class_docstr: Layer constructor function for a general convolution layer.
code: def forward(self, x):
      if self._use_bias:
          w, b = self.weights
      else:
          w = 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] + x_shape[-3:])
      res = fastmath.conv(
          x,
          w,
          self._strides,
          self._padding,
          self._dimension_numbers,
          self._one,
      )
      if self._use_bias:
          res = res + b
      if len(x_shape) > 4:
          res = jnp.reshape(res, x_shape[:-3] + list(res.shape[-3:]))
      return res
  
imports: import functools
  import itertools
  import operator
  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 Conv obj's underlying member method code with example usage: obj.forward(x) and returns: res