class_docstr: Fundamental way to create a sparse node.
  
  Parameters
  ----------
  dtype : numpy dtype string such as 'int64' or 'float64' (among others)
      Type of numbers in the matrix.
  format: str
      The sparse storage strategy.
  
  Returns
  -------
  An empty SparseVariable instance.
  
  Notes
  -----
  As far as I can tell, L{scipy.sparse} objects must be matrices, i.e.
  have dimension 2.
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code: def values_eq_approx(self, a, b, eps=1e-6):
      # WARNING: equality comparison of sparse matrices is not fast or easy
      # we definitely do not want to be doing this un-necessarily during
      # a FAST_RUN computation..
      if not scipy.sparse.issparse(a) or not scipy.sparse.issparse(b):
          return False
      diff = abs(a - b)
      if diff.nnz == 0:
          return True
      # Built-in max from python is not implemented for sparse matrix as a
      # reduction. It returns a sparse matrix which cannot be compared to a
      # scalar. When comparing sparse to scalar, no exceptions is raised and
      # the returning value is not consistent. That is why it is apply to a
      # numpy.ndarray.
      return max(diff.data) < eps
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  import theano
  from theano import gof
  from six import string_types
label_desc: Use image node_id 9 for calling the SparseType obj's underlying member method code with example usage: obj.values_eq_approx(a, b, eps) and returns: unknown, False, True