class_docstr: Layer constructor function for a general convolution layer.
code: def init_weights_and_state(self, input_signature):
      input_shape = input_signature.shape
      if len(input_shape) > 4:
          self._check_nhwc()
          new_batch_dim = functools.reduce(
              operator.mul, input_shape[:-3]
          )
          input_shape = [new_batch_dim] + list(input_shape[-3:])
      kernel_shape = self._kernel_shape(input_shape)
      rng1, rng2 = fastmath.random.split(self.rng, 2)
      w = self._kernel_initializer(kernel_shape, rng1)
      if self._use_bias:
          bias_shape = [
              self._filters if c == "C" else 1 for c in self._out_spec
          ]
          bias_shape = tuple(
              itertools.dropwhile(lambda x: x == 1, bias_shape)
          )
          b = self._bias_initializer(bias_shape, rng2)
          self.weights = (w, b)
      else:
          self.weights = w
  
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 5 for calling the Conv obj's underlying member method code with example usage: obj.init_weights_and_state(input_signature) without return types