code: def conv_transpose(
      lhs: Array,
      rhs: Array,
      strides: Sequence[int],
      padding: str | Sequence[tuple[int, int]],
      rhs_dilation: Sequence[int] | None = None,
      dimension_numbers: ConvGeneralDilatedDimensionNumbers = None,
      transpose_kernel: bool = False,
      precision: lax.PrecisionLike = None,
      preferred_element_type: DTypeLike | None = None,
  ) -> Array:
      """Convenience wrapper for calculating the N-d convolution "transpose".
  
      This function directly calculates a fractionally strided conv rather than
      indirectly calculating the gradient (transpose) of a forward convolution.
  
      Args:
        lhs: a rank `n+2` dimensional input array.
        rhs: a rank `n+2` dimensional array of kernel weights.
        strides: sequence of `n` integers, sets fractional stride.
        padding: 'SAME', 'VALID' will set as transpose of corresponding forward
          conv, or a sequence of `n` integer 2-tuples describing before-and-after
          padding for each `n` spatial dimension.
        rhs_dilation: `None`, or a sequence of `n` integers, giving the
          dilation factor to apply in each spatial dimension of `rhs`. RHS dilation
          is also known as atrous convolution.
        dimension_numbers: tuple of dimension descriptors as in
          lax.conv_general_dilated. Defaults to tensorflow convention.
        transpose_kernel: if True flips spatial axes and swaps the input/output
          channel axes of the kernel. This makes the output of this function identical
          to the gradient-derived functions like keras.layers.Conv2DTranspose
          applied to the same kernel. For typical use in neural nets this is completely
          pointless and just makes input/output channel specification confusing.
        precision: Optional. Either ``None``, which means the default precision for
          the backend, a :class:`~jax.lax.Precision` enum value (``Precision.DEFAULT``,
          ``Precision.HIGH`` or ``Precision.HIGHEST``) or a tuple of two
          :class:`~jax.lax.Precision` enums indicating precision of ``lhs``` and ``rhs``.
        preferred_element_type: Optional. Either ``None``, which means the default
          accumulation type for the input types, or a datatype, indicating to
          accumulate results to and return a result with that datatype.
  
      Returns:
        Transposed N-d convolution, with output padding following the conventions of
        keras.layers.Conv2DTranspose.
      """
      assert len(lhs.shape) == len(rhs.shape) and len(lhs.shape) >= 2
      ndims = len(lhs.shape)
      one = (1,) * (ndims - 2)
      # Set dimensional layout defaults if not specified.
      if dimension_numbers is None:
          if ndims == 2:
              dimension_numbers = ("NC", "IO", "NC")
          elif ndims == 3:
              dimension_numbers = ("NHC", "HIO", "NHC")
          elif ndims == 4:
              dimension_numbers = ("NHWC", "HWIO", "NHWC")
          elif ndims == 5:
              dimension_numbers = ("NHWDC", "HWDIO", "NHWDC")
          else:
              raise ValueError(
                  "No 4+ dimensional dimension_number defaults."
              )
      dn = conv_dimension_numbers(
          lhs.shape, rhs.shape, dimension_numbers
      )
      k_shape = np.take(rhs.shape, dn.rhs_spec)
      k_sdims = k_shape[2:]  # type: ignore[index]
      # Calculate correct output shape given padding and strides.
      pads: str | Sequence[tuple[int, int]]
      if isinstance(padding, str) and padding in {"SAME", "VALID"}:
          if rhs_dilation is None:
              rhs_dilation = (1,) * (rhs.ndim - 2)
          effective_k_size = map(
              lambda k, r: core.dilate_dim(k, r), k_sdims, rhs_dilation
          )
          pads = [
              _conv_transpose_padding(k, s, padding)
              for k, s in zip(effective_k_size, strides)
          ]
      else:
          pads = padding
      if transpose_kernel:
          # flip spatial dims and swap input / output channel axes
          rhs = _flip_axes(rhs, np.array(dn.rhs_spec)[2:])
          rhs = rhs.swapaxes(dn.rhs_spec[0], dn.rhs_spec[1])
      return conv_general_dilated(
          lhs,
          rhs,
          one,
          pads,
          strides,
          rhs_dilation,
          dn,
          precision=precision,
          preferred_element_type=preferred_element_type,
      )
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from functools import partial
  import operator
  from typing import NamedTuple, Union
  import numpy
  from jax._src import core
  from jax._src import dtypes
  from jax._src import util
  from jax._src.interpreters import ad
  from jax._src.interpreters import batching
  from jax._src.interpreters import mlir
  from jax._src.lax import lax
  from jax._src.lib.mlir.dialects import hlo
  from jax._src.typing import Array, DTypeLike
label_desc: Use image node_id 6 for calling a global function with example usage: conv_transpose(lhs, rhs, strides, padding, rhs_dilation, dimension_numbers, transpose_kernel, precision, preferred_element_type) and returns: conv_general_dilated