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tensorflow/compiler/mlir/lite/tests/legalize_jax_random.mlir

// CHECK:           return %[[VAL_2]] : tuple<tensor<3x4xf32>>
// CHECK:         }
func.func @tfl_wrapped_jax_random_normal(%arg0: tensor<2xui32>) -> tuple<tensor<3x4xf32>> {
  // This is a fake jax random normal body.
  %0 = stablehlo.constant dense<0.0> : tensor<12xf32>
  %1 = "stablehlo.reshape"(%0) : (tensor<12xf32>) -> tensor<3x4xf32>
  %2 = "stablehlo.tuple"(%1) : (tensor<3x4xf32>) -> tuple<tensor<3x4xf32>>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/tensorflow/tests/add_dump_tensor_op_stablehlo.mlir

    %2 = "tf.XlaCallModule"(%arg0, %0) <{Sout = [#tf_type.shape<1x3>], module = "", version = 9 : i64}> {_entry_function = @composite_dot_general_fn_1, _original_entry_function = "composite_dot_general_fn_1", _stablehlo_module_attrs = {jax.uses_shape_polymorphism = true}, _tfl_quant_trait = "fully_quantizable"} : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    %3 = stablehlo.concatenate %2, %1, dim = 0 : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<2x3xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/stablehlo/transforms/composite_lowering_patterns.td

        ConstBoolAttrTrue,
        (returnType (GetNhwcReturnTypeFromNchw $old_val))),
      (Arith_ConstantOp
        ConstantAttr<RankedI32ElementsAttr<[4]>,"{0, 3, 1, 2}">))>;

// pattern to lower a stablehlo.composite of `jax` and `pytorch` image resize fuctions
// in `nearest` mode and with NHWC inputs to a tflite.resize_nearest_neighbor op.
def LegalizeNhwcResizeNearestNeighbor2dComposite: Pat<
    (MHLO_CompositeOp:$old_val
    (variadic $input),

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github.com/tensorflow/tensorflow

ci/official/requirements_updater/requirements.in

keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.18.0.a

# Test dependencies
grpcio >= 1.24.3, < 2.0
portpicker == 1.6.0
scipy == 1.11.3
requests >= 2.31.0
packaging==23.2
setuptools==68.2.2

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github.com/tensorflow/tensorflow

requirements_lock_3_11.txt

    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via
    #   -r requirements.in
    #   jax
packaging==23.2 \

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/stablehlo/ops/stablehlo_op_quant_spec_test.cc

      return %0 : tensor<1x1x4xf32>
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op =

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize_op_with_region.mlir

    %9 = "quantfork.qcast"(%8) {volatile} : (tensor<2x3x1x3xf32>) -> tensor<2x3x1x3x...

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github.com/tensorflow/tensorflow

requirements_lock_3_12.txt

    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via
    #   -r requirements.in
    #   jax
packaging==23.2 \

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github.com/tensorflow/tensorflow

requirements_lock_3_10.txt

    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via
    #   -r requirements.in
    #   jax
packaging==23.2 \

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github.com/tensorflow/tensorflow

requirements_lock_3_9.txt

    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via
    #   -r requirements.in
    #   jax
packaging==23.2 \

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