covariance matrix of coefficient estimates

I am wondering how to get covariance matrix of parameter estimates (e.g., inverse of Fisher's information matrix) in an SEM with OpenMx. I have included a sample code for a path model below. For example, I would like to get covariances between estimates of b1, b2, and cp that include in the covariance matrix.

library(OpenMx)
set.seed(1234)
n <- 100
x <- rnorm(n)
m <- 0.1 * x + rnorm(n)
y <- 0.3 * m + rnorm(n)
df <- data.frame(x = x, y = y, m = m)
manifests = c("x", "m", "y")
myModel <- mxModel(
  "Path Model",
  type = "RAM",
  manifestVars = manifests,
  mxPath(
    from = 'one',
    to = manifests,
    free = FALSE,
    values = 0
  ),
  mxPath(
    from = "x",
    to = "m",
    arrows = 1,
    free = TRUE,
    values = 0,
    labels = "b1"
  ),
  mxPath(
    from = c("x", "m"),
    to = "y",
    arrows = 1,
    free = TRUE,
    values = 0,
    labels = c("cp", "b2")
  ),
  mxPath(
    from = manifests,
    arrows = 2,
    free = TRUE,
    values = 1,
    labels = c("s2x", "s2m", "s2y")
  ),
  mxData(df, type = "raw")
)
fit <- mxRun(myModel)
summary(fit)