code: def conv_transpose(
      inputs,
      kernel,
      strides=1,
      padding="valid",
      output_padding=None,
      data_format=None,
      dilation_rate=1,
  ):
      data_format = standardize_data_format(data_format)
      num_spatial_dims = inputs.ndim - 2
      padding_values = compute_conv_transpose_padding_args_for_jax(
          input_shape=inputs.shape,
          kernel_shape=kernel.shape,
          strides=strides,
          padding=padding,
          output_padding=output_padding,
          dilation_rate=dilation_rate,
      )
      dimension_numbers = _convert_to_lax_conv_dimension_numbers(
          num_spatial_dims,
          data_format,
          transpose=False,
      )
      strides = _convert_to_spatial_operand(
          strides,
          num_spatial_dims,
          data_format,
          include_batch_and_channels=False,
      )
      dilation_rate = _convert_to_spatial_operand(
          dilation_rate,
          num_spatial_dims,
          data_format,
          include_batch_and_channels=False,
      )
  
      return jax.lax.conv_transpose(
          inputs,
          kernel,
          strides,
          padding=padding_values,
          rhs_dilation=dilation_rate,
          dimension_numbers=dimension_numbers,
          transpose_kernel=True,
      )
  
imports: import jax
  import jax.numpy
  import numpy
  from jax import lax
  from jax import nn
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_jax
  from keras.backend.config import epsilon
  from keras.backend.jax.core import cast
  from keras.backend.jax.core import convert_to_tensor
label_desc: Use image node_id 25 for calling a global function with example usage: conv_transpose(inputs, kernel, strides, padding, output_padding, data_format, dilation_rate) and returns: jax