message: Change sample to example
diff: @@ -73,10 +73,10 @@ class TextVectorization(base_preprocessing_layer.CombinerPreprocessingLayer):
  """Text vectorization layer.
  This layer has basic options for managing text in a Keras model. It
  - transforms a batch of strings (one sample = one string) into either a list of
  - token indices (one sample = 1D tensor of integer token indices) or a dense
  - representation (one sample = 1D tensor of float values representing data about
  - the sample's tokens).
  + transforms a batch of strings (one example = one string) into either a list of
  + token indices (one example = 1D tensor of integer token indices) or a dense
  + representation (one example = 1D tensor of float values representing data about
  + the example's tokens).
  If desired, the user can call this layer's adapt() method on a dataset.
  When this layer is adapted, it will analyze the dataset, determine the
  @@ -86,13 +86,13 @@ class TextVectorization(base_preprocessing_layer.CombinerPreprocessingLayer):
  input than the maximum vocabulary size, the most frequent terms will be used
  to create the vocabulary.
  - The processing of each sample contains the following steps:
  + The processing of each example contains the following steps:
  - 1. standardize each sample (usually lowercasing + punctuation stripping)
  - 2. split each sample into substrings (usually words)
  + 1. standardize each example (usually lowercasing + punctuation stripping)
  + 2. split each example into substrings (usually words)
  3. recombine substrings into tokens (usually ngrams)
  4. index tokens (associate a unique int value with each token)
  - 5. transform each sample using this index, either into a vector of ints or
  + 5. transform each example using this index, either into a vector of ints or
  a dense float vector.
  Some notes on passing Callables to customize splitting and normalization for
  