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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Random weights and biases
train() allows us to shrink the output of neuron C, which has 10 inputs/fan-in-weights
no need to show that weights are asymmetrical, this is the usual case
Step1:  Random biases, No randomness in weights
we can break symmetry in weights
Step2:  fan-ins are almost identical across neurons, elements within one neuron's fan-in are different
Step3:  after 25 epochs
Step4:  No randomness in bias/weights
we can't break symmetry
Step5:  fan-ins are identical across neurons, elements within one neuron's fan-in are identical
Step6:  after 25 epochs
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Python Code:
initialize()
print(train())
print(train())
Explanation: Random weights and biases
train() allows us to shrink the output of neuron C, which has 10 inputs/fan-in-weights
no need to show that weights are asymmetrical, this is the usual case
End of explanation
initialize()
for layer in [a,b,c]:
    layer.weight.data = torch.ones_like(layer.weight)
print(c.weight)
train(), print(c.weight)
train(), print(c.weight)
train(), print(c.weight)
Explanation: Random biases, No randomness in weights
we can break symmetry in weights
End of explanation
b.weight #there's a small amount of symmetry breaking, but the fan-ins are pretty similar
Explanation: fan-ins are almost identical across neurons, elements within one neuron's fan-in are different
End of explanation
for _ in range(25):
    train()
b.weight #now the fan-ins are pretty different
Explanation: after 25 epochs: fan-ins are different across neurons, elements within one neuron's fan-in are different
End of explanation
initialize()
for layer in [a,b,c]:
    layer.weight.data = torch.ones_like(layer.weight)
    layer.bias.data = torch.ones_like(layer.bias)
print(c.weight, c.bias)
train(), print(c.weight, c.bias)
train(), print(c.weight, c.bias)
train(), print(c.weight, c.bias)
Explanation: No randomness in bias/weights
we can't break symmetry
End of explanation
b.weight
Explanation: fan-ins are identical across neurons, elements within one neuron's fan-in are identical
End of explanation
for _ in range(25):
    train()
b.weight
Explanation: after 25 epochs: fan-ins are identical across neurons, elements within one neuron's fan-in are identical
End of explanation
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