//  "\08\03\12\12\10\0b\1a\02\01\04\2a\06\0a\02\01\00\20\01\32\02\00\00\1a\02\01\04\22\04\00\01\02\03"
//
// output 1
//  type: MAXIMAL
//  tile_assignment_dimensions: 1
//  tile_assignment_devices: 1
// Serialized string:
//  "\08\01\1A\01\01\22\01\01"
//
// -----

// Tests tile sharding of outputs with number of splits that exeed number
// of logical devices is not allowed.

// -----

func.func @testSplitOpWithMismatchTensorTypeSplitDimOut1(%arg0 : tensor<16xf32>) -> (tensor<8xf32>, tensor<4xf32>) {
  %split_dim = arith.constant dense<0> : tensor<i32>
  // expected-error @+1 {{'tfl.split' op output #1 should be 'tensor<8xf32>'}}
  %0, %1 = "tfl.split"(%split_dim, %arg0) {num_splits = 2 : i32} : (tensor<i32>, tensor<16xf32>) -> (tensor<8xf32>, tensor<4xf32>)
  func.return %0, %1 : tensor<8xf32>, tensor<4xf32>
}

    its predecessor:

    ```mlir
    %outputs1:2, %control1 = tf_executor.island wraps "tf.IteratorGetNext"(%arg)
      {_class = ["loc:@dataset_iterator_1"]}
    %outputs2:2, %control2 = tf_executor.island wraps "tf.Split"(%outputs0, %outputs1#1)
      {_class = ["loc:@dataset_iterator_1", num_split = 2 : i32}
    ```
  }];
}

// `tfl.batch_matmul` when it accepts uniform quantized tensors.
//
// StableHLO Quantizer output:
//   * input: per-tensor qi8
//   * filter: per-channel qi8 for non-batching op, per-tensor for batching op.
//   * output: per-tensor qi32
// JAX Quantizer output:
//   * input: per-tensor qi8
//   * filter: per-channel qi8
//   * output: per-tensor qi8
//
// Conditions for the `tfl.batch_matmul` conversion:

          inputs={'x1': in_placeholder_1}, outputs={'output1': output_tensor_1}
      )

      in_placeholder_2, output_tensor_2 = self._create_simple_tf1_conv_model()
      sig_def_2 = signature_def_utils_impl.predict_signature_def(
          inputs={'x2': in_placeholder_2}, outputs={'output2': output_tensor_2}
      )