Consider the following code:1a=torch.tensor([[0,1,1],[1,0,1]])What is the output of a.size() and a.ndimension()?

# Find the output of the following:tensor_A = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=torch.float32)tensor_B = torch.tensor([[7, 10], [8, 11], [9, 12]], dtype=torch.float32)torch.matmul(tensor_A, tensor_B) )[[58,64],[139,154]][[27,30,33],[61,68,75],[95,106,117]]value errornone of the above

what is the result of the following:123A = torch.tensor([[0, 1, 1], [1, 0, 1]])B = torch.tensor([[1, 1], [1, 1], [-1, 1]])A_times_B = torch.mm(A,B)1 pointtensor([[0, 2], [0, 2]])tensor([[0, 1], [1, 4]])

Machine ChallengeIn the Python file, write a PyTorch program to create a 2-dimensional tensor of size 3x3, filled with predefined random float numbers between 0 and 1. Set the seed for the random number generator in PyTorch to 0 using torch.manual_seed(0).Next, compute and print the sum of all the tensor elements. Finally, convert the tensor to a Python list and print it.Example Output:Sum of all tensor elements: 4.724854469299316[[0.7576315999031067, 0.2793108820915222, 0.40306925773620605], [0.7346844673156738, 0.029281556606292725, 0.7998586297035217], [0.3971373438835144, 0.7543719410896301, 0.5695084929466248]]

Question 2How many dimensions is the input for the following neural network object:45678910111213321        self.linear1=nn.Linear(D_in,H)        self.linear2=nn.Linear(H,D_out)             def forward(self,x):        x=torch.sigmoid(self.linear1(x))          x=torch.sigmoid(self.linear2(x))        return x        model=Net(4,10,1)        super(Net,self).__init__()    def __init__(self,D_in,H,D_out):class Net(nn.Module):

Which of the following attribute is used to check numpy array dimensions? dim ndim mdim None of the above