Function args_to_matching_eager

tensorflow/python/eager/execute.py:231–272

Convert sequence `l` to eager same-type Tensors.

(l, ctx, default_dtype=None)

def args_to_matching_eager(l, ctx, default_dtype=None):
  """Convert sequence `l` to eager same-type Tensors."""
  EagerTensor = ops.EagerTensor  # pylint: disable=invalid-name
  for x in l:
    if not isinstance(x, EagerTensor):
      break
  else:  # note: intentional for-else
    return l[0]._datatype_enum(), l  # pylint: disable=protected-access
  # TODO(josh11b): Could we do a better job if we also passed in the
  # allowed dtypes when that was known?

  # Is some input already a Tensor with a dtype?
  dtype = None
  for t in l:
    if isinstance(t, EagerTensor):
      dtype = t.dtype
      break

  internal_convert_to_tensor = ops.internal_convert_to_tensor
  if dtype is None:
    # Infer a dtype based on the first value, and use that dtype for the
    # remaining values.
    ret = []
    for t in l:
      ret.append(
          internal_convert_to_tensor(
              t, dtype, preferred_dtype=default_dtype, ctx=ctx))
      if dtype is None:
        dtype = ret[-1].dtype
  else:
    ret = [internal_convert_to_tensor(t, dtype, ctx=ctx) for t in l]

  # TODO(slebedev): consider removing this as it leaks a Keras concept.
  # pylint: disable=protected-access
  keras_symbolic_tensors = [x for x in ret if
                            ops._is_keras_symbolic_tensor(x)]
  if keras_symbolic_tensors:
    raise core._SymbolicException(
        "Using symbolic output of a Keras layer during eager execution "
        "{}".format(keras_symbolic_tensors))
  # pylint: enable=protected-access
  return dtype.as_datatype_enum, ret


def convert_to_mixed_eager_tensors(values, ctx):