message: Fixed some typos in python.rst
  Fixed typos in 6 places.
diff: @@ -80,7 +80,7 @@ intermediate results. This is how it looks like.
  [Y]) # outputs
  # onnx graph
  - # there is no metata in this case.
  + # there is no metadata in this case.
  onnx_model = make_model(graph)
  @@ -167,7 +167,7 @@ Serialization
  =============
  ONNX is built on the top of protobuf. It adds the necessary definitions
  -to describes a machine learned and most of the time, ONNX is used
  +to describe a machine learning model and most of the time, ONNX is used
  to serialize or deserialize a model. First section addresses this need.
  Second section introduces the serialization and deserialization of
  data such as tensors, sparse tensors...
  @@ -230,7 +230,7 @@ overcome that limit.
  Data Serialization
  ++++++++++++++++++
  -The serialization of tensor usually happens the following:
  +The serialization of tensor usually happens like the following:
  .. exec_code::
  @@ -250,7 +250,7 @@ The serialization of tensor usually happens the following:
  with open("saved_tensor.pb", "wb") as f:
  f.write(serialized_tensor)
  -And the deserialization.
  +And the deserialization like:
  .. exec_code::
  @@ -269,7 +269,7 @@ And the deserialization.
  numpy_tensor = to_array(onnx_tensor)
  print(numpy_tensor)
  -The same schema can be used for :ref:`l-tensorproto` but not only:
  +The same schema can be used for but not limited to :ref:`l-tensorproto`:
  .. exec_code::
  @@ -1003,7 +1003,7 @@ Parsing
  =======
  Module onnx provides a faster way to define a graph
  -a lot easier to read. That's easy to use when the graph is built
  +and is lot easier to read. That's easy to use when the graph is built
  in a single function, less easy when the graph is built from many
  different functions converting each piece of a machine learning
  pipeline.
  