### `tf.contrib.util.make_tensor_proto(values, dtype=None, shape=None, verify_shape=False)` {#make_tensor_proto}

Create a TensorProto.

##### Args:


*  <b>`values`</b>: Values to put in the TensorProto.
*  <b>`dtype`</b>: Optional tensor_pb2 DataType value.
*  <b>`shape`</b>: List of integers representing the dimensions of tensor.
*  <b>`verify_shape`</b>: Boolean that enables verification of a shape of values.

##### Returns:

  A TensorProto. Depending on the type, it may contain data in the
  "tensor_content" attribute, which is not directly useful to Python programs.
  To access the values you should convert the proto back to a numpy ndarray
  with tensor_util.MakeNdarray(proto).

##### Raises:


*  <b>`TypeError`</b>: if unsupported types are provided.
*  <b>`ValueError`</b>: if arguments have inappropriate values or if verify_shape is
   True and shape of values is not equals to a shape from the argument.

make_tensor_proto accepts "values" of a python scalar, a python list, a
numpy ndarray, or a numpy scalar.

If "values" is a python scalar or a python list, make_tensor_proto
first convert it to numpy ndarray. If dtype is None, the
conversion tries its best to infer the right numpy data
type. Otherwise, the resulting numpy array has a compatible data
type with the given dtype.

In either case above, the numpy ndarray (either the caller provided
or the auto converted) must have the compatible type with dtype.

make_tensor_proto then converts the numpy array to a tensor proto.

If "shape" is None, the resulting tensor proto represents the numpy
array precisely.

Otherwise, "shape" specifies the tensor's shape and the numpy array
can not have more elements than what "shape" specifies.

