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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def computeMatchProbabilityOmega(k, bMax, theta, nTrials=100):
 """ The Omega match probability estimates the probability of matching when both vectors have exactly b components in common. This function computes this probability for b=1 to bMax. For each value of b this function: 1) Creates nTrials instances of Xw(b) which are vectors with b components where each component is uniform in [-1/k, 1/k]. 2) Creates nTrials instances of Xi(b) which are vectors with b components where each component is uniform in [0, 2/k]. 3) Does every possible dot product of Xw(b) dot Xi(b), i.e. nTrials * nTrials dot products. 4) Counts the fraction of cases where Xw(b) dot Xi(b) >= theta Returns an array with bMax entries, where each entry contains the probability computed in 4). """

  omegaProb = np.zeros(bMax+1)

  for b in range(1, bMax+1):
    xwb = getSparseTensor(b, b, nTrials, fixedRange=1.0/k)
    xib = getSparseTensor(b, b, nTrials, onlyPositive=True, fixedRange=2.0/k)
    r = xwb.matmul(xib.t())
    numMatches = ((r >= theta).sum()).item()
    omegaProb[b] = numMatches / float(nTrials * nTrials)

  print(omegaProb)

  return omegaProb