class_docstr: Term binding to a numexpr expression.
  
  Parameters
  ----------
  expr : string
      A string suitable for passing to numexpr.  All variables in 'expr'
      should be of the form "x_i", where i is the index of the corresponding
      factor input in 'binds'.
  binds : tuple
      A tuple of factors to use as inputs.
  dtype : np.dtype
      The dtype for the expression.
class_docstr_tok: ["Term","binding","to","a","numexpr","expression",".","Parameters","--","--","--","--","--","expr",":","string","A","string","suitable","for","passing","to","numexpr",".","All","variables","in","'expr'","should","be","of","the","form","``","x_i","''",",","where","i","is","the","index","of","the","corresponding","factor","input","in","'binds","'",".","binds",":","tuple","A","tuple","of","factors","to","use","as","inputs",".","dtype",":","np.dtype","The","dtype","for","the","expression","."]
docstr: Compute our stored expression string with numexpr.
docstr_tok: ["Compute","our","stored","expression","string","with","numexpr","."]
code: def _compute(self, arrays, dates, assets, mask):
      """
      Compute our stored expression string with numexpr.
      """
      out = full(mask.shape, self.missing_value, dtype=self.dtype)
      # This writes directly into our output buffer.
      numexpr.evaluate(
          self._expr,
          local_dict={
              "x_%d" % idx: array for idx, array in enumerate(arrays)
          },
          global_dict={"inf": inf},
          out=out,
      )
      return out
  
imports: from itertools import chain
  import re
  from numbers import Number
  import numexpr
  from numexpr.necompiler import getExprNames
  from numpy import full, inf
  from catalyst.pipeline.term import Term, ComputableTerm
label_desc: Use image node_id 5 for calling the NumericalExpression obj's underlying member method code with example usage: obj._compute(arrays, dates, assets, mask) and returns: out