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Solve the following python programming problem given the following code:
# Make sure I can execute every cell multiple times in a row. Sometimes, 
# if one reuses a variable defined in an earlier cell, variables can be overwritten
# which can lead to problems. 

from sklearn.model_selection import train_test_split
# The test+train dataset was created using a Gaussian Process
xdata = torch.tensor(x_test)  # (n, 1)
#print(xdata[0:20]).

ydata = torch.tensor(y_pred.copy()).reshape(-1,1) # (n,)
#print(xdata)

train_size = .01
# Data is shuffled by default
xtrain, xtest, ytrain, ytest = train_test_split(xdata.numpy(), ydata.numpy(), train_size=train_size)

#x_train = torch.tensor(x_train, dtype=torch.float)
xtrain = torch.from_numpy(xtrain).float()
xtest = torch.from_numpy(xtest).float()
ytrain = torch.from_numpy(ytrain).float()
ytest = torch.from_numpy(ytest).float()
#print(type(xtrain))
xtrain = xtrain.reshape(-1,1)
ytrain = ytrain.reshape(-1,1)
#print(xtrain.shape, ytrain.shape);

from torch.autograd import Variable
import torch.nn as nn

class linearRegression(torch.nn.Module):
    def __init__(self, inputSize, hiddenSize, outputSize):
        super(linearRegression, self).__init__()
        self.input_linear1 = torch.nn.Linear(inputSize, hiddenSize)
        self.input_linear2 = torch.nn.Linear(hiddenSize, outputSize)
        self.sigmoid = nn.Tanh()
 
    def forward(self, x):
        x = self.input_linear1(x)
        x = self.sigmoid(x)  # I should be able to drive loss to zero
        x = self.input_linear2(x)
        return x

inputDim = 1
hiddenDim = 10
outputDim = 1
learningRate = .1
epochs = 500
model = linearRegression(inputDim, hiddenDim, outputDim)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learningRate)
#print(model.state_dict())

losses = []
for epoch in range(epochs):
    # Converting inputs and labels to Variable
    if torch.cuda.is_available():
        inputs = Variable(xtrain)
        labels = Variable(ytrain)
    else:
        inputs = Variable(xtrain)
        labels = Variable(ytrain)

    # Clear gradient buffers because...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Construct a Neural network to do regression using Pytorch
First create the data for training