def generate_rest_request_from_dictionary(self, row_dict):
    triton_request_inputs = []
    for (key, value) in row_dict.items():
        t = clients.utils.get_type(value, self._default_float_type, self._default_int_type)
        if (t == np.object_):
            value = clients.utils.map_multi_dimensional_list(value, (lambda s: s.encode('utf-8')))
        numpy_value = np.array(value, dtype=t)
        triton_request_input = triton_httpclient.InferInput(key, list(numpy_value.shape), triton_utils.np_to_triton_dtype(t))
        triton_request_input.set_data_from_numpy(numpy_value, binary_data=True)
        triton_request_inputs.append(triton_request_input)
    (request, json_size) = triton_httpclient._get_inference_request(inputs=triton_request_inputs, request_id='', outputs=None, sequence_id=0, sequence_start=0, sequence_end=0, priority=0, timeout=None)
    headers = {}
    if json_size:
        headers['Inference-Header-Content-Length'] = str(json_size)
    return (request, headers)
def generate_rest_request_from_dictionary(self, row_dict):
    triton_request_inputs = []
    for (key, value) in row_dict.items():
        t = clients.utils.get_type(value, self._default_float_type, self._default_int_type)
        if (t == np.object_):
            value = clients.utils.map_multi_dimensional_list(value, (lambda s: s.encode('utf-8')))
        numpy_value = np.array(value, dtype=t)
        triton_request_input = triton_httpclient.InferInput(key, list(numpy_value.shape), triton_utils.np_to_triton_dtype(t))
        triton_request_input.set_data_from_numpy(numpy_value, binary_data=True)
        triton_request_inputs.append(triton_request_input)
    (request, json_size) = triton_httpclient._get_inference_request(inputs=triton_request_inputs, request_id=, outputs=None, sequence_id=0, sequence_start=0, sequence_end=0, priority=0, timeout=None)
    headers = {}
    if json_size:
        headers['Inference-Header-Content-Length'] = str(json_size)
    return (request, headers)