\n  );\n}\n","import isIE from './isIE';\n\nfunction getSize(axis, body, html, computedStyle) {\n  return Math.max(\n    body[`offset${axis}`],\n    body[`scroll${axis}`],\n    html[`client${axis}`],\n    html[`offset${axis}`],\n    html[`scroll${axis}`],\n    isIE(10)\n      ? (parseInt(html[`offset${axis}`]) + \n      parseInt(computedStyle[`margin${axis === 'Height' ? 'Top' : 'Left'}`]) + \n      parseInt(computedStyle[`margin${axis === 'Height' ? 'Bottom' : 'Right'}`]))\n    : 0 \n  );\n}\n\nexport...

  // Returns number of elements across all dimensions.
  virtual absl::Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.

`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`:
`reduction_indices` becomes `axis` * `tf.reduce_max`: `reduction_indices`
becomes `axis` * `tf.reduce_mean`: `reduction_indices` becomes `axis` *
`tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`:
`reduction_indices` becomes `axis` * `tf.reduce_sum`: `reduction_indices`

      int adjust = size() - 1;
      for (int i = 0; i < axes.size(); i++) {
        adjust *= 31;
        adjust = ~~adjust;
        // in GWT, we have to deal with integer overflow carefully
      }
      int hash = 1;
      for (Set<E> axis : axes) {
        hash = 31 * hash + (size() / axis.size() * axis.hashCode());

        hash = ~~hash;
      }
      hash += adjust;

            "shape ",
            first_shape.DebugString(), " and another had shape ",
            component_shape.DebugString()));
      } else {
        // Generalize differing axis lengths to "variable"/"unknown".
        for (int axis_index = 0; axis_index < combined_shape.dims();
             ++axis_index) {
          int64_t axis_length = combined_shape.dim_size(axis_index);

        // Non-parallel tensors from _EagerConst/tf.constant are implicitly
        // broadcast, i.e. set as the input to each parallel operation. This
        // allows code like "tf.constant(1.)" or "tf.reduce_sum(..., axis=1)"
        // (where the value starts on the host), without allowing other implicit
        // copies/broadcasts. Other implicit copies may be supported eventually,

  // Make a placeholder operation.
  TF_Operation* feed = Placeholder(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Make a constant operation with the scalar "0", for axis.
  TF_Operation* one = ScalarConst(0, graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Create a session for this graph.
  CSession csession(graph, s, use_XLA);

# ADR-0006 - Use of Provider APIs in Gradle

## Date

2024-06-21

## Context

    the [Guava Beta Checker] to ensure that you do not use any `@Beta` APIs!**

2.  APIs without `@Beta` will remain binary-compatible for the indefinite
    future. (Previously, we sometimes removed such APIs after a deprecation
    period. The last release to remove non-`@Beta` APIs was Guava 21.0.) Even
    `@Deprecated` APIs will remain (again, unless they are `@Beta`). We have no
    plans to start removing things again, but officially, we're leaving our

## IMPORTANT WARNINGS

1. APIs marked with the `@Beta` annotation at the class or method level
are subject to change. They can be modified in any way, or even
removed, at any time. If your code is a library itself (i.e. it is
used on the CLASSPATH of users outside your own control), you should
not use beta APIs, unless you [repackage] them. **If your