def ignite_dataset_eager_fallback(cache_name, host, port, local, part, page_size, schema, permutation, name=None, ctx=None):
    'This is the slowpath function for Eager mode.\n  This is for function ignite_dataset\n  '
    _ctx = (ctx if ctx else _context.context())
    cache_name = _ops.convert_to_tensor(cache_name, _dtypes.string)
    host = _ops.convert_to_tensor(host, _dtypes.string)
    port = _ops.convert_to_tensor(port, _dtypes.int32)
    local = _ops.convert_to_tensor(local, _dtypes.bool)
    part = _ops.convert_to_tensor(part, _dtypes.int32)
    page_size = _ops.convert_to_tensor(page_size, _dtypes.int32)
    schema = _ops.convert_to_tensor(schema, _dtypes.int32)
    permutation = _ops.convert_to_tensor(permutation, _dtypes.int32)
    _inputs_flat = [cache_name, host, port, local, part, page_size, schema, permutation]
    _attrs = None
    _result = _execute.execute(b'IgniteDataset', 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient('IgniteDataset', _inputs_flat, _attrs, _result, name)
    (_result,) = _result
    return _result
This is the slowpath function for Eager mode.
This is for function ignite_dataset
def ignite_dataset_eager_fallback(cache_name, host, port, local, part, page_size, schema, permutation, name=None, ctx=None):
    'This is the slowpath function for Eager mode.\n  This is for function ignite_dataset\n  '
    _ctx = (ctx if ctx else _context.context())
    cache_name = _ops.convert_to_tensor(cache_name, _dtypes.string)
    host = _ops.convert_to_tensor(host, _dtypes.string)
    port = _ops.convert_to_tensor(port, _dtypes.int32)
    local = _ops.convert_to_tensor(local, _dtypes.bool)
    part = _ops.convert_to_tensor(part, _dtypes.int32)
    page_size = _ops.convert_to_tensor(page_size, _dtypes.int32)
    schema = _ops.convert_to_tensor(schema, _dtypes.int32)
    permutation = _ops.convert_to_tensor(permutation, _dtypes.int32)
    _inputs_flat = [cache_name, host, port, local, part, page_size, schema, permutation]
    _attrs = None
    _result = _execute.execute(b'IgniteDataset', 1, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient('IgniteDataset', _inputs_flat, _attrs, _result, name)
    (_result,) = _result
    return _result