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TensorFlow 2 version View source on GitHub

A context manager for use when defining a Python op.

This context manager validates that the given values are from the same graph, makes that graph the default graph, and pushes a name scope in that graph (see tf.Graph.name_scope for more details on that).

For example, to define a new Python op called my_op:

def my_op(a, b, c, name=None):
  with tf.name_scope(name, "MyOp", [a, b, c]) as scope:
    a = tf.convert_to_tensor(a, name="a")
    b = tf.convert_to_tensor(b, name="b")
    c = tf.convert_to_tensor(c, name="c")
    # Define some computation that uses `a`, `b`, and `c`.
    return foo_op(..., name=scope)

name The name argument that is passed to the op function.
default_name The default name to use if the name argument is None.
values The list of Tensor arguments that are passed to the op function.

TypeError if default_name is passed in but not a string.




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Start the scope block.

The scope name.

ValueError if neither name nor default_name is provided but values are.


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