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| # https://github.com/google-research/tensorflow-coder/blob/master/tf_coder/tf_coder_main.py | |
| import streamlit as st | |
| from tf_coder.value_search import colab_interface | |
| from tf_coder.value_search import value_search_settings | |
| import io | |
| from contextlib import redirect_stdout | |
| inputs = st.text_area('The input tensor(s) specified as key-value pairs', placeholder=''' | |
| {'rows': [10, 20, 30], | |
| 'cols': [1,2,3,4],} | |
| ''') | |
| # The single desired output tensor. | |
| settings = value_search_settings.from_dict({ | |
| 'timeout': 300, | |
| 'only_minimal_solutions': False, | |
| 'max_solutions': 1, | |
| 'require_all_inputs_used': True, | |
| 'require_one_input_used': False, | |
| }) | |
| with io.StringIO() as buf, redirect_stdout(buf): | |
| output = [[11, 12, 13, 14], | |
| [21, 22, 23, 24], | |
| [31, 32, 33, 34]] | |
| # A list of relevant scalar constants (if any). | |
| constants = [] | |
| # An English description of the tensor manipulation. | |
| description = 'add two vectors with broadcasting to get a matrix' | |
| results = colab_interface.run_value_search_from_colab(eval(inputs), output, constants, description, settings) | |
| stdout = buf.getvalue() | |
| st.code(stdout, language='bash') |