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

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

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

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

        "decompose_reduce_dataset.cc",
        "decompose_resource_ops_pass.cc",
        "device_attribute_to_launch.cc",
        "device_index_selector.cc",
        "drop_while_shape_invariant.cc",
        "einsum.cc",
        "executor_island_coarsening.cc",
        "executor_tpuv1_inline_tpu_island.cc",
        "executor_tpuv1_island_coarsening.cc",
        "executor_tpuv1_outline_tpu_island.cc",

std::unique_ptr<OperationPass<func::FuncOp>> CreateUnrollBatchMatMulPassPass();

// Optional pass which will map TF BatchMatMul to TF Einsum
std::unique_ptr<OperationPass<func::FuncOp>> CreateBatchMatMulToEinsumPass();

// Pass that transform Einsum to other TF Ops for the supported variants.
std::unique_ptr<OperationPass<func::FuncOp>> CreateTransformEinsumPass();

// Optimizes Tensorflow graph.

    MLIR pipeline in the future.
  }];
  let constructor = "TF::CreateGpuOpFusionPass()";
}

def BatchMatMulToEinsumPass : Pass<"tf-batch-matmul-to-tf-einsum", "mlir::func::FuncOp"> {
  let summary = "Replace TF BatchMatMul op by TF Einsum op.";
  let constructor = "TF::CreateBatchMatMulToEinsumPass()";
}

is raised.

@compatibility(numpy)
Similar to [`numpy.einsum`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html).

Comparison with `numpy.einsum`:

 * This Op only supports unary and binary forms of `numpy.einsum`.
 * This Op does not support implicit form. (i.e. equations without `->`).

  broadcast_one_side(rhs, rhs_type, rhs_splitted.getTail(), out_rhs);
}

class ConvertBatchMatMulV2Op : public OpRewritePattern<TF::BatchMatMulV2Op> {
 public:
  // TODO(hinsu): Legalize this op to Einsum op. HLO Einsum op needs to be moved
  // to CHLO and it is missing legalization to MHLO. Once that is done, this
  // pattern's benefit can be changed back to one as well as the fallback
  // lowering pattern for the op can be removed.

    `StatusOr::ConsumeValueOrDie`.

## Major Features and Improvements

*   `tf.lite`:

    *   New operations supported:
        *   tflite SelectV2 now supports 5D.
        *   `tf.einsum` is supported with multiple unknown shapes.
        *   `tf.unsortedsegmentprod` op is supported.
        *   `tf.unsortedsegmentmax` op is supported.
        *   `tf.unsortedsegmentsum` op is supported.