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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def td_conv(inputs, filters, kernel_size, targeting_count, targeting_fn, keep_prob, is_training, do_prune=True, strides=(1, 1), padding="valid", data_format="channels_last", dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.zeros_initializer(), name=None, reuse=None):
 """Apply targeted dropout to the weights of a convolution."""

  with tf.variable_scope(name, default_name="td_conv", reuse=reuse):
    nhwc = data_format == "channels_last"
    in_dim = shape_list(inputs)[-1] if nhwc else shape_list(inputs)[1]

    kernel_shape = [kernel_size, kernel_size, in_dim, filters]
    w = tf.get_variable(
        "DW", shape=kernel_shape, initializer=kernel_initializer)
    if use_bias:
      b = tf.get_variable("b", shape=[filters], initializer=bias_initializer)

    if keep_prob < 1.0:
      w = targeted_dropout(
          w,
          targeting_count,
          keep_prob,
          targeting_fn,
          is_training,
          do_prune=do_prune)

    if isinstance(strides, int):
      strides = [strides, strides]
    if isinstance(dilation_rate, int):
      dilation_rate = [dilation_rate, dilation_rate]

    if nhwc:
      strides = [1, strides[0], strides[1], 1]
      dilation_rate = [1, dilation_rate[0], dilation_rate[1], 1]
    else:
      strides = [1, 1, strides[0], strides[1]]
      dilation_rate = [1, 1, dilation_rate[0], dilation_rate[1]]

    y = tf.nn.conv2d(
        inputs,
        w,
        strides,
        padding,
        data_format="NHWC" if nhwc else "NCHW",
        dilations=dilation_rate,
        name=None)

    if use_bias:
      y += b

    if activation:
      y = activation(y)

    return y