%0 = "tf.Gather"(%arg0, %arg1) : (tensor<2x3x6xf32>, tensor<4x5xi32>) -> tensor<4x5x3x6xf32>
  func.return %0 : tensor<4x5x3x6xf32>

// CHECK-LABEL:gatherHigherRankIndices
// CHECK:  "tfl.gather"(%arg0, %arg1) <{axis = 0 : i32, batch_dims = 0 : i32}> : (tensor<2x3x6xf32>, tensor<4x5xi32>) -> tensor<4x5x3x6xf32>
}

func.func @gatherNdVectorIndices(%arg0 : tensor<3x2x2xf32>, %arg1 : tensor<2xi32>) -> tensor<2xf32> {

	// The child process must write to a log file, not
	// the stderr file it inherited from the parent, as
	// the child may outlive the parent but should not prolong
	// the life of any pipes created (by the grandparent)
	// to gather the output of the parent.
	//
	// By default, we discard the child process's stderr,
	// but in line with the uploader, log to a file in debug
	// only if that directory was created by the user.

  //   f = scale * (q - zero_point)
  // For other quantization types, the QuantizationDetails below is used.
  min:[float];  // For importing back into tensorflow.
  max:[float];  // For importing back into tensorflow.
  scale:[float];  // For dequantizing the tensor's values.
  zero_point:[long];

  // If this is not none, the other quantization parameters (i.e. min, max,

    %5 = tfl.add %arg2, %cst_5 {fused_activation_function = "NONE"} : tensor<i32>
    %6 = tfl.add %arg1, %cst_5 {fused_activation_function = "NONE"} : tensor<i32>
    %7 = "tfl.gather"(%0, %arg2) {axis = 0 : i32} : (tensor<4x4x3xf32>, tensor<i32>) -> tensor<4x3xf32>
    %8 = "tfl.concatenation"(%7, %arg5) {axis = 1 : i32, fused_activation_function = "NONE"} : (tensor<4x3xf32>, tensor<4x2xf32>) -> tensor<4x5xf32>

See the <<publishing_ivy.adoc#publishing_ivy,Ivy>> and <<publishing_maven.adoc#publishing_maven,Maven>> chapters on publishing artifacts.

If you are interested in publishing your plugin to be used by the wider Gradle community, you can publish it to http://plugins.gradle.org[Gradle Plugin Portal].

//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU Affero General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful
// but WITHOUT ANY WARRANTY; without even the implied warranty of

			return 0, flagM
		}
		return 0, 0

	case OpCapture, OpStar, OpPlus, OpQuest, OpRepeat:
		return calcFlags(re.Sub[0], flags)

	case OpConcat, OpAlternate:
		// Gather the must and cant for each subexpression.
		// When we find a conflicting subexpression, insert the necessary
		// flags around the previously identified span and start over.
		var must, cant, allCant printFlags
		start := 0

//CHECK-NEXT:    "vhlo.return_v1"(%2, %1) : (tensor<i64>, tensor<i64>) -> ()
//CHECK-NEXT:  }) : (tensor<i64>, tensor<i64>) -> (tensor<i64>, tensor<i64>)
//CHECK-NEXT: return %0#0 : tensor<i64>
//CHECK-NEXT:}

func.func @gather(%operand: tensor<3x4x2xi32>, %start_indices: tensor<2x3x2xi64>) -> tensor<2x3x2x2xi32>{
  %result = "vhlo.gather_v1"(%operand, %start_indices) <{
    offset_dims = #vhlo.tensor_v1<dense<[2, 3]> : tensor<2xi64>>,

//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU Affero General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful
// but WITHOUT ANY WARRANTY; without even the implied warranty of

            !backward_pass_ops.contains(user)) {
          core_tpu_ops.insert(user);
        }
      }
    }
  }

  // Gather all TPU ops marked for compilation in this while loop body that also
  // are not in one of the two other sets.
  for (Operation& op : loop_body_func.getOps()) {
    // Find all TPU ops that don't belong to the forward or backward pass.