code: def GeneralConvTranspose(
      dimension_numbers,
      out_chan,
      filter_shape,
      strides=None,
      padding="VALID",
      W_init=None,
      b_init=normal(1e-6),
  ):
      """Layer construction function for a general transposed-convolution layer."""
      lhs_spec, rhs_spec, out_spec = dimension_numbers
      one = (1,) * len(filter_shape)
      strides = strides or one
      W_init = W_init or glorot_normal(
          rhs_spec.index("I"), rhs_spec.index("O")
      )
  
      def init_fun(rng, input_shape):
          filter_shape_iter = iter(filter_shape)
          kernel_shape = [
              out_chan
              if c == "O"
              else input_shape[lhs_spec.index("C")]
              if c == "I"
              else next(filter_shape_iter)
              for c in rhs_spec
          ]
          output_shape = lax.conv_transpose_shape_tuple(
              input_shape,
              kernel_shape,
              strides,
              padding,
              dimension_numbers,
          )
          bias_shape = [out_chan if c == "C" else 1 for c in out_spec]
          k1, k2 = random.split(rng)
          W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)
          return output_shape, (W, b)
  
      def apply_fun(params, inputs, **kwargs):
          W, b = params
          return (
              lax.conv_transpose(
                  inputs,
                  W,
                  strides,
                  padding,
                  dimension_numbers=dimension_numbers,
              )
              + b
          )
  
      return init_fun, apply_fun
  
imports: import functools
  import operator
  from jax import lax
  from jax import random
  import jax.numpy
  from jax.nn import relu, log_softmax, softmax, softplus, sigmoid, elu, leaky_relu, selu, gelu, standardize
  from jax.nn.initializers import glorot_normal, normal, ones, zeros
label_desc: Use image node_id 3 for calling a global function with example usage: GeneralConvTranspose(dimension_numbers, out_chan, filter_shape, strides, padding, W_init, b_init) and returns: init_fun,  apply_fun, output_shape,  W,  b, unknown