class_docstr: Make a convolution kernel from a symbolic function of t
  
  A convolution kernel is a function with extra attributes to allow it to
  function as a kernel for numerical convolution (see
  :func:`convolve_functions`).
  
  Parameters
  ----------
  expr : sympy expression
      An expression that is a function of t only.
  support : 2 sequence
      Sequence is ``(low, high)`` where expression is defined between ``low``
      and ``high``, and can be assumed to be `fill` otherwise
  delta : float
      smallest change in domain of `expr` to use for numerical evaluation of
      `expr`
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code: def __init__(self, expr, support, delta, fill=0):
      self.expr = expr
      self.support = support
      self.delta = delta
      self.fill = fill
      self._vals = _eval_for(expr, self.support, self.delta)
  
imports: import itertools
  import numpy
  import sympy
  from scipy.interpolate import interp1d
  from sympy import DiracDelta, Symbol
  from sympy.utilities.lambdify import implemented_function, lambdify
  from nipy.algorithms.statistics.formula.formulae import Formula, Term
label_desc: Use image node_id 1 to create a new TimeConvolver object  with example: obj = TimeConvolver(expr, support, delta, fill)