class_docstr: Layer constructor function for a general Transpose Convolutional Layer.
class_docstr_tok: ["Layer","constructor","function","for","a","general","Transpose","Convolutional","Layer","."]
code: def forward(self, x):
      w, b = self.weights
      x_shape = list(x.shape)
      if len(x_shape) > 4:
          self._check_nhwc()
          new_batch_dim = functools.reduce(operator.mul, x.shape[:-3])
          x = jnp.reshape(x, [new_batch_dim] + list(x.shape[-3:]))
      res = (
          lax.conv_transpose(
              x,
              w,
              self._strides,
              self._padding,
              self._rhs_dilation,
              self._dimension_numbers,
          )
          + b
      )
      if len(x_shape) > 4:
          res = jnp.reshape(res, x_shape[:-3] + list(res.shape[-3:]))
      return res
  
file_path: trax/trax/layers/deconvolution.py
imports: import functools
  import itertools
  import operator
  from jax import lax
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import initializers
label_desc: Use image node_id 3 for calling the ConvTranspose obj's underlying member method code with example usage: obj.forward(x) and returns: res