AttributeError: 'numpy.ndarray' object has no attribute 'A'

I am trying to perform tfidf on a matrix. I would like to use gensim, but models.TfidfModel() only works on a corpus and therefore returns a list of lists of varying lengths (I want a matrix).

The options are to somehow fill in the missing values of the list of lists, or just convert the corpus to a matrix

numpy_matrix = gensim.matutils.corpus2dense(corpus, num_terms=number_of_corpus_features)

Choosing the latter, I then try to convert this count matrix to a tf-idf weighted matrix:

def TFIDF(m):
    #import numpy
    WordsPerDoc = numpy.sum(m, axis=0)
    DocsPerWord = numpy.sum(numpy.asarray(m > 0, 'i'), axis=1)
    rows, cols = m.shape
    for i in range(rows):
        for j in range(cols):
            amatrix[i,j] = (amatrix[i,j] / WordsPerDoc[j]) * log(float(cols) /     DocsPerWord[i])

But, I get the error AttributeError: 'numpy.ndarray' object has no attribute 'A'

I copied the function above from another script. It was:

def TFIDF(self):
    WordsPerDoc = sum(self.A, axis=0)        
    DocsPerWord = sum(asarray(self.A > 0, 'i'), axis=1)
    rows, cols = self.A.shape
    for i in range(rows):
       for j in range(cols):
          self.A[i,j] = (self.A[i,j] / WordsPerDoc[j]) * log(float(cols) / DocsPerWord[i])

Which I believe is where it's getting the A from. However, I re-imported the function.

Why is this happening?