code: def forward_pass(self, inputs, param_vector):
      # Input dimensions:  [data, color_in, y, x]
      # Params dimensions: [color_in, color_out, y, x]
      # Output dimensions: [data, color_out, y, x]
      params = self.parser.get(param_vector, "params")
      biases = self.parser.get(param_vector, "biases")
      conv = convolve(
          inputs,
          params,
          axes=([2, 3], [2, 3]),
          dot_axes=([1], [0]),
          mode="valid",
      )
      return conv + biases
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  from builtins import range
  import autograd.numpy
  import autograd.numpy.random
  import autograd.scipy.signal
  from autograd import grad
  import data_mnist
label_desc: Use image node_id 2 for calling the conv_layer obj's underlying member method code with example usage: obj.forward_pass(inputs, param_vector) and returns: unknown