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How does one store arrays in TensorFlow?

I am trying to store a custom array in TensorFlow that would look something like this:
Tensor = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0]

I am doing this by using a V1 Tensor object to access it. The problem is that when I want to use it again later in code, it returns a tensor that is different than the one defined. Here is a snippet of the code:
# This returns a different V1 Tensors than the ones defined in the graph.
V1_Tensors = tf.get_collection(“V1_Tensors”)
print(V1_Tensors[0])

I also tried using V1Tensors.append to append it but this didn’t work either. I tried also doing V1_Tensors = tf.Variable(0.0, name=”V1_Tensors”) and then using V1_Tensors.initialized_value = V1_Tensors which also didn’t work.
I also read the TensorFlow documentation but there it states that Variable objects are used to store variable arrays and I am not trying to store an array.
Is there any way to do this?
Thanks in advance!

A:

In the function graph.as_graph_def() that generates the GraphDef file, the names that you assign to tensors are unique and they can’t be changed (default name is ).
If you want to change the name of a tensor you
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