diff --git a/R/layers-normalization.R b/R/layers-normalization.R
index 227c486f3..32d6f2ad2 100644
--- a/R/layers-normalization.R
+++ b/R/layers-normalization.R
@@ -473,7 +473,7 @@ function (object, layer, power_iterations = 1L, ...)
 #'
 #' # Examples
 #' ```{r}
-#' data <- op_reshape(1:6, newshape = c(2, 3))
+#' data <- 1:6 |> op_array("float32") |> op_reshape(c(2, 3))
 #' normalized_data <- layer_unit_normalization(data)
 #' op_sum(normalized_data[1, ]^2)
 #' ```
diff --git a/R/utils.R b/R/utils.R
index 17f8c6421..5c5986df5 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -128,6 +128,7 @@ function (tensor)
 #' # Examples
 #' ```{r}
 #' path_to_downloaded_file <- get_file(
+#'     "flower_photos",
 #'     origin = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz",
 #'     extract = TRUE
 #' )
diff --git a/man/get_file.Rd b/man/get_file.Rd
index d13a955fe..730cbcad4 100644
--- a/man/get_file.Rd
+++ b/man/get_file.Rd
@@ -78,7 +78,8 @@ Passing a hash will verify the file after download. The command line
 programs \code{shasum} and \code{sha256sum} can compute the hash.
 }
 \section{Examples}{
-\if{html}{\out{<div class="sourceCode {r}">}}\preformatted{path_to_downloaded_file <- get_file(
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{path_to_downloaded_file <- get_file(
+    "flower_photos",
     origin = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz",
     extract = TRUE
 )
diff --git a/man/layer_integer_lookup.Rd b/man/layer_integer_lookup.Rd
index 1e639a3cc..bd648ff94 100644
--- a/man/layer_integer_lookup.Rd
+++ b/man/layer_integer_lookup.Rd
@@ -48,7 +48,7 @@ for OOV indices. Defaults to \code{-1}.}
 
 \item{vocabulary}{Optional. Either an array of integers or a string path to a
 text file. If passing an array, can pass a list, list,
-1D NumPy array, or 1D tensor containing the integer vocbulary terms.
+1D NumPy array, or 1D tensor containing the integer vocabulary terms.
 If passing a file path, the file should contain one line per term
 in the vocabulary. If this argument is set,
 there is no need to \code{adapt()} the layer.}
diff --git a/man/layer_string_lookup.Rd b/man/layer_string_lookup.Rd
index dde622685..4ec0da0a3 100644
--- a/man/layer_string_lookup.Rd
+++ b/man/layer_string_lookup.Rd
@@ -49,7 +49,7 @@ indices. Defaults to \code{"[UNK]"}.}
 
 \item{vocabulary}{Optional. Either an array of integers or a string path to a
 text file. If passing an array, can pass a list, list,
-1D NumPy array, or 1D tensor containing the integer vocbulary terms.
+1D NumPy array, or 1D tensor containing the integer vocabulary terms.
 If passing a file path, the file should contain one line per term
 in the vocabulary. If this argument is set,
 there is no need to \code{adapt()} the layer.}
diff --git a/man/layer_tfsm.Rd b/man/layer_tfsm.Rd
index 32dcacc16..b9fa51b03 100644
--- a/man/layer_tfsm.Rd
+++ b/man/layer_tfsm.Rd
@@ -59,8 +59,8 @@ model |> export_savedmodel("path/to/artifact")
 ## Output Type:
 ##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None)
 ## Captures:
-##   130786084334736: TensorSpec(shape=(), dtype=tf.resource, name=None)
-##   130786084333584: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   131686858665488: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   131686858664336: TensorSpec(shape=(), dtype=tf.resource, name=None)
 
 }\if{html}{\out{</div>}}
 
diff --git a/man/layer_unit_normalization.Rd b/man/layer_unit_normalization.Rd
index 7aa553eb3..10bb2f56e 100644
--- a/man/layer_unit_normalization.Rd
+++ b/man/layer_unit_normalization.Rd
@@ -32,9 +32,13 @@ Normalize a batch of inputs so that each input in the batch has a L2 norm
 equal to 1 (across the axes specified in \code{axis}).
 }
 \section{Examples}{
-\if{html}{\out{<div class="sourceCode {r}">}}\preformatted{data <- op_reshape(1:6, newshape = c(2, 3))
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{data <- 1:6 |> op_array("float32") |> op_reshape(c(2, 3))
 normalized_data <- layer_unit_normalization(data)
 op_sum(normalized_data[1, ]^2)
+}\if{html}{\out{</div>}}
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## tf.Tensor(0.9999999, shape=(), dtype=float32)
+
 }\if{html}{\out{</div>}}
 }