// Copy the internal data representation of `from` to `to`. `new_dims` and
// `num_new_dims` specify the new shape of the `to` tensor, `type` specifies its
// data type. On success, *status is set to TF_OK and the two tensors share the
// same data buffer.
//
// This call requires that the `from` tensor and the given type and shape (dims
// and num_dims) are "compatible" (i.e. they occupy the same number of bytes).

// Copyright 2024 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package p

// This type and the following one will share the same GC shape and size.
type Pointery struct {
	p *Pointery
	x [1024]int
}

type Pointery2 struct {
	p *Pointery2
	x [1024]int
}

// This type and the following one will have the same size.
type Vanilla struct {

  // name that has not been used so far in a scope will get no suffix. Later
  // uses of the same name will get suffixes _1, _2, _3, etc. Multiple scopes
  // can share the same NameMap. For instance, a new scope created using
  // WithControlDependencies() would share the same NameMap with the parent.
  typedef std::unordered_map<string, int> NameMap;

  Impl(const std::shared_ptr<Graph>& graph,
       const std::shared_ptr<Status>& status,

# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key2"]


def Test():

  x = tf.constant([[1.0], [1.0], [1.0]])
  y = tf.get_variable(
      name='y',
      shape=(1, 3),
      initializer=tf.random_normal_initializer(),
      trainable=True)
  r = tf.matmul(x, y)

  tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
  tensor_info_r = tf.saved_model.utils.build_tensor_info(r)

		// if either host or share name is different, return s
		{`\\host\share\foo`, ``, `\\host\share\foo`},
		{`\\host\share\foo`, `\foo`, `\\host\share\foo`},
		{`\\host\share\foo`, `\\host\other\`, `\\host\share\foo`},
		{`\\host\share\foo`, `\\other\share\`, `\\host\share\foo`},
		{`\\host\share\foo`, `\\host\`, `\\host\share\foo`},
		{`\\host\share\foo`, `\share\`, `\\host\share\foo`},

		// only volume names are case-insensitive

                       absl::Span<AbstractTensorHandle*> outputs) {
  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 0) {
    return errors::InvalidArgument(
        "Tensor expected to have scalar shape found rank: ", shape.dims());
  }
  return absl::OkStatus();
}

TEST_P(UnifiedAPI, TestTensorShapeScalar) {
  if (UseFunction() && UseMlir()) {

    value {
      type: DT_INT32
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
}
node {
  name: "x"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_VARIANT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
}
node {

      type: DT_RESOURCE
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
}
versions {
  producer: 216
}

# CHECK-NO-SHAPE: func @main(%arg0: tensor<i32>, %arg1: tensor<!tf_type.resource<tensor<i32>>>) -> (tensor<i32>, tensor<!tf_type.resource<tensor<i32>>>)

//   element. For example, if you have a single image of shape `[height, width,
//   channels]`, you can make it a batch of 1 image with `expand_dims(image,
//   0)`, which will make the shape `[1, height, width, channels]`.
//
//   Other examples:
//
//   ```
//   # 't' is a tensor of shape [2]
//   shape(expand_dims(t, 0)) ==> [1, 2]
//   shape(expand_dims(t, 1)) ==> [2, 1]
//   shape(expand_dims(t, -1)) ==> [2, 1]
//

    TF_RETURN_IF_ERROR(session->Create(graph));
    RunOptions run_options;
    Tensor input_a = CreateInputTensor(shape, 0);
    Tensor input_b = CreateInputTensor(shape, shape.num_elements());
    Tensor input_c = CreateInputTensor(shape, 2 * shape.num_elements());
    TF_RETURN_IF_ERROR(session->Run(
        run_options,
        {std::make_pair("a", input_a), std::make_pair("b", input_b),