local got
	got=$(mc cat "$2" | sha256sum)

	if [ "${expected}" != "${got}" ]; then
		echo "mismatch - expected ${expected}, got ${got}"
		exit 1
	fi
	echo "matches - ${expected}, got ${got}"
}

add_alias() {
	for i in $(seq 1 4); do
		echo "... attempting to add alias $i"
		until (mc alias set minio http://127.0.0.1:9000 minioadmin minioadmin); do
			echo "...waiting... for 5secs" && sleep 5
		done
	done

                }

                client.admin().indices().prepareCreate(indexName).setSettings(settingsSource, XContentType.JSON).setMapping(mappingSource)
                        .addAlias(new Alias(getSearchAlias(index))).addAlias(new Alias(getUpdateAlias(index))).execute()
                        .actionGet(suggestSettings.getIndicesTimeout());

  %q_bias = "quantfork.qcast"(%bias) : (tensor<2xf32>) -> tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>
  %dq_bias = "quantfork.dcast"(%q_bias) : (tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>) -> tensor<2xf32>

      }
    } else if (function_name.contains("matmul")) {
      spec->coeff_op_quant_dim[1] = -1;
      if (function_name.contains("with_bias") ||
          function_name.contains("and_bias")) {
        spec->biases_params[2] = {{0, 1},
                                  quant::GetUniformQuantizedTypeForBias};
      }
    } else if (function_name.contains("einsum")) {
      spec->coeff_op_quant_dim[1] = -1;

  %q_bias = "quantfork.qcast"(%bias) : (tensor<2xf32>) -> tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>
  %dq_bias = "quantfork.dcast"(%q_bias) : (tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>) -> tensor<2xf32>

            if (aliasConfigFile.exists()) {
                final String source = FileUtil.readUTF8(aliasConfigFile);
                final AcknowledgedResponse response = indicesClient.prepareAliases().addAlias(indexName, aliasName, source).execute()
                        .actionGet(fessConfig.getIndexIndicesTimeout());
                if (response.isAcknowledged()) {

    bool needs_adjustment = false;
    for (int i = 0; i < bias_quantized_type.getScales().size(); ++i) {
      const float abs_bias = std::abs(bias_values.getValues<float>()[i]);
      if (abs_bias / new_bias_scales[i] > kBiasMax) {
        new_bias_scales[i] = static_cast<double>(abs_bias) / kBiasMax;
        new_filter_scales[i] = new_bias_scales[i] / input_scale;
        needs_adjustment = true;
      }
    }

            int last = dotted.size() - 1;
            for (int i = 0; i < last; i++) {
                current = current.child(dotted.get(i));
            }
            current.addAlias(alias);
        }
        return root;
    }

    private static class ClassNode {
        private final ClassNode parent;
        private final AccessorKind kind;
        private final String name;

      if (addend_shape.getShape().size() != 1) return failure();
    }

    // Calculate new bias.  Generate a new FC; it will be constant folded.
    auto old_bias = fc_op.getBias();
    if (!old_bias || mlir::isa<NoneType>(old_bias.getType())) {
      // TODO(b/180752069): Figure out new bias' type when old bias is empty.
      return failure();
    }

  ::flatbuffers::FlatBufferBuilder &fbb_;
  ::flatbuffers::uoffset_t start_;
  void add_radius(int32_t radius) {
    fbb_.AddElement<int32_t>(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0);
  }
  void add_bias(float bias) {
    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f);
  }
  void add_alpha(float alpha) {
    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f);