              available_dtypes=helpers.get_dtypes("float", full=True),
              shape=x_shape,
              min_value=0,
              max_value=1,
          ).filter(lambda x: x[0] != ["float16"])
      )
      if transpose:
          return (
              dtype,
              vals,
              input_channels,
              output_channels,
              filter_shape,
              strides,
              dilations,
              data_format,
              padding,
              output_shape,
          )
      return (
          dtype,
          vals,
          input_channels,
          output_channels,
          filter_shape,
          strides,
          dilations,
          data_format,
          padding,
      )
  
imports: import numpy
  from hypothesis import assume
  from hypothesis import strategies
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy.data_classes.container import Container
  from ivy.functional.ivy.gradients import _variable
  from ivy.functional.ivy.layers import _deconv_length
  from ivy_tests.test_ivy.helpers import handle_method
  from ivy_tests.test_ivy.helpers.assertions import assert_same_type_and_shape
  from ivy_tests.test_ivy.test_functional.test_experimental.test_nn import test_layers
  from ivy_tests.test_ivy.test_functional.test_experimental.test_nn.test_layers import _valid_dct
label_desc: Use image node_id 7 for calling a global function with example usage: _x_ic_oc_f_d_df(draw, dim, transpose, depthwise) and returns: dtype,  vals,  input_channels,  output_channels,  filter_shape,  strides,  dilations,  data_format,  padding, dtype,  vals,  input_channels,  output_channels,  filter_shape,  strides,  dilations,  data_format,  padding,  output_shape