code: def _conv(
      input_var,
      name,
      filter_size,
      num_filter,
      strides,
      hidden_w_init,
      hidden_b_init,
      padding,
  ):
      """Helper function for performing convolution.
  
      Args:
          input_var (tf.Tensor): Input tf.Tensor to the CNN.
          name (str): Variable scope of the convolution Op.
          filter_size (tuple[int]): Dimension of the filter. For example,
              (3, 5) means the dimension of the filter is (3 x 5).
          num_filter (int): Number of filters. For example, (3, 32) means
              there are two convolutional layers. The filter for the first layer
              has 3 channels and the second one with 32 channels.
          strides (tuple[int]): The stride of the sliding window. For example,
              (1, 2) means there are two convolutional layers. The stride of the
              filter for first layer is 1 and that of the second layer is 2.
          hidden_w_init (callable): Initializer function for the weight
              of intermediate dense layer(s). The function should return a
              tf.Tensor.
          hidden_b_init (callable): Initializer function for the bias
              of intermediate dense layer(s). The function should return a
              tf.Tensor.
          padding (str): The type of padding algorithm to use,
              either 'SAME' or 'VALID'.
  
      Return:
          tf.Tensor: The output of the convolution.
  
      """
      # channel from input
      input_shape = input_var.get_shape()[-1]
      # [filter_height, filter_width, in_channels, out_channels]
      w_shape = [
          filter_size[0],
          filter_size[1],
          input_shape,
          num_filter,
      ]
      b_shape = [1, 1, 1, num_filter]
  
      with tf.compat.v1.variable_scope(name):
          weight = tf.compat.v1.get_variable(
              "weight", w_shape, initializer=hidden_w_init
          )
          bias = tf.compat.v1.get_variable(
              "bias", b_shape, initializer=hidden_b_init
          )
  
          return (
              tf.nn.conv2d(
                  input_var, weight, strides=strides, padding=padding
              )
              + bias
          )
  
imports: import tensorflow
  from garage.experiment import deterministic
label_desc: Use image node_id 3 for calling a global function with example usage: _conv(input_var, name, filter_size, num_filter, strides, hidden_w_init, hidden_b_init, padding) and returns: unknown